importance of critical thinking in scientific inquiry

3. Critical Thinking in Science: How to Foster Scientific Reasoning Skills

Critical thinking in science is important largely because a lot of students have developed expectations about science that can prove to be counter-productive. 

After various experiences — both in school and out — students often perceive science to be primarily about learning “authoritative” content knowledge: this is how the solar system works; that is how diffusion works; this is the right answer and that is not. 

This perception allows little room for critical thinking in science, in spite of the fact that argument, reasoning, and critical thinking lie at the very core of scientific practice.

Argument, reasoning, and critical thinking lie at the very core of scientific practice.

In this article, we outline two of the best approaches to be most effective in fostering scientific reasoning. Both try to put students in a scientist’s frame of mind more than is typical in science education:

• First, we look at  small-group inquiry , where students formulate questions and investigate them in small groups. This approach is geared more toward younger students but has applications at higher levels too.
• We also look  science   labs . Too often, science labs too often involve students simply following recipes or replicating standard results. Here, we offer tips to turn labs into spaces for independent inquiry and scientific reasoning.

I. Critical Thinking in Science and Scientific Inquiry

Even very young students can “think scientifically” under the right instructional support. A series of experiments , for instance, established that preschoolers can make statistically valid inferences about unknown variables. Through observation they are also capable of distinguishing actions that cause certain outcomes from actions that don’t. These innate capacities, however, have to be developed for students to grow up into rigorous scientific critical thinkers. 

Even very young students can “think scientifically” under the right instructional support.

Although there are many techniques to get young children involved in scientific inquiry — encouraging them to ask and answer “why” questions, for instance — teachers can provide structured scientific inquiry experiences that are deeper than students can experience on their own. 

Goals for Teaching Critical Thinking Through Scientific Inquiry

When it comes to teaching critical thinking via science, the learning goals may vary, but students should learn that:

• Failure to agree is okay, as long as you have reasons for why you disagree about something.
• The logic of scientific inquiry is iterative. Scientists always have to consider how they might improve your methods next time. This includes addressing sources of uncertainty.
• Claims to knowledge usually require multiple lines of evidence and a “match” or “fit” between our explanations and the evidence we have.
• Collaboration, argument, and discussion are central features of scientific reasoning.
• Visualization, analysis, and presentation are central features of scientific reasoning.
• Overarching concepts in scientific practice — such as uncertainty, measurement, and meaningful experimental contrasts — manifest themselves somewhat differently in different scientific domains.

How to Teaching Critical Thinking in Science Via Inquiry

Sometimes we think of science education as being either a “direct” approach, where we tell students about a concept, or an “inquiry-based” approach, where students explore a concept themselves.  

But, especially, at the earliest grades, integrating both approaches can inform students of their options (i.e., generate and extend their ideas), while also letting students make decisions about what to do.

Like a lot of projects targeting critical thinking, limited classroom time is a challenge. Although the latest content standards, such as the Next Generation Science Standards , emphasize teaching scientific practices, many standardized tests still emphasize assessing scientific content knowledge.

The concept of uncertainty comes up in every scientific domain.

Creating a lesson that targets the right content is also an important aspect of developing authentic scientific experiences. It’s now more  widely acknowledged  that effective science instruction involves the interaction between domain-specific knowledge and domain-general knowledge, and that linking an inquiry experience to appropriate target content is vital.

For instance, the concept of uncertainty  comes up  in every scientific domain. But the sources of uncertainty coming from any given measurement vary tremendously by discipline. It requires content knowledge to know how to wisely apply the concept of uncertainty.

Tips and Challenges for teaching critical thinking in science

Teachers need to grapple with student misconceptions. Student intuition about how the world works — the way living things grow and behave, the way that objects fall and interact — often conflicts with scientific explanations. As part of the inquiry experience, teachers can help students to articulate these intuitions and revise them through argument and evidence.

Group composition is another challenge. Teachers will want to avoid situations where one member of the group will simply “take charge” of the decision-making, while other member(s) disengage. In some cases, grouping students by current ability level can make the group work more productive. 

Another approach is to establish group norms that help prevent unproductive group interactions. A third tactic is to have each group member learn an essential piece of the puzzle prior to the group work, so that each member is bringing something valuable to the table (which other group members don’t yet know).

It’s critical to ask students about how certain they are in their observations and explanations and what they could do better next time. When disagreements arise about what to do next or how to interpret evidence, the instructor should model good scientific practice by, for instance, getting students to think about what kind of evidence would help resolve the disagreement or whether there’s a compromise that might satisfy both groups.

The subjects of the inquiry experience and the tools at students’ disposal will depend upon the class and the grade level. Older students may be asked to create mathematical models, more sophisticated visualizations, and give fuller presentations of their results.

Lesson Plan Outline

This lesson plan takes a small-group inquiry approach to critical thinking in science. It asks students to collaboratively explore a scientific question, or perhaps a series of related questions, within a scientific domain.

Suppose students are exploring insect behavior. Groups may decide what questions to ask about insect behavior; how to observe, define, and record insect behavior; how to design an experiment that generates evidence related to their research questions; and how to interpret and present their results.

An in-depth inquiry experience usually takes place over the course of several classroom sessions, and includes classroom-wide instruction, small-group work, and potentially some individual work as well.

Students, especially younger students, will typically need some background knowledge that can inform more independent decision-making. So providing classroom-wide instruction and discussion before individual group work is a good idea.

For instance, Kathleen Metz had students observe insect behavior, explore the anatomy of insects, draw habitat maps, and collaboratively formulate (and categorize) research questions before students began to work more independently.

The subjects of a science inquiry experience can vary tremendously: local weather patterns, plant growth, pollution, bridge-building. The point is to engage students in multiple aspects of scientific practice: observing, formulating research questions, making predictions, gathering data, analyzing and interpreting data, refining and iterating the process.

As student groups take responsibility for their own investigation, teachers act as facilitators. They can circulate around the room, providing advice and guidance to individual groups. If classroom-wide misconceptions arise, they can pause group work to address those misconceptions directly and re-orient the class toward a more productive way of thinking.

Throughout the process, teachers can also ask questions like:

• What are your assumptions about what’s going on? How can you check your assumptions?
• Suppose that your results show X, what would you conclude?
• If you had to do the process over again, what would you change? Why?

II. Rethinking Science Labs

Beyond changing how students approach scientific inquiry, we also need to rethink science labs. After all, science lab activities are ubiquitous in science classrooms and they are a great opportunity to teach critical thinking skills.

Often, however, science labs are merely recipes that students follow to verify standard values (such as the force of acceleration due to gravity) or relationships between variables (such as the relationship between force, mass, and acceleration) known to the students beforehand. 

This approach does not usually involve critical thinking: students are not making many decisions during the process, and they do not reflect on what they’ve done except to see whether their experimental data matches the expected values.

With some small tweaks, however, science labs can involve more critical thinking. Science lab activities that give students not only the opportunity to design, analyze, and interpret the experiment, but re -design, re -analyze, and re -interpret the experiment provides ample opportunity for grappling with evidence and evidence-model relationships, particularly if students don’t know what answer they should be expecting beforehand.

Such activities improve scientific reasoning skills, such as: 

• Evaluating quantitative data
• Plausible scientific explanations for observed patterns

And also broader critical thinking skills, like:

• Comparing models to data, and comparing models to each other
• Thinking about what kind of evidence supports one model or another
• Being open to changing your beliefs based on evidence

Traditional science lab experiences bear little resemblance to actual scientific practice. Actual practice  involves  decision-making under uncertainty, trial-and-error, tweaking experimental methods over time, testing instruments, and resolving conflicts among different kinds of evidence. Traditional in-school science labs rarely involve these things.

Traditional science lab experiences bear little resemblance to actual scientific practice.

When teachers use science labs as opportunities to engage students in the kinds of dilemmas that scientists actually face during research, students make more decisions and exhibit more sophisticated reasoning.

In the lesson plan below, students are asked to evaluate two models of drag forces on a falling object. One model assumes that drag increases linearly with the velocity of the falling object. Another model assumes that drag increases quadratically (e.g., with the square of the velocity).  Students use a motion detector and computer software to create a plot of the position of a disposable paper coffee filter as it falls to the ground. Among other variables, students can vary the number of coffee filters they drop at once, the height at which they drop them, how they drop  them, and how they clean their data. This is an approach to scaffolding critical thinking: a way to get students to ask the right kinds of questions and think in the way that scientists tend to think.

Design an experiment to test which model best characterizes the motion of the coffee filters. 

Things to think about in your design:

• What are the relevant variables to control and which ones do you need to explore?
• What are some logistical issues associated with the data collection that may cause unnecessary variability (either random or systematic) or mistakes?
• How can you control or measure these?
• What ways can you graph your data and which ones will help you figure out which model better describes your data?

Discuss your design with other groups and modify as you see fit.

Initial data collection

Conduct a quick trial-run of your experiment so that you can evaluate your methods.

• Do your graphs provide evidence of which model is the best?
• What ways can you improve your methods, data, or graphs to make your case more convincing?
• Do you need to change how you’re collecting data?
• Do you need to take data at different regions?
• Do you just need more data?
• Do you need to reduce your uncertainty?

After this initial evaluation of your data and methods, conduct the desired improvements, changes, or additions and re-evaluate at the end.

In your lab notes, make sure to keep track of your progress and process as you go. As always, your final product is less important than how you get there.

How to Make Science Labs Run Smoothly

Managing student expectations . As with many other lesson plans that incorporate critical thinking, students are not used to having so much freedom. As with the example lesson plan above, it’s important to scaffold student decision-making by pointing out what decisions have to be made, especially as students are transitioning to this approach.

Supporting student reasoning . Another challenge is to provide guidance to student groups without telling them how to do something. Too much “telling” diminishes student decision-making, but not enough support may leave students simply not knowing what to do. 

There are several key strategies teachers can try out here: 

• Point out an issue with their data collection process without specifying exactly how to solve it.
• Ask a lab group how they would improve their approach.
• Ask two groups with conflicting results to compare their results, methods, and analyses.

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Sources and Resources

Lehrer, R., & Schauble, L. (2007). Scientific thinking and scientific literacy . Handbook of child psychology , Vol. 4. Wiley. A review of research on scientific thinking and experiments on teaching scientific thinking in the classroom.

Metz, K. (2004). Children’s understanding of scientific inquiry: Their conceptualizations of uncertainty in investigations of their own design . Cognition and Instruction 22(2). An example of a scientific inquiry experience for elementary school students.

The Next Generation Science Standards . The latest U.S. science content standards.

Concepts of Evidence A collection of important concepts related to evidence that cut across scientific disciplines.

Scienceblind A book about children’s science misconceptions and how to correct them.

Holmes, N. G., Keep, B., & Wieman, C. E. (2020). Developing scientific decision making by structuring and supporting student agency. Physical Review Physics Education Research , 16 (1), 010109. A research study on minimally altering traditional lab approaches to incorporate more critical thinking. The drag example was taken from this piece.

ISLE , led by E. Etkina.  A platform that helps teachers incorporate more critical thinking in physics labs.

Holmes, N. G., Wieman, C. E., & Bonn, D. A. (2015). Teaching critical thinking . Proceedings of the National Academy of Sciences , 112 (36), 11199-11204. An approach to improving critical thinking and reflection in science labs. Walker, J. P., Sampson, V., Grooms, J., Anderson, B., & Zimmerman, C. O. (2012). Argument-driven inquiry in undergraduate chemistry labs: The impact on students’ conceptual understanding, argument skills, and attitudes toward science . Journal of College Science Teaching , 41 (4), 74-81. A large-scale research study on transforming chemistry labs to be more inquiry-based.

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Supporting Early Scientific Thinking Through Curiosity

Curiosity and curiosity-driven questioning are important for developing scientific thinking and more general interest and motivation to pursue scientific questions. Curiosity has been operationalized as preference for uncertainty ( Jirout and Klahr, 2012 ), and engaging in inquiry-an essential part of scientific reasoning-generates high levels of uncertainty ( Metz, 2004 ; van Schijndel et al., 2018 ). This perspective piece begins by discussing mechanisms through which curiosity can support learning and motivation in science, including motivating information-seeking behaviors, gathering information in response to curiosity, and promoting deeper understanding through connection-making related to addressing information gaps. In the second part of the article, a recent theory of how to promote curiosity in schools is discussed in relation to early childhood science reasoning. Finally, potential directions for research on the development of curiosity and curiosity-driven inquiry in young children are discussed. Although quite a bit is known about the development of children’s question asking specifically, and there are convincing arguments for developing scientific curiosity to promote science reasoning skills, there are many important areas for future research to address how to effectively use curiosity to support science learning.

Scientific Thinking and Curiosity

Scientific thinking is a type of knowledge seeking involving intentional information seeking, including asking questions, testing hypotheses, making observations, recognizing patterns, and making inferences ( Kuhn, 2002 ; Morris et al., 2012 ). Much research indicates that children engage in this information-seeking process very early on through questioning behaviors and exploration. In fact, children are quite capable and effective in gathering needed information through their questions, and can reason about the effectiveness of questions, use probabilistic information to guide their questioning, and evaluate who they should question to get information, among other related skills (see Ronfard et al., 2018 for review). Although formal educational contexts typically give students questions to explore or steps to follow to “do science,” young children’s scientific thinking is driven by natural curiosity about the world around them, and the desire to understand it and generate their own questions about the world ( Chouinard et al., 2007 ; Duschl et al., 2007 ; French et al., 2013 ; Jirout and Zimmerman, 2015 ).

What Does Scientific Curiosity Look Like?

Curiosity is defined here as the desire to seek information to address knowledge gaps resulting from uncertainty or ambiguity ( Loewenstein, 1994 ; Jirout and Klahr, 2012 ). Curiosity is often seen as ubiquitous within early childhood. Simply observing children can provide numerous examples of the bidirectional link between curiosity and scientific reasoning, such as when curiosity about a phenomenon leads to experimentation, which, in turn, generates new questions and new curiosities. For example, an infant drops a toy to observe what will happen. When an adult stoops to pick it up, the infant becomes curious about how many times an adult will hand it back before losing interest. Or, a child might observe a butterfly over a period of time, and wonder why it had its wings folded or open at different points, how butterflies fly, why different butterflies are different colors, and so on (see Figure 1 ). Observations lead to theories, which may be immature, incomplete, or even inaccurate, but so are many early scientific theories. Importantly, theories can help identify knowledge gaps, leading to new instances of curiosity and motivating children’s information seeking to acquire new knowledge and, gradually, correct misconceptions. Like adults, children learn from their experiences and observations and use information about the probability of events to revise their theories ( Gopnik, 2012 ).

A child looks intently at a butterfly, becoming curious about the many things she wonders based on her observations.

Although this type of reasoning is especially salient in science, curiosity can manifest in many different types of information seeking in response to uncertainty, and is similar to critical thinking in other domains of knowledge and to active learning and problem solving more generally ( Gopnik, 2012 ; Klahr et al., 2013 ; Saylor and Ganea, 2018 ). The development of scientific thinking begins as the senses develop and begin providing information about the world ( Inhelder and Piaget, 1958 ; Gopnik et al., 1999 ). When they are not actively discouraged, children need no instruction to ask questions and explore, and the information they get often leads to further information seeking. In fact, observational research suggests that children can ask questions at the rate of more than 100 per hour ( Chouinard et al., 2007 )! Although the adults in a child’s life might tire of what seems like relentless questioning ( Turgeon, 2015 ), even young children can modify their beliefs and learn from the information they receive ( Ronfard et al., 2018 ). More generally, children seek to understand their world through active exploration, especially in response to recognizing a gap in their understanding ( Schulz and Bonawitz, 2007 ). The active choice of what to learn, driven by curiosity, can provide motivation and meaning to information and instill a lasting positive approach to learning in formal educational contexts.

How Does Curiosity Develop and Support Scientific Thinking?

There are several mechanisms through which children’s curiosity can support the development and persistence of scientific thinking. Three of these are discussed below, in sequence: that curiosity can (1) motivate information-seeking behavior, which leads to (2) question-asking and other information-seeking behaviors, which can (3) activate related previous knowledge and support deeper learning. Although we discuss these as independent, consecutive steps for the sake of clarity, it is much more likely that curiosity, question asking and information seeking, and cognitive processing of information and learning are all interrelated processes that support each other ( Oudeyer et al., 2016 ). For example, information seeking that is not a result of curiosity can lead to new questions, and as previous knowledge is activated it may influence the ways in which a child seeks information.

Curiosity as a Motivation for Information Seeking

Young children’s learning is driven by exploration to make sense of the world around them (e.g., Piaget, 1926 ). This exploration can result from curiosity ( Loewenstein, 1994 ; Jirout and Klahr, 2012 ) and lead to active engagement in learning ( Saylor and Ganea, 2018 ). In the example given previously, the child sees that some butterflies have open wings and some have closed wings, and may be uncertain about why, leading to more careful observations that provide potential for learning. Several studies demonstrate that the presence of uncertainty or ambiguity leads to higher engagement ( Howard-Jones and Demetriou, 2009 ) and more exploration and information seeking ( Berlyne, 1954 ; Lowry and Johnson, 1981 ; Loewenstein, 1994 ; Litman et al., 2005 ; Jirout and Klahr, 2012 ). For example, when children are shown ambiguous demonstrations for how a novel toy works, they prefer and play longer with that toy than with a new toy that was demonstrated without ambiguity ( Schulz and Bonawitz, 2007 ). Similar to ambiguity, surprising or unexpected observations can create uncertainty and lead to curiosity-driven questions or explanations through adult–child conversations ( Frazier et al., 2009 ; Danovitch and Mills, 2018 ; Jipson et al., 2018 ). This curiosity can promote lasting effects; Shah et al. (2018) show that young children’s curiosity, reported by parents at the start of kindergarten, relates to academic school readiness. In one of the few longitudinal studies including curiosity, research shows that parents’ promotion of curiosity early in childhood leads to science intrinsic motivation years later and science achievement in high school ( Gottfried et al., 2016 ). More generally, curiosity can provide a remedy to boredom, giving children a goal to direct their behavior and the motivation to act on their curiosity ( Litman and Silvia, 2006 ).

Curiosity as Support for Directing Information-Seeking Behavior

Gopnik et al. (2015) suggest that adults are efficient in their attention allocation, developed through extensive experience, but this attentional control comes at the cost of missing much of what is going on around them unrelated to their goals. Children have less experience and skill in focusing their attention, and more exploration-oriented goals, resulting in more open-ended exploratory behavior but also more distraction. Curiosity can help focus children’s attention on the specific information being sought (e.g., Legare, 2014 ). For example, when 7–9-year-old children completed a discovery-learning task in a museum, curiosity was related to more efficient learning-more curious children were quicker and learned more from similar exploration than less-curious children ( van Schijndel et al., 2018 ). Although children are quite capable of using questions to express curiosity and request specific information ( Berlyne, 1954 ; Chin and Osborne, 2010 ; Jirout and Zimmerman, 2015 ; Kidd and Hayden, 2015 ; Luce and Hsi, 2015 ), these skills can and should be strategically supported, as question asking plays a fundamental role in science and is important to develop ( Chouinard et al., 2007 ; Dewey, 1910 ; National Governors Association, 2010 ; American Association for the Advancement of Science [AAAS], 1993 ; among others). Indeed, the National Resource Council (2012) National Science Education Standards include question asking as the first of eight scientific and engineering practices that span all grade levels and content areas.

Children are proficient in requesting information from quite early ages ( Ronfard et al., 2018 ). Yet, there are limitations to children’s question asking; it can be “inefficient.” For example, to identify a target object from an array, young children often ask confirmation questions or make guesses rather than using more efficient “constraint-seeking” questions ( Mills et al., 2010 ; Ruggeri and Lombrozo, 2015 ). However, this behavior is observed in highly structured problem-solving tasks, during which children likely are not very curious. In fact, if the environment contains other things that children are curious about, it could be more efficient to use a simplistic strategy, freeing up cognitive resources for the true target of their curiosity. More research is needed to better understand children’s use of curiosity-driven questioning behavior as well as exploration, but naturalistic observations show that children do ask questions spontaneously to gain information, and that their questions (and follow-up questions) are effective in obtaining desired information ( Nelson et al., 2004 ; Kelemen et al., 2005 ; Chouinard et al., 2007 ).

Curiosity as Support for Deeper Learning

Returning to the definition of curiosity as information seeking to address knowledge gaps, becoming curious-by definition-involves the activation of previous knowledge, which enhances learning ( VanLehn et al., 1992 ; Conati and Carenini, 2001 ). The active learning that results from curiosity-driven information seeking involves meaningful cognitive engagement and constructive processing that can support deeper learning ( Bonwell and Eison, 1991 ; King, 1994 ; Loyens and Gijbels, 2008 ). The constructive process of seeking information to generate new thinking or new knowledge in response to curiosity is a more effective means of learning than simply receiving information ( Chi and Wylie, 2014 ). Even if information is simply given to a child as a result of their asking a question, the mere process of recognizing the gap in one’s knowledge to have a question activates relevant previous knowledge and leads to more effective storage of the new information within a meaningful mental representation; the generation of the question is a constructive process in itself. Further, learning more about a topic allows children to better recognize their related knowledge and information gaps ( Danovitch et al., 2019 ). This metacognitive reasoning supports learning through the processes of activating, integrating, and inferring involved in the constructive nature of curiosity-drive information seeking ( Chi and Wylie, 2014 ). Consistent with this theory, Lamnina and Chase (2019) showed that higher curiosity, which increased with the amount of uncertainty in a task, related to greater transfer of middle school students’ learning about specific science topics.

Promoting Curiosity in Young Children

Curiosity is rated by early childhood educators as “very important” or “essential” for school readiness and considered to be even more important than discrete academic skills like counting and knowing the alphabet ( Heaviside et al., 1993 ; West et al., 1993 ), behind only physical health and communication skills in importance ( Harradine and Clifford, 1996 ). Engel (2011 , 2013) finds that curiosity declines with development and suggests that understanding how to promote or at least sustain it is important. Although children’s curiosity is considered a natural characteristic that is present at birth, interactions with and responses from others can likely influence curiosity, both at a specific moment and context and as a more stable disposition ( Jirout et al., 2018 ). For example, previous work suggests that curiosity can be promoted by encouraging children to feel comfortable with and explore uncertainty ( Jirout et al., 2018 ); experiences that create uncertainty lead to higher levels of curious behavior (e.g., Bonawitz et al., 2011 ; Engel and Labella, 2011 ; Gordon et al., 2015 ).

One strategy for promoting curiosity is through classroom climate; children should feel safe and be encouraged to be curious and exploration and questions should be valued ( Pianta et al., 2008 ). This is accomplished by de-emphasizing being “right” or all-knowing, and instead embracing uncertainty and gaps in one’s own knowledge as opportunities to learn. Another strategy to promote curiosity is to provide support for the information-seeking behaviors that children use to act on their curiosity. There are several specific strategies that may promote children’s curiosity (see Jirout et al., 2018 , for additional strategies), including:

• 1. Encourage and provide opportunities for children to explore and “figure out,” emphasizing the value of the process (exploration) over the outcome (new knowledge or skills). Children cannot explore if opportunities are not provided to them, and they will not ask questions if they do not feel that their questions are welcomed. Even if opportunities and encouragement are provided, the fear of being wrong can keep children from trying to learn new things ( Martin and Marsh, 2003 ; Martin, 2011 ). Active efforts to discover or “figure out” are more effective at supporting learning than simply telling children something or having them practice learned procedures ( Schwartz and Martin, 2004 ). Children can explore when they have guidance and support to engage in think-aloud problem solving, instead of being told what to try or getting questions answered directly ( Chi et al., 1994 ).
• 2. Model curiosity for children, allowing them to see that others have things that they do not know and want to learn about, and that others also enjoy information-seeking activities like asking questions and researching information. Technology makes information seeking easier than it has ever been. For example, children are growing up surrounded by internet-connected devices (more than 8 per capita in 2018), and asking questions is reported to be one of the most frequent uses of smart speakers ( NPR-Edison Research Spring, 2019 ). Observing others seeking information as a normal routine can encourage children’s own question asking ( McDonald, 1992 ).
• 3. Children spontaneously ask questions, but adults can encourage deeper questioning by using explicit prompts and then supporting children to generate questions ( King, 1994 ; Rosenshine et al., 1996 ). This is different from asking “Do you have any questions?,” which may elicit a simple “yes” or “no” response from the child. Instead, asking, “What questions do you have?” is more likely to provide a cue for children to practice analyzing what they do not know and generating questions. The ability to evaluate one’s knowledge develops through practice, and scaffolding this process by helping children recognize questions to ask can effectively support development ( Kuhn and Pearsall, 2000 ; Chin and Brown, 2002 ).
• 4. Other methods to encourage curiosity include promoting and reinforcing children’s thinking about alternative ideas, which could also support creativity. Part of being curious is recognizing questions that can be asked, and if children understand that there are often multiple solutions or ways to do something they will be more likely to explore to learn “ how we know and why we believe; e.g., to expose science as a way of knowing” ( Duschl and Osborne, 2002 , p. 40). Children who learn to “think outside the box” will question what they and others know and better understand the dynamic nature of knowledge, supporting a curious mindset ( Duschl and Osborne, 2002 ).

Although positive interactions can promote and sustain curiosity in young children, curiosity can also be suppressed or discouraged through interactions that emphasize performance or a focus on explicit instruction ( Martin and Marsh, 2003 ; Martin, 2011 ; Hulme et al., 2013 ). Performance goals, which are goals that are focused on demonstrating the attainment of a skill, can lead to lower curiosity to avoid distraction or risk to achieving the goal ( Hulme et al., 2013 ). Mastery goals, which focus on understanding and the learning process, support learning for its own sake ( Ames, 1993 ). When children are older and attend school, they experience expectations that prioritize performance metrics over academic and intellectual exploration, such as through tests and state-standardized assessments, which discourages curiosity ( Engel, 2011 ; Jirout et al., 2018 ). In my own recent research, we observed a positive association between teachers’ use of mastery-focused language and their use of curiosity-promoting instructional practices in preschool math and science lessons ( Jirout and Vitiello, 2019 ). Among 5th graders, student ratings of teacher emphasis on standardized testing was associated with lower observed curiosity-promotion by teachers ( Jirout and Vitiello, 2019 ). It is likely that learning orientations influence children’s curiosity even before children begin formal schooling, and de-emphasizing performance is a way to support curiosity.

In summary, focusing on the process of “figuring out” something children do not know, modeling and explicitly prompting exploration and question asking, and supporting metacognitive and creative thinking are all ways to promote curiosity and support effective cognitive engagement during learning. These methods are consistent with inquiry-based and active learning, which both are grounded in constructivism and information gaps similar to the current operationalization of curiosity ( Jirout and Klahr, 2012 ; Saylor and Ganea, 2018 ; van Schijndel et al., 2018 ). Emphasizing performance, such as academic climates focused on teaching rote procedures and doing things the “correct” way to get the right answer, can suppress or discourage curiosity. Instead, creating a supportive learning climate and responding positively to curiosity are likely to further reinforce children’s information seeking, and to sustain their curiosity so that it can support scientific thinking and learning.

Conclusion: a Call for Research

In this article, I describe evidence from the limited existing research showing that curiosity is important and relates to science learning, and I suggest several mechanisms through which curiosity can support science learning. The general perspective presented here is that science learning can and should be supported by promoting curiosity, and I provide suggestions for promoting (and avoiding the suppression of) curiosity in early childhood. However, much more research is needed to address the complex challenge of educational applications of this work. Specifically, the suggested mechanisms through which curiosity promotes learning need to be studied to tease apart questions of directionality, the influence of related factors such as interest, the impact of context and learning domain on these relations, and the role of individual differences. Both the influence of curiosity on learning and effective ways to promote it likely change in interesting and important ways across development, and research is needed to understand this development-especially through studying change in individuals over time. Finally, it is important to acknowledge that learning does not happen in isolation, and one’s culture and environment have important roles in shaping one’s development. Thus, application of research on curiosity and science learning must include studies of the influence of social factors such as socioeconomic status and contexts, the influence of peers, teachers, parents, and others in children’s environments, and the many ways that culture may play a role, both in the broad values and beliefs instilled in children and the adults interacting with them, and in the influences of behavior expectations and norms. For example, parents across cultures might respond differently to children’s questions, so cross-cultural differences in questions likely indicate something other than differences in curiosity ( Ünlütabak et al., 2019 ). Although curiosity likely promotes science learning across cultures and contexts, the ways in which it does so and effective methods of promoting it may differ, which is an important area for future research to explore. Despite the benefits I present, curiosity seems to be rare or even absent from formal learning contexts ( Engel, 2013 ), even as children show curiosity about things outside of school ( Post and Walma van der Molen, 2018 ). Efforts to promote science learning should focus on the exciting potential for curiosity in supporting children’s learning, as promoting young children’s curiosity in science can start children on a positive trajectory for later learning.

Ethics Statement

Written informed consent was obtained from the individual(s) and/or minor(s)’ legal guardian/next of kin publication of any potentially identifiable images or data included in this article.

Author Contributions

JJ conceived of the manuscript topic and wrote the manuscript.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. This publication was made possible through the support of grants from the John Templeton Foundation, the Spencer Foundation, and the Center for Curriculum Redesign. The opinions expressed in this publication are those of the author and do not necessarily reflect the views of the John Templeton Foundation or other funders.

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Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

You will be using throughout CRIT 602 to explore the larger context for your field of study and its associated professions will be critical inquiry:

  “ Critical inquiry is the process of gathering and evaluating information , ideas, and assumptions from multiple perspectives to produce well-reasoned analysis and understanding , and leading to new ideas, applications and questions ” (“Critical Inquiry,” n.d.).

A high stakes example of critical inquiry is clinical diagnosis and treatment of physical or mental illness, which involves the doctor asking an entire matrix of complex questions at every stage of the process and knowledge of a body of research that is continually changing, as well as the ability to determine how each set of questions intersects with the body of research.

In a work setting, critical inquiry might be used to solve an organizational problem, such as poor morale among support staff. A director must ask: how can we identify the cause of the decline in morale that we’re experiencing now?  When did it start? What factors could have led to it? Have there been changes in management? Have there been changes in our business practices? Could any external factors have caused or contributed to the decline in morale among these staff? Are organizations similar to ours experiencing a similar problem locally? Regionally? Nationally? Has research been done that we could use to find solutions to the problem? What’s been written in the professional literature for our industry that might help us solve our morale problem? Should we bring in a consultant?

In daily life, you might use critical inquiry to buy a car. What kind of vehicle will best meet my needs and the needs of my family? Truck, car, SUV? What about reliability history for the make and model I’m considering? What price range can I afford? How can I find out what is a fair price for the make and model I’m considering? Should I go with dealer financing or get the loan directly through my own bank or credit union? Why is this guy trying to sell me an extended warranty when I’ve already been here for three hours, and I want to go home?

The Role of Reflection in Critical Inquiry

Reflective learning is included in one of the three primary skills goals for CRIT 602: Reflect on learning to guide professional practice. The Center for Simplified Strategic Planning (CSSP) identifies reflection as a critical skill for strategic thinkers:

“Critical Skill #6: [Strategic thinkers] are committed lifelong learners and learn from each of their experiences. They use their experiences to enable them to think better on strategic issues” (“Strategic Thinking,” n.d.) .

We learn from experience by reflecting on it: Simply put, reflection is thinking about new experiences, connecting them to prior experiences, and learning something useful for the future.

In “Defining Reflection: Another Look at John Dewey and Reflective Thinking,” Carol R. Rodgers (2002) defines reflection in a way that aligns particularly well with the critical inquiry you will be conducting into the larger context for your field of study and its associated professions:

1. Reflection is a meaning-making process that moves a learner from one experience into the next with deeper understanding of its relationships with and connections to other experiences and ideas. It is the thread that makes continuity of learning possible, and ensures the progress of the individual and, ultimately, society. It is a means to essentially moral ends.

[Analytical Thinking Goal: You break concepts or evidence into parts and explain how the parts are related to each other.]

2. Reflection is a systematic, rigorous, disciplined way of thinking, with its roots in scientific inquiry.

[Analytical Thinking Goal: Your conclusion is logically tied to information. You have identified consequences and implications clearly.]

3. Reflection needs to happen in community, in interaction with others.

[ CCSP “Critical Skill #8: [Strategic thinkers] are committed to and seek advice from others. They may use a coach, a mentor, a peer advisory group or some other group that they can confide in and offer up ideas for feedback.”] (“Strategic Thinking,” n.d.)

4. Reflection requires attitudes that value the personal and intellectual growth of oneself and others.

[Developing a Community of Practice]

Critical Inquiry (AFCI 101). (n.d.). Retrieved September 10, 2022, from University of South Carolina Aiken website: https://www.usca.edu/academic-affairs/general-education/critical-inquiry.dot

Rodgers, C. (2002). Defining Reflection Another Look at John Dewey and Reflective Thinking. Teachers College Record, 104(4), 842-866.(1).pdf

Strategic thinking: 11 critical thinking skills. (n.d.). Retrieved September 10, 2018, from Center for Simplified Strategic Planning website: https://www.cssp.com/CD0808b/ CriticalStrategicThinkingSkills/

Feel free to check out the full article if you’re interested! (John Dewey was a top influencer in the field of public education.)

• Defining Reflection: Ano ther Look at John Dewey and Reflective Thinking

CRIT 602 Readings and Resources Copyright © 2019 by Granite State College (USNH) is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Published: 11 September 2019

Inquiry and critical thinking skills for the next generation: from artificial intelligence back to human intelligence

• Jonathan Michael Spector   ORCID: orcid.org/0000-0002-6270-3073 1 &
• Shanshan Ma 1  

Smart Learning Environments volume  6 , Article number:  8 ( 2019 ) Cite this article

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Along with the increasing attention to artificial intelligence (AI), renewed emphasis or reflection on human intelligence (HI) is appearing in many places and at multiple levels. One of the foci is critical thinking. Critical thinking is one of four key 21st century skills – communication, collaboration, critical thinking and creativity. Though most people are aware of the value of critical thinking, it lacks emphasis in curricula. In this paper, we present a comprehensive definition of critical thinking that ranges from observation and inquiry to argumentation and reflection. Given a broad conception of critical thinking, a developmental approach beginning with children is suggested as a way to help develop critical thinking habits of mind. The conclusion of this analysis is that more emphasis should be placed on developing human intelligence, especially in young children and with the support of artificial intelligence. While much funding and support goes to the development of artificial intelligence, this should not happen at the expense of human intelligence. Overall, the purpose of this paper is to argue for more attention to the development of human intelligence with an emphasis on critical thinking.

Introduction

In recent decades, advancements in Artificial Intelligence (AI) have developed at an incredible rate. AI has penetrated into people’s daily life on a variety of levels such as smart homes, personalized healthcare, security systems, self-service stores, and online shopping. One notable AI achievement was when AlphaGo, a computer program, defeated the World Go Champion Mr. Lee Sedol in 2016. In the previous year, AlphaGo won in a competition against a professional Go player (Silver et al. 2016 ). As Go is one of the most challenging games, the wins of AI indicated a breakthrough. Public attention has been further drawn to AI since then, and AlphaGo continues to improve. In 2017, a new version of AlphaGo beat Ke Jie, the current world No.1 ranking Go player. Clearly AI can manage high levels of complexity.

Given many changes and multiple lines of development and implement, it is somewhat difficult to define AI to include all of the changes since the 1980s (Luckin et al. 2016 ). Many definitions incorporate two dimensions as a starting point: (a) human-like thinking, and (b) rational action (Russell and Norvig 2009 ). Basically, AI is a term used to label machines (computers) that imitate human cognitive functions such as learning and problem solving, or that manage to deal with complexity as well as human experts.

AlphaGo’s wins against human players were seen as a comparison between artificial and human intelligence. One concern is that AI has already surpassed HI; other concerns are that AI will replace humans in some settings or that AI will become uncontrollable (Epstein 2016 ; Fang et al. 2018 ). Scholars worry that AI technology in the future might trigger the singularity (Good 1966 ), a hypothesized future that the development of technology becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization (Vinge 1993 ).

The famous theoretical physicist Stephen Hawking warned that AI might end mankind, yet the technology he used to communicate involved a basic form of AI (Cellan-Jones 2014 ). This example highlights one of the basic dilemmas of AI – namely, what are the overall benefits of AI versus its potential drawbacks, and how to move forward given its rapid development? Obviously, basic or controllable AI technologies are not what people are afraid of. Spector et al. 1993 distinguished strong AI and weak AI. Strong AI involves an application that is intended to replace an activity performed previously by a competent human, while weak AI involves an application that aims to enable a less experienced human to perform at a much higher level. Other researchers categorize AI into three levels: (a) artificial narrow intelligence (Narrow AI), (b) artificial general intelligence (General AI), and (c) artificial super intelligence (Super AI) (Siau and Yang 2017 ; Zhang and Xie 2018 ). Narrow AI, sometimes called weak AI, refers to a computer that focus on a narrow task such as AlphaZero or a self-driving car. General AI, sometimes referred to as strong AI, is the simulation of human-level intelligence, which can perform more cognitive tasks as well as most humans do. Super AI is defined by Bostrom ( 1998 ) as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills” (p.1).

Although the consequence of singularity and its potential benefits or harm to the human race have been intensely debated, an undeniable fact is that AI is capable of undertaking recursive self-improvement. With the increasing improvement of this capability, more intelligent generations of AI will appear rapidly. On the other hand, HI has its own limits and its development requires continuous efforts and investment from generation to generation. Education is the main approach humans use to develop and improve HI. Given the extraordinary growth gap between AI and HI, eventually AI can surpass HI. However, that is no reason to neglect the development and improvement of HI. In addition, in contrast to the slow development rate of HI, the growth of funding support to AI has been rapidly increasing according to the following comparison of support for artificial and human intelligence.

The funding support for artificial and human intelligence

There are challenges in comparing artificial and human intelligence by identifying funding for both. Both terms are somewhat vague and can include a variety of aspects. Some analyses will include big data and data analytics within the sphere of artificial intelligence and others will treat them separately. Some will include early childhood developmental research within the sphere of support for HI and others treat them separately. Education is a major way of human beings to develop and improve HI. The investments in education reflect the efforts put on the development of HI, and they pale in comparison with investments in AI.

Sources also vary from governmental funding of research and development to business and industry investments in related research and development. Nonetheless, there are strong indications of increased funding support for AI in North America, Europe and Asia, especially in China. The growth in funding for AI around the world is explosive. According to ZDNet, AI funding more than doubled from 2016 to 2017 and more than tripled from 2016 to 2018. The growth in funding for AI in the last 10 years has been exponential. According to Venture Scanner, there are approximately 2500 companies that have raised $60 billion in funding from 3400 investors in 72 different countries (see https://www.slideshare.net/venturescanner/artificial-intelligence-q1-2019-report-highlights ). Areas included in the Venture Scanner analysis included virtual assistants, recommendation engines, video recognition, context-aware computing, speech recognition, natural language processing, machine learning, and more.

The above data on AI funding focuses primarily on companies making products. There is no direct counterpart in the area of HI where the emphasis is on learning and education. What can be seen, however, are trends within each area. The above data suggest exponential growth in support for AI. In contrast, according to the Urban Institute, per-student funding in the USA has been relatively flat for nearly two decades, with a few states showing modest increases and others showing none (see http://apps.urban.org/features/education-funding-trends/ ). Funding for education is complicated due to the various sources. In the USA, there are local, state and federal sources to consider. While that mixture of funding sources is complex, it is clear that federal and state spending for education in the USA experienced an increase after World War II. However, since the 1980s, federal spending for education has steadily declined, and state spending on education in most states has declined since 2010 according to a government report (see https://www.usgovernmentspending.com/education_spending ). This decline in funding reflects the decreasing emphasis on the development of HI, which is a dangerous signal.

Decreased support for education funding in the USA is not typical of what is happening in other countries, according to The Hechinger Report (see https://hechingerreport.org/rest-world-invests-education-u-s-spends-less/ ). For example, in the period of 2010 to 2014, American spending on elementary and high school education declined 3%, whereas in the same period, education spending in the 35 countries in the OECD rose by 5% with some countries experiencing very significant increases (e.g., 76% in Turkey).

Such data can be questioned in terms of how effectively funds are being spent or how poorly a country was doing prior to experiencing a significant increase. However, given the performance of American students on the Program for International Student Assessment (PISA), the relative lack of funding support in the USA is roughly related with the mediocre performance on PISA tests (see https://nces.ed.gov/surveys/pisa/pisa2015/index.asp ). Research by Darling-Hammond ( 2014 ) indicated that in order to improve learning and reduce the achievement gap, systematic government investments in high-need schools would be more effective if the focus was on capacity building, improving the knowledge and skills of educators and the quality of curriculum opportunities.

Though HI could not be simply defined by the performance on PISA test, improving HI requires systematic efforts and funding support in high-need areas as well. So, in the following section, we present a reflection on HI.

Reflection on human intelligence

Though there is a variety of definitions of HI, from the perspective of psychology, according to Sternberg ( 1999 ), intelligence is a form of developing expertise, from a novice or less experienced person to an expert or more experienced person, a student must be through multiple learning (implicit and explicit) and thinking (critical and creative) processes. In this paper, we adopted such a view and reflected on HI in the following section by discussing learning and critical thinking.

What is learning?

We begin with Gagné’s ( 1985 ) definition of learning as characterized by stable and persistent changes in what a person knows or can do. How do humans learn? Do you recall how to prove that the square root of 2 is not a rational number, something you might have learned years ago? The method is intriguing and is called an indirect proof or a reduction to absurdity – assume that the square root of 2 is a rational number and then apply truth preserving rules to arrive at a contradiction to show that the square root of 2 cannot be a rational number. We recommend this as an exercise for those readers who have never encountered that method of learning and proof. (see https://artofproblemsolving.com/wiki/index.php/Proof_by_contradiction ). Yet another interesting method of learning is called the process of elimination, sometimes accredited to Arthur Conan Doyle’s ( 1926 ) in The Adventure of the Blanched Soldier – Sherlock Holmes says to Dr. Watson that the process of elimination “starts upon the supposition that when you have eliminated all which is impossible, that whatever remains, however improbable, must be the truth ” (see https://www.dfw-sherlock.org/uploads/3/7/3/8/37380505/1926_november_the_adventure_of_the_blanched_soldier.pdf ).

The reason to mention Sherlock Holmes early in this paper is to emphasize the role that observation plays in learning. The character Sherlock Holmes was famous for his observation skills that led to his so-called method of deductive reasoning (a process of elimination), which is what logicians would classify as inductive reasoning as the conclusions of that reasoning process are primarily probabilistic rather than certain, unlike the proof of the irrationality of the square root of 2 mentioned previously.

In dealing with uncertainty, it seems necessary to make observations and gather evidence that can lead one to a likely conclusion. Is that not what reasonable people and accomplished detectives do? It is certainly what card counters do at gambling houses; they observe high and low value cards that have already been played in order to estimate the likelihood of the next card being a high or low value card. Observation is a critical process in dealing with uncertainty.

Moreover, humans typically encounter many uncertain situations in the course of life. Few people encounter situations which require resolution using a mathematical proof such as the one with which this article began. Jonassen ( 2000 , 2011 ) argued that problem solving is one of the most important and frequent activities in which people engage. Moreover, many of the more challenging problems are ill-structured in the sense that (a) there is incomplete information pertaining to the situation, or (b) the ideal resolution of the problem is unknown, or (c) how to transform a problematic situation into an acceptable situation is unclear. In short, people are confronted with uncertainty nearly every day and in many different ways. The so called key 21st century skills of communication, collaboration, critical thinking and creativity (the 4 Cs; see http://www.battelleforkids.org/networks/p21 ) are important because uncertainty is a natural and inescapable aspect of the human condition. The 4 Cs are interrelated and have been presented by Spector ( 2018 ) as interrelated capabilities involving logic and epistemology in the form of the new 3Rs – namely, re-examining, reasoning, and reflecting. Re-examining is directly linked to observation as a beginning point for inquiry. The method of elimination is one form of reasoning in which a person engages to solve challenging problems. Reflecting on how well one is doing in the life-long enterprise of solving challenging problems is a higher kind of meta-cognitive activity in which accomplished problem-solvers engage (Ericsson et al. 1993 ; Flavell 1979 ).

Based on these initial comments, a comprehensive definition of critical thinking is presented next in the form of a framework.

A framework of critical thinking

Though there is variety of definitions of critical thinking, a concise definition of critical thinking remains elusive. For delivering a direct understanding of critical thinking to readers such as parents and school teachers, in this paper, we present a comprehensive definition of critical thinking through a framework that includes many of the definitions offered by others. Critical thinking, as treated broadly herein, is a multi-dimensioned and multifaceted human capability. Critical thinking has been interpreted from three perspectives: education, psychology, and epistemology, all of which are represented in the framework that follows.

In a developmental approach to critical thinking, Spector ( 2019 ) argues that critical thinking involves a series of cumulative and related abilities, dispositions and other variables (e.g., motivation, criteria, context, knowledge). This approach proceeds from experience (e.g., observing something unusual) and then to various forms of inquiry, investigation, examination of evidence, exploration of alternatives, argumentation, testing conclusions, rethinking assumptions, and reflecting on the entire process.

Experience and engagement are ongoing throughout the process which proceeds from relatively simple experiences (e.g., direct and immediate observation) to more complex interactions (e.g., manipulation of an actual or virtual artifact and observing effects).

The developmental approach involves a variety of mental processes and non-cognitive states, which help a person’s decision making to become purposeful and goal directed. The associated critical thinking skills enable individuals to be likely to achieve a desired outcome in a challenging situation.

In the process of critical thinking, apart from experience, there are two additional cognitive capabilities essential to critical thinking – namely, metacognition and self-regulation . Many researchers (e.g., Schraw et al. 2006 ) believe that metacognition has two components: (a) awareness and understanding of one’s own thoughts, and (b) the ability to regulate one’s own cognitive processes. Some other researchers put more emphasis on the latter component. For example, Davies ( 2015 ) described metacognition as the capacity to monitor the quality of one’s thinking process, and then to make appropriate changes. However, the American Psychology Association (APA) defines metacognition as an awareness and understanding of one’s own thought with the ability to control related cognitive processes (see https://psycnet.apa.org/record/2008-15725-005 ).

Although the definition and elaboration of these two concepts deserve further exploration, they are often used interchangeably (Hofer and Sinatra 2010 ; Schunk 2008 ). Many psychologists see the two related capabilities of metacognition and self-regulation as being closely related - two sides on one coin, so to speak. Metacognition involves or emphasizes awareness, whereas self-regulation involves and emphasizes appropriate control. These two concepts taken together enable a person to create a self-regulatory mechanism, which monitors and regulates the corresponding skills (e.g., observation, inquiry, interpretation, explanation, reasoning, analysis, evaluation, synthesis, reflection, and judgement).

As to the critical thinking skills, it should be noted that there is much discussion about the generalizability and domain specificity of them, just as there is about problem-solving skills in general (Chi et al. 1982 ; Chiesi et al. 1979 ; Ennis 1989 ; Fischer 1980 ). The research supports the notion that to achieve high levels of expertise and performance, one must develop high levels of domain knowledge. As a consequence, becoming a highly effective critical thinker in a particular domain of inquiry requires significant domain knowledge. One may achieve such levels in a domain in which one has significant domain knowledge and experience but not in a different domain in which one has little domain knowledge and experience. The processes involved in developing high levels of critical thinking are somewhat generic. Therefore, it is possible to develop critical thinking in nearly any domain when the two additional capabilities of metacognition and self-regulation are coupled with motivation and engagement and supportive emotional states (Ericsson et al. 1993 ).

Consequently, the framework presented here (see Fig. 1 ) is built around three main perspectives about critical thinking (i.e., educational, psychological and epistemological) and relevant learning theories. This framework provides a visual presentation of critical thinking with four dimensions: abilities (educational perspective), dispositions (psychological perspective), levels (epistemological perspective) and time. Time is added to emphasize the dynamic nature of critical thinking in terms of a specific context and a developmental approach.

Critical thinking often begins with simple experiences such as observing a difference, encountering a puzzling question or problem, questioning someone’s statement, and then leads, in some instances to an inquiry, and then to more complex experiences such as interactions and application of higher order thinking skills (e.g., logical reasoning, questioning assumptions, considering and evaluating alternative explanations).

If the individual is not interested in what was observed, an inquiry typically does not begin. Inquiry and critical thinking require motivation along with an inquisitive disposition. The process of critical thinking requires the support of corresponding internal indispositions such as open-mindedness and truth-seeking. Consequently, a disposition to initiate an inquiry (e.g., curiosity) along with an internal inquisitive disposition (e.g., that links a mental habit to something motivating to the individual) are both required (Hitchcock 2018 ). Initiating dispositions are those that contribute to the start of inquiry and critical thinking. Internal dispositions are those that initiate and support corresponding critical thinking skills during the process. Therefore, critical thinking dispositions consist of initiating dispositions and internal dispositions. Besides these factors, critical thinking also involves motivation. Motivation and dispositions are not mutually exclusive, for example, curiosity is a disposition and also a motivation.

Critical thinking abilities and dispositions are two main components of critical thinking, which involve such interrelated cognitive constructs as interpretation, explanation, reasoning, evaluation, synthesis, reflection, judgement, metacognition and self-regulation (Dwyer et al. 2014 ; Davies 2015 ; Ennis 2018 ; Facione 1990 ; Hitchcock 2018 ; Paul and Elder 2006 ). There are also some other abilities such as communication, collaboration and creativity, which are now essential in current society (see https://en.wikipedia.org/wiki/21st_century_skills ). Those abilities along with critical thinking are called the 4Cs; they are individually monitored and regulated through metacognitive and self-regulation processes.

The abilities involved in critical thinking are categorized in Bloom’s taxonomy into higher order skills (e.g., analyzing and synthesizing) and lower level skills (e.g., remembering and applying) (Anderson and Krathwohl 2001 ; Bloom et al. 1956 ).

The thinking process can be depicted as a spiral through both lower and higher order thinking skills. It encompasses several reasoning loops. Some of them might be iterative until a desired outcome is achieved. Each loop might be a mix of higher order thinking skills and lower level thinking skills. Each loop is subject to the self-regulatory mechanism of metacognition and self-regulation.

But, due to the complexity of human thinking, a specific spiral with reasoning loops is difficult to represent. Therefore, instead of a visualized spiral with an indefinite number of reasoning loops, the developmental stages of critical thinking are presented in the diagram (Fig. 1 ).

Besides, most of the definitions of critical thinking are based on the imagination about ideal critical thinkers such as the consensus generated from the Delphi report (Facione 1990 ). However, according to Dreyfus and Dreyfus ( 1980 ), in the course of developing an expertise, students would pass through five stages. Those five stages are “absolute beginner”, “advanced beginner”, “competent performer”, “proficient performer,” and “intuitive expert performer”. Dreyfus and Dreyfus ( 1980 ) described the five stages the result of the successive transformations of four mental functions: recollection, recognition, decision making, and awareness.

In the course of developing critical thinking and expertise, individuals will pass through similar stages which are accompanied with the increasing practices and accumulation of experience. Through the intervention and experience of developing critical thinking, as a novice, tasks are decomposed into context-free features which could be recognized by students without the experience of particular situations. For further improving, students need to be able to monitor their awareness, and with a considerable experience. They can note recurrent meaningful component patterns in some contexts. Gradually, increased practices expose students to a variety of whole situations which enable the students to recognize tasks in a more holistic manner as a professional. On the other hand, with the increasing accumulation of experience, individuals are less likely to depend simply on abstract principles. The decision will turn to something intuitive and highly situational as well as analytical. Students might unconsciously apply rules, principles or abilities. A high level of awareness is absorbed. At this stage, critical thinking is turned into habits of mind and in some cases expertise. The description above presents a process of critical thinking development evolving from a novice to an expert, eventually developing critical thinking into habits of mind.

We mention the five-stage model proposed by Dreyfus and Dreyfus ( 1980 ) to categorize levels of critical thinking and emphasize the developmental nature involved in becoming a critical thinker. Correspondingly, critical thinking is categorized into 5 levels: absolute beginner (novice), advanced beginner (beginner), competent performer (competent), proficient performer (proficient), and intuitive expert (expert).

Ability level and critical thinker (critical thinking) level together represent one of the four dimensions represented in Fig. 1 .

In addition, it is noteworthy that the other two elements of critical thinking are the context and knowledge in which the inquiry is based. Contextual and domain knowledge must be taken into account with regard to critical thinking, as previously argued. Besides, as Hitchcock ( 2018 ) argued, effective critical thinking requires knowledge about and experience applying critical thinking concepts and principles as well.

Critical thinking is considered valuable across disciplines. But except few courses such as philosophy, critical thinking is reported lacking in most school education. Most of researchers and educators thus proclaim that integrating critical thinking across the curriculum (Hatcher 2013 ). For example, Ennis ( 2018 ) provided a vision about incorporating critical thinking across the curriculum in higher education. Though people are aware of the value of critical thinking, few of them practice it. Between 2012 and 2015, in Australia, the demand of critical thinking as one of the enterprise skills for early-career job increased 125% (Statista Research Department, 2016). According to a survey across 1000 adults by The Reboot Foundation 2018 , more than 80% of respondents believed that critical thinking skills are lacking in today’s youth. Respondents were deeply concerned that schools do not teach critical thinking. Besides, the investigation also found that respondents were split over when and how to teach critical thinking, clearly.

In the previous analysis of critical thinking, we presented the mechanism of critical thinking instead of a concise definition. This is because, given the various perspectives of interpreting critical thinking, it is not easy to come out with an unitary definition, but it is essential for the public to understand how critical thinking works, the elements it involves and the relationships between them, so they can achieve an explicit understanding.

In the framework, critical thinking starts from simple experience such as observing a difference, then entering the stage of inquiry, inquiry does not necessarily turn the thinking process into critical thinking unless the student enters a higher level of thinking process or reasoning loops such as re-examining, reasoning, reflection (3Rs). Being an ideal critical thinker (or an expert) requires efforts and time.

According to the framework, simple abilities such as observational skills and inquiry are indispensable to lead to critical thinking, which suggests that paying attention to those simple skills at an early stage of children can be an entry point to critical thinking. Considering the child development theory by Piaget ( 1964 ), a developmental approach spanning multiple years can be employed to help children develop critical thinking at each corresponding development stage until critical thinking becomes habits of mind.

Although we emphasized critical thinking in this paper, for the improvement of intelligence, creative thinking and critical thinking are separable, they are both essential abilities that develop expertise, eventually drive the improvement of HI at human race level.

As previously argued, there is a similar pattern among students who think critically in different domains, but students from different domains might perform differently in creativity because of different thinking styles (Haller and Courvoisier 2010 ). Plus, students have different learning styles and preferences. Personalized learning has been the most appropriate approach to address those differences. Though the way of realizing personalized learning varies along with the development of technologies. Generally, personalized learning aims at customizing learning to accommodate diverse students based on their strengths, needs, interests, preferences, and abilities.

Meanwhile, the advancement of technology including AI is revolutionizing education; students’ learning environments are shifting from technology-enhanced learning environments to smart learning environments. Although lots of potentials are unrealized yet (Spector 2016 ), the so-called smart learning environments rely more on the support of AI technology such as neural networks, learning analytics and natural language processing. Personalized learning is better supported and realized in a smart learning environment. In short, in the current era, personalized learning is to use AI to help learners perform at a higher level making adjustments based on differences of learners. This is the notion with which we conclude – the future lies in using AI to improve HI and accommodating individual differences.

The application of AI in education has been a subject for decades. There are efforts heading to such a direction though personalized learning is not technically involved in them. For example, using AI technology to stimulate critical thinking (Zhu 2015 ), applying a virtual environment for building and assessing higher order inquiry skills (Ketelhut et al. 2010 ). Developing computational thinking through robotics (Angeli and Valanides 2019 ) is another such promising application of AI to support the development of HI.

However, almost all of those efforts are limited to laboratory experiments. For accelerating the development rate of HI, we argue that more emphasis should be given to the development of HI at scale with the support of AI, especially in young children focusing on critical and creative thinking.

In this paper, we argue that more emphasis should be given to HI development. Rather than decreasing the funding of AI, the analysis of progress in artificial and human intelligence indicates that it would be reasonable to see increased emphasis placed on using various AI techniques and technologies to improve HI on a large and sustainable scale. Well, most researchers might agree that AI techniques or the situation might be not mature enough to support such a large-scale development. But it would be dangerous if HI development is overlooked. Based on research and theory drawn from psychology as well as from epistemology, the framework is intended to provide a practical guide to the progressive development of inquiry and critical thinking skills in young children as children represent the future of our fragile planet. And we suggested a sustainable development approach for developing inquiry and critical thinking (See, Spector 2019 ). Such an approach could be realized through AI and infused into HI development. Besides, a project is underway in collaboration with NetDragon to develop gamified applications to develop the relevant skills and habits of mind. A game-based assessment methodology is being developed and tested at East China Normal University that is appropriate for middle school children. The intention of the effort is to refocus some of the attention on the development of HI in young children.

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Abbreviations

Artificial Intelligence

Human Intelligence

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Acknowledgements

We wish to acknowledge the generous support of NetDragon and the Digital Research Centre at the University of North Texas.

Initial work is being funded through the NetDragon Digital Research Centre at the University of North Texas with Author as the Principal Investigator.

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Spector, J.M., Ma, S. Inquiry and critical thinking skills for the next generation: from artificial intelligence back to human intelligence. Smart Learn. Environ. 6 , 8 (2019). https://doi.org/10.1186/s40561-019-0088-z

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Guiding Students to Develop an Understanding of Scientific Inquiry: A Science Skills Approach to Instruction and Assessment

• Elisa M. Stone

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New approaches for teaching and assessing scientific inquiry and practices are essential for guiding students to make the informed decisions required of an increasingly complex and global society. The Science Skills approach described here guides students to develop an understanding of the experimental skills required to perform a scientific investigation. An individual teacher's investigation of the strategies and tools she designed to promote scientific inquiry in her classroom is outlined. This teacher-driven action research in the high school biology classroom presents a simple study design that allowed for reciprocal testing of two simultaneous treatments, one that aimed to guide students to use vocabulary to identify and describe different scientific practices they were using in their investigations—for example, hypothesizing, data analysis, or use of controls—and another that focused on scientific collaboration. A knowledge integration (KI) rubric was designed to measure how students integrated their ideas about the skills and practices necessary for scientific inquiry. KI scores revealed that student understanding of scientific inquiry increased significantly after receiving instruction and using assessment tools aimed at promoting development of specific inquiry skills. General strategies for doing classroom-based action research in a straightforward and practical way are discussed, as are implications for teaching and evaluating introductory life sciences courses at the undergraduate level.

INTRODUCTION

Instruction and assessment are often designed to teach and measure the science concepts students learn but are less likely to address the skills students must develop in order to answer meaningful scientific questions. To make informed decisions in modern society, students must routinely formulate questions, test ideas, collect and analyze data, support arguments with evidence, and collaborate with peers. To promote such skills, science educators have long recommended frequent experimental work and hands-on activities ( Dewey, 1916 ) and effective assessment methods for measuring critical scientific-thinking skills and evaluating performance in laboratory exercises ( National Research Council [NRC], 2000 ). It is crucial that we strive to meet the call to improve K–12 and undergraduate inquiry instruction and assessment set out by a number of national science education organizations ( NRC, 1996 , 2003 ; American Association for the Advancement of Science [AAAS], 1998 , 2009 ). Most recently, the Next Generation Science Standards (NGSS) provided guidelines for encouraging the practices that scientists and engineers engage in as they investigate and build models across the K–12 science curriculum and beyond ( NGSS, 2013 ).

Many educators have claimed that inquiry is especially important in urban environments and for engaging minority students in making math and science relevant for them ( Barnes et al. , 1989 ; Stigler and Heibert, 1999 ; Moses, 2001 ; Haberman, 2003 ; Tate et al. , 2008 ; Siritunga et al. , 2011 ). Inquiry-based instruction includes a variety of teaching strategies, such as questioning; focusing on language; and guiding students to make comparisons, analyze, synthesize, and model. Skills important for scientific thinking are often taught implicitly; that is, the instructor assumes students learn how to think like a scientist by simply engaging in frequent experimental work in the classroom. However, explicit approaches have been shown to be more effective, for example, in teaching nature of science concepts to both students and science teachers ( Abd-El-Khalick and Lederman, 2000 ; Lederman et al ., 2001 ). The classroom described in this action research study aims to create a learning environment that is explicit about these essential features of classroom inquiry.

An accumulation of evidence exists for how inquiry in the science classroom at both the undergraduate and K–12 levels is effective in promoting student understanding of various content areas in life sciences education. Examples include an increased understanding of a variety of key concepts in the life sciences ( Aronson and Silviera, 2009 ; Lau and Robinson, 2009 ; Rissing and Cogan, 2009 ; Ribarič and Kordaš, 2011 ; Siritunga et al. , 2011 ; Treacy et al. , 2011 ; Zion et al. , 2011 ; Ryoo and Linn, 2012 ). Moreover, Derting and Ebert-May (2010) have shown long-term improvements in learning for students who experience learning-centered inquiry in introductory biology classes. While several studies have correlated such inquiry-based curricula on specific science topics with improvements in general academic skills ( Lord and Orkwiszewski, 2006 ; Treacy et al. , 2011 ), more research is needed on how students develop and integrate their understanding of specific experimental and scientific inquiry skills, as well as what general strategies are effective for promoting and measuring this understanding.

The theoretical framework that lies at the foundation of this study is knowledge integration (KI). “The knowledge integration perspective … characterizes learners as developing a repertoire of ideas, adding new ideas from instruction, experience, or social interactions, sorting out these ideas in varied contexts, making connections among ideas at multiple levels of analysis, developing more and more nuanced criteria for evaluating ideas, and formulating an increasingly linked set of views about any phenomenon” ( Linn, 2006 , p. 243). KI lies at the heart of the curricular design for both concepts and skills taught in the classroom in this study, as well as the design of the research tools for the study itself.

A number of KI rubrics and scoring guides have been developed to measure the extent to which students connect ideas important for understanding key concepts in different content areas ( Linn et al. , 2006 ; Liu et al. , 2008 ). For example, Ryoo and Linn (2012) designed a rubric that captures how middle school students integrate their ideas about how light energy is transformed into chemical energy during photosynthesis. In this study, the KI framework is applied to issues of how students integrate their ideas about skills important for scientific inquiry. In particular, this framework goes beyond considering inquiry as an accumulation of compartmentalized ideas. Rather than examining discrete steps in the process of experimentation, such as analyzing data or reaching conclusions ( Casotti et al. , 2008 ), the KI construct described below allows for the integration of student ideas about different aspects of experimental work, such as how experimental design is connected to interpretation of data, accounting for the complex ways in which separate skills important for experimentation are interconnected.

The research question for this study was: How does the use of Science Skills instructional and assessment tools that encourage students to identify and explain the skills they are using in laboratory activities improve KI of student ideas about scientific inquiry and experimentation? A successful model for combining inquiry-based instruction with assessment tools for measuring student understanding of concepts related to scientific experimentation in a high school biology class is presented. While this study is set in a high school context, an argument is provided for how it could translate into introductory life sciences courses at the undergraduate level.

School Site and Participants

There has been an emphasis in recent years on creating “small schools” within large comprehensive schools that provide a more personalized education for students; build relationships among students, teachers, and parents; give teachers additional opportunities to collaborate; and focus on specific themes, such as health or the arts ( Feldman, 2010 ). Student participants were enrolled in a small school for visual and performing arts students within a large urban high school of more than 3000 students. The total enrollment for this small school was approximately 200 students distributed throughout grades 9 through 12. Arts, humanities, and science teachers collaborated to design an integrated science curriculum in which students learned cell biology, genetics, evolution, and ecology in a ninth-grade biology course, and applied this learning to an in-depth study of human anatomy and physiology, particularly those topics most relevant for visual and performing artists, in a 10th-grade human anatomy and physiology course. Participants in this study included all 10th-grade students for one academic year in two class periods, referred to here as groups 1 and 2.

Students in each of the two class periods for this research study represent a typical group of performing and visual arts high school students. As with any two class periods at most high schools, the two classes for this study differed from one another in some ways. De facto tracking existed in terms of students’ interests in specific performing arts, as performing and visual arts classes were scheduled to alternate with the science classes. Students were given a choice about which arts classes they could take; students who preferred drama ended up in group 1, and those who preferred dance ended up in group 2. Additionally, because there were fewer male dancers than female dancers, group 2 was predominantly female (89% compared with 53% for group 1). Students who identified as visual artists were in both groups 1 and 2. It is not clear whether the differences between the two groups may have had any effect on academic performance in a life sciences class, as the overall academic performance as reflected in the grades awarded to assignments in this class was roughly similar between the two groups, falling between 75% and 82% each semester. Student demographic data for this small school were roughly similar to those of the total student body at the high school for the academic year studied (see Supplemental Table S1; Education Data Partnership, 2010 ).

Permission to do this research was sought and obtained through the local school district; parents received a letter describing the teacher's plans for the research and had the option to give consent for their student's work to be published.

Course Curricula

The students who participated in this research were in two class periods of 10th-grade human anatomy and physiology. The yearlong curriculum was organized by body system, as are many courses in human anatomy and physiology, with the systems brought together under different “big ideas,” such as “The brain serves to control and organize all body functions” and “Structure determines function.” While there was a strong emphasis on hands-on experience in the course, a variety of instructional approaches were used in these classrooms; these included lecture, group discussions, collaborative research projects, laboratory experiments, and inquiry-driven computer-based curricula. Throughout the course, the instructor made it clear to students that she particularly valued student-driven questions, experimentation, and the excitement of discovery. Key teaching strategies included: guiding students to provide a rationale for their predictions and hypotheses for experiments; leaving data organization and analysis open-ended; discussing as a class the pros and cons of experimental choices made by different student groups; combining class data to expand sample size; grading lab reports on the quality of evidence-based arguments rather than experimental outcomes; highlighting modeling whenever there was an opportunity; having students present findings directly to other classmates, including in scientific conference-style poster sessions; and providing structure for frequent class discussions and scientific discourse between peers in which there was an expectation of challenging and defending ideas. The textbook ( Marieb, 2006 ) was supplemented with curricula designed, collected, and/or modified by the author (e.g., from Kalamuck et al. , 2000 ; National Institutes of Health [NIH], 2000 ; WISE, 2012 ; Tate et al. , 2008 ). Students in the course typically engaged in experimental work for approximately 40% of the instructional time each week, with a specific focus on different aspects of the scientific research process and introduction to associated specialized vocabulary on an ongoing basis. A key learning objective for this curriculum is for students to increase awareness of who they are as scientists and develop a more specific vocabulary for discussing experimentation and their strengths in science.

Science Skills Instruments

With the intent of making a number of scientific-thinking and problem-solving skills explicit for students, a list of “A to Z Science Skills” was developed (such as A nalyzing, B uilding in controls, G raphing, H ypothesizing; Figure 1 ). Not only was this list posted on bulletin boards throughout the classroom, but every student also had quick access to a copy in his or her class binder. The instructor referred to the list whenever a term or method was introduced or required special emphasis in a class activity. It was made clear that this list was not all-inclusive; indeed, the class would often focus on a skill not on the list. Thus, this tool can be used to focus on different terms or parts of the scientific process, depending on the lab or activity for the day.

Figure 1. A to Z Science Skills. Note that this list is, of course, not all-inclusive and can be modified to fit instruction for other disciplines and at other levels from elementary through graduate education. A to Z Science Skills was adapted from Math Alphabilities (P. Tucher, personal communication).

Students’ experimental work was formatively assessed with a postlab reflection, which asked students to use the A to Z Science Skills list to “Pick 3 skills that you think you did well during lab, and describe how you demonstrated this skill.” This simple assignment was given approximately one to two times per month to assess 1) how students understood the importance of these terms and methods in relation to their own work doing science investigations, and 2) how they viewed their own development in using such skills. Generally, students completed this self-assessment at the end of a lab exercise as they wrote up their conclusions or after a series of experiments to reflect on their work during the previous week or two, which had the added benefit of promoting metacognition for their science learning. Conclusions for lab work were structured and always followed a similar pattern; students were asked to report their findings, use their data as evidence to support their claims, discuss sources of error, and identify a next experiment that would extend their work. As students completed their conclusions and the postlab reflection, they were encouraged to talk to one another about skills they had used that week (which were frequently different from those employed by their peers), giving the instructor the opportunity to wander around the classroom, checking to make sure their self-assessment matched her own understanding of what they had accomplished.

Group Collaboration Instruments

One skill that is key to successful scientific research is collaboration, which is specifically named in the A to Z Science Skills list. Scientific collaboration was promoted in the classroom on a regular basis with the use of a Group Collaboration rubric (Supplemental Figure S1) and the corresponding reflection, designed to measure successful collaboration skills, such as sharing ideas, distributing work, using time efficiently, and decision making. The goal for using the Group Collaboration tools was to improve awareness of what it takes to collaborate successfully and to help students learn to value group work more as an effective way to accomplish a major project. The Group Collaboration rubric outlined expectations for student group work and was introduced to students with the first major group project. Specifically, it measured collaboration skills in five categories described in a way that is accessible to high school students: Contributing & Listening to Ideas, Sharing in Work Equally, Using Time Efficiently, Making Decisions, and Discussing Science (Supplemental Figure S1). Over the course of the academic year, there were four times that groups worked together on an extensive project that served to review, connect, and apply concepts from a particular unit. These projects generally corresponded with the end of each quarter and lasted 1–2 wk each. After the projects were complete, each student was instructed “Use the rubric to pick the level that you feel your group reached in its collaboration for each category, and list one or more specific examples in the evidence column for how you reached that level” on an individual Group Collaboration reflection, which was a self-assessment of his or her group's performance. The Group Collaboration tools were also used intermittently for smaller group projects. It was made clear to the students that they would not be evaluated negatively if they identified areas needing improvement, but instead would be evaluated on their ability to provide evidence for their choices and to explain how they would improve on these areas in the future. As with the Science Skills tools, outlining different categories important for collaboration in the Group Collaboration rubric allowed an explicit focus on a specific aspect of good group work depending on the day or project.

Science Skills Assessment and KI Rubric

The extent to which student understanding of science skills and scientific collaboration changed over the course of the year was measured with a simple assessment, the Science Skills assessment (Supplemental Figure S2). Students were told that the assessment was for the instructor's own use to improve the course and would solicit their feedback in different ways about what helped them learn in the class. The assessment was given to students as a pre-, mid- and postassessment during approximately the first few weeks of the first semester, the first week of the second semester, and the last week of the academic year, respectively, and took less than 15 min to administer and complete each time.

A KI framework was used to develop a rubric for scoring open-ended responses to question 2 (“Name three skills that you think are important for doing science well, and explain why you picked them”) and question 5 (“What are your strengths in doing science?”). The Science Skills Knowledge Integration (SSKI) rubric was developed for this study to measure the extent to which students made links between specific skills and their importance for science ( Figure 2 includes examples of student responses). The SSKI rubric, a five-level latent construct aligned with other KI rubrics ( Linn et al. , 2006 ; Liu et al ., 2008 ), maps onto students’ increasingly sophisticated understanding of the research process. All scoring levels were represented in student responses. Interrater reliability for the SSKI rubric was greater than 95% for both questions 2 and 5, with two raters, with a high agreement indicated by a Cohen's kappa of 0.976.

Figure 2. SSKI rubric, with student examples. Examples are responses to the question “Name three skills that you think are important for doing science well, and explain why you picked them.”

For assessing whether students had more awareness of what it takes to collaborate successfully, at midsemester and at the end of the year, an attempt was made to create a KI rubric for collaboration to analyze responses to question 4 on the Science Skills assessment. Unfortunately, the open-ended responses proved difficult to code and categorize, limiting further information about student understanding of scientific collaboration from this study.

While some of the items on the Science Skills assessment are self-assessments, the SSKI rubric does not measure how much the students perceive they learned about science skills nor their skill level, but instead measures the ability of the student to identify specific skills important for scientific experimentation and the extent to which they are able to connect different ideas about the scientific research process. See Table 1   for a complete list of the different instruments used and the purpose intended for each.

a Science Skills tools.

b Group Collaboration tools.

Study Design

The study was designed to simultaneously test two treatments, Science Skills or Group Collaboration, with two different groups of students and each group serving as a “no-treatment” comparison for other ( Figure 3 ). This experimental design can be applied to any classroom situation in which the student population can be divided into two groups for two independent interventions. In this case, the groups represented two different periods of students taking the same course, a typical teaching assignment for high school science teachers. During the first half of the year, group 2 was instructed to use the Science Skills assessment tools (A to Z Science Skills and postlab reflection, Table 1 ); at the same time, group 1 was instructed to use the Group Collaboration assessment tools (Group Collaboration rubric and reflection, Table 1 ). Thus, group 1 served as a no-treatment comparison for the group 2 Science Skills treatment, and group 2 served as a no-treatment comparison for the group 1 scientific collaboration treatment. Throughout the second half of the year, both groups were encouraged to develop scientific thinking and collaboration by using both types of assessment tools and therefore received instruction in both areas by the end of the course. KI gains were measured by scoring the Science Skills assessment responses to question 2 using the SSKI rubric (see above and Figure 2 ).

Figure 3. The study design allows for reciprocal testing of two simultaneous treatments.

The work presented here began when the author participated in Project IMPACT, a university program that provides a professional learning community structure to take on action research with regular feedback from teacher colleagues ( Curry, 2008 ). Teachers involved in this professional learning community worked on individual research projects connected by the general theme of how to promote social justice in a science or mathematics classroom. Teachers worked in groups of five to six with guidance from one another and a trained facilitator, posing a research question, designing a study, and testing changes they implemented in their classrooms. The Science Skills and Group Collaboration tools were piloted by the author with 120–140 high school life sciences students over the 2-yr period prior to the academic year in which this study took place.

Statistical Methods

Statistical analysis was done using Stata Data Analysis and Statistical Software (2012) and the R Software Environment (2013) , as indicated in relevant figure legends and associated text (see Results ).

Snapshots of Inquiry in the Classroom Using the Science Skills Approach

Many science teachers in K–12 urban schools struggle to create a learning environment that contains the essential features of classroom inquiry. This includes a learning environment in which students engage in scientifically oriented questions, formulate explanations from evidence, and communicate and justify their proposed explanations ( NRC, 2000 ; NGSS, 2013 ). Secondary life sciences teachers also struggle to unify inquiry-based lessons that span the entire curriculum in a cohesive manner. To meet the goal of promoting student inquiry, a new approach was designed and tested in the author's classroom, called “Science Skills,” a term that was accessible for high school students.

When we did the caffeine lab I observed my heart rate and pulse every two minutes and made notes on it. Observing makes it easier for me to understand [the effects of caffeine].

I think observing is a more nature skill everyone has [ sic ]. I personally specialize with that skill because I’m always check[ing] new things and experiments out whenever something seems interesting.

For example, when I had to dissect a cow's eye. I had to first examine and observe its outside.

While this student struggled to express himself articulately, this practice helped him advance his understanding of what approaches are important in scientific experiments. All of the students, in both groups 1 and 2, engaged in the same inquiry lessons at the same point in the curriculum. For example, in the case of the caffeine experiment, all students were instructed similarly to choose controls to address different variables, record data in tables, and analyze data with graphs. The two groups only differed in their use of additional tools that supplemented each inquiry lesson (see the following section).

Evidence of Gains in SSKI

For determining whether the Science Skills tools designed for this study were effective in promoting student inquiry and scientific thinking, student responses were evaluated for the pre-, mid-, and postassessment question 2 on the Science Skills assessment (see Methods ). Two different groups of students participated in the study: group 2 was instructed to use the Science Skills tools in the first half of the year; during this same instructional time, group 1 was being taught to use the Group Collaboration tools ( Figure 3 ). The learning goals, sequence of content taught, group projects, experiments, and activities were otherwise identical in both groups. Thus, group 1 served as a no-treatment comparison for the Science Skills treatment group 2, until the second half of the year, when both groups used both types of instruction and assessment tools ( Table 1 ). Student responses for both groups were analyzed using the SSKI rubric ( Figure 2 ) to measure the extent to which students made links between specific skills and their importance for science.

Results of this analysis revealed that students who used the Science Skills tools are better able to name and explain scientific-thinking strategies than their peers who did not use the same tools ( Table 2 ). The average scores for responses from both groups started at a similar level, as a two-sample t test revealed there was no significant difference ( p = 0.55). When the average pre- and midassessment KI scores for each student were compared, the change in scores for the no-treatment comparison group 1 was not statistically significant, as revealed by a one-sided paired t test ( p = 0.88). However, for group 2, which received the Science Skills treatment, each student's score improved on average by 0.44 at midsemester, demonstrating a statistically significant difference in KI ( p = 0.021). By the end of the school year, after both groups of students had received the Science Skills treatment, the average improvement on the assessment for group 1 was 1.1, and for group 2, it was 0.67. The average improvement in KI for each group pre- to postassessment is highly statistically significant ( p values were 0.00053 and 0.0048, respectively).

a This analysis was done on student responses to question 2 on the Science Skills assessment (“Name three skills that you think are important for doing science well, and explain why you picked them.”). A one-sided paired t test with 18 (or 17) degrees of freedom was performed for group 1 and group 2 changes in average KI scores, respectively. The null hypothesis was that there was no change and the average difference from pre- to midassessment or from pre- to postassessment was 0.

Effect size, which helps determine the extent to which statistically significant changes are likely to be meaningful, was also calculated ( Cohen, 1992 ). Consistently, Cohen's d measurements revealed that the Science Skills treatment for group 2 had a modest effect size midyear ( d = 0.57). By the end of the year, when both groups had received the treatment, an even larger effect size was seen ( d = 1.0 and d = 0.70, respectively; see Table 2B ). Similar patterns were seen from analysis of question 5 responses, in which students clearly identified science skills when describing their own particular strengths in science, confirming the results obtained with question 2 (unpublished data).

Further analysis of student responses for the Science Skills assessment was done to gain additional insight on the distribution of the student scores for pre-, mid-, and postassessment of these two groups ( Figure 4 ). A box plot reveals that the distribution was similar for both groups at the beginning of the year and for group 1 at midyear, before receiving the Science Skills treatment. However, the distribution shifted higher for group 2 midyear and for both groups at the end of the year, after both received the Science Skills treatment, confirming that students show gains in KI only after using these instruction and assessment tools.

Figure 4. Box plots reveal differences in the distribution of KI scores after students use Science Skills instruction and assessment tools. Group 1 (white boxes) used the Science Skills tools only after they were assessed midyear ( n = 19); group 2 (gray boxes) used Science Skills tools from the beginning of the course, were assessed at midyear, and continued use of the tools through the end of the year ( n = 18). Note that the dark lines represent the median; the boxes include 50% of the data, representing the 1st to the 3rd quartile; 90% of the data is within the whiskers; and open circles represent outliers. Figure 4 shows the distribution of the same data set as is analyzed in Table 2 and Figure 5 .

Not only did students show increases in average KI scores for treatment groups, but comparison of individual students’ scores for different assessments using scatter plots ( Figure 5 ) revealed that most students showed an increase from a lower KI score to a higher score after they had used the Science Skills instructional and assessment tools. A comparison of pre- and midassessment scores ( Figure 5A ) shows that most students in the no-treatment group 1 tended to score the same or worse, whereas most students in the Science Skills treatment group 2 had immediately increased their understanding of science skills at the midyear point. In comparing pre- and postassessment scores, Figure 5B shows that students in both groups had improved KI scores at the end of the year. Thus, there is a clear relationship between increased student understanding of science skills and the use of the Science Skills tools.

Figure 5. Scatter plots reveal increases in individual student's KI scores after using Science Skills instruction and assessment tools. (A) Comparison of pre- and midassessment scores for each student. (B) Comparison of pre- and postassessment scores for each student. Data points were jittered in the R Software Environment so that all were visible. A red line with a slope of 1 indicates no improvement; points above the line indicate increased improvement; points below the line indicate decreased improvement. Note that the data set is the same as that analyzed in Table 2 and Figure 4 .

Comparing Science Skills and Group Collaboration Treatments

Among many skills important for successful inquiry is group collaboration because the outcome of scientific projects and experiments depends on how well groups in classrooms or research laboratories function. In addition to helping students see collaboration as an important skill for scientific research, a goal for the introduction of Group Collaboration tools was for students to learn to appreciate collaborative group work more as an effective way to accomplish a significant science project. For assessing whether this goal was met, the five-level Likert-scale responses to the simple query in question 3 on the Science Skills assessment were analyzed by comparing the average change in KI scores for each student. For group 1, who had received the Group Collaboration treatment from the beginning of the year, an average of only 21% of the students had immediately increased their appreciation of group work at the midyear point; by the end of the year, 37% of the students on average increased their appreciation of group work as they continued to use the tools (as seen by comparing pre- and midassessments, and pre- and postassessments, respectively). For the no-treatment group 2, a surprising average of 33% of the students appreciated group work more when midyear responses were compared with those from the beginning of the year, but by the end of the year, this number had on average regressed back to the preassessment average (i.e., 0% of the students showed an increased appreciation of group work, the reason for which cannot be explained at this time). Thus evaluation of pre-, mid-, and postassessment of the scientific collaboration treatment did not reveal any relationship between the Group Collaboration treatment and gains made in the appreciation students had for group work.

Analysis of Group Collaboration Reflection Responses

Another goal for promoting scientific collaboration with the use of Group Collaboration tools was to improve awareness of what it takes to collaborate successfully, including that successful collaboration means sharing ideas, distributing work, using time efficiently, and decision making. Responses to the Group Collaboration reflection revealed that students’ self-assessment was both reflective and honest (see Table 3 for illustrative examples from each of these categories). The accuracy of their responses matched that of the teacher's own assessment, which frequently aligned well with students’ self-assessments. For example, Student E discussed a common issue for group work, referring to the group needing to pace itself in order to better meet the project deadline. Additionally, different students on the same team often responded similarly, even though they had completed their reflections independently. Almost every student discussed some things they did well, at the exemplary level, and some things they could improve on, at the developing or beginner level. Interestingly, different groups of students answered differently, emphasizing that the categories outlined on the rubric are each important for collaborative work in a science class. Moreover, each assessment revealed that only about half of the students indicated they had reached what they considered to be the exemplary level on any of the five categories.

a Note that misspelled words were corrected for clarity.

New approaches for teaching and assessing scientific skills and practices are critical for producing scientifically literate citizens ( NGSS, 2013 ). This work shows that student understanding of scientific inquiry can be significantly increased by using instruction and assessment tools aimed at promoting development of specific inquiry skills. The success of the Science Skills approach can be attributed to being explicit with students about what skills are particularly important for progress in science, introducing specific terminology for experimentation, encouraging student self-assessment, and assessing scientific thinking in addition to content. Such strategies also place an emphasis on developing the academic language necessary for communicating in science and improving literacy ( Snow, 2010 ).

Overall the analysis of responses to the pre-, mid-, and postassessments revealed that students expressed an increasing awareness of who they are as scientists and developed a more specific vocabulary for discussing experimentation and their strengths in science compared with students who had not used the same tools, meeting one of the key learning outcome goals for this classroom. Moreover, this new assessment approach enabled the teacher to work individually with struggling students to help them master critical skills; it was these students who often showed the greatest gains in learning how to talk about their experimental work (unpublished data). Not only do such assessments evaluate the extent to which students understand experimental skills, but they also serve as a tool for learning the skills and vocabulary themselves; assessments that accomplish both goals simultaneously have been dubbed “learning tests” by Linn and Chiu (2011) .

While clear learning gains were made for KI of scientific inquiry skills, the tools designed to promote a better understanding of group collaboration were not as successful. Several interpretations could account for why the collaboration treatment was not as effective, including: 1) in contrast to science skills, group work is something with which students already have a lot of experience, as well as the vocabulary for describing strengths and limitations of good collaboration; 2) the maturity level of high school students makes the social interactions required for negotiating tasks like sharing work and making decisions difficult; 3) the measure was not optimal, for example, the pre–post questions did not fully elicit what students understood about scientific collaboration; and 4) implementation of the Group Collaboration rubric was not ideal. Plans for improving the measure and its implementation in the future include probing student understanding of group work with other questions, guiding students to be more specific in their responses, and performing more formative assessment and collecting suggestions for improvement from the students during the project group work. Despite limited success with these measures, from the teacher's perspective, the author found that listening to students discuss the Group Collaboration rubric and reading student responses for the Group Collaboration reflection were useful for understanding class patterns of what was working and not working for the students during collaborative work, as well as for uncovering problems with group dynamics for particular student teams. Although classroom group work is known to be difficult to implement effectively ( Cohen et al. , 1999 ), it is also an important component of successful teaching and is thus worthy of further investigation.

Action Research on Classroom Practices

When introducing a new teaching strategy, it is often difficult to determine its impact in isolation from other instructional approaches used in the classroom. While many instructors are interested in testing new teaching approaches in their own classrooms, questions of ethics quickly arise when one considers exposing some students to new strategies designed to improve learning, while a control group may not benefit from those same experimental strategies. Moreover, randomized field trials, the current gold standard for educational research, are impractical for the typical K–12 or college classroom instructor. Nonetheless, there is a need for improvement of scientific approaches to science education ( Wieman, 2007 ; Asai, 2011 ). Teacher research, also known as teacher inquiry or action research, is an intentional and systematic approach to educational research in which data are collected and analyzed by individual teachers in their own classrooms to improve their teaching practices ( Cochran-Smith and Lytle, 1993 ). This teacher-driven action research project served to improve the author's own teaching practice and is an example for other instructors on how to manage effective educational research while teaching high school, undergraduate, or graduate classes. Not only did these findings provide evidence for the educational benefits of the Science Skills approach to promoting scientific inquiry, but research in the context of the author's own classroom also allowed her to question the Group Collaboration approach and plan next steps for making it more effective. Being involved in an action research group can be a valuable professional development opportunity for any instructor, as it provides an opportunity to reflect on teaching strategies, engage in data analysis of student work, learn from colleagues, and consequently improve teaching practice.

Applications for Undergraduate Life Sciences Education

While this study is set in a high school context, lessons learned can easily transfer to introductory life sciences courses at the undergraduate level. The experimental design shown in Figure 3 allows the instructor to simultaneously test two treatments with two different groups of students, with each group serving as a no-treatment comparison for the other. This experimental design can be applied to any classroom situation in which the student population can be divided into two groups for two independent interventions. Examples of other contexts for which this design could be useful are parallel discussion or laboratory sections for the same undergraduate course or a large lecture format that can be divided into two groups to test the impact of implementing two independent teaching strategies. The KI perspective described here is a promising framework with which to evaluate the effectiveness of both K–12 and undergraduate student learning in life sciences education.

ACKNOWLEDGMENTS

I thank Marcia Linn and her research group; Marnie Curry, Jessica Quindel, and other teacher colleagues with Project IMPACT; and Nicci Nunes, Heeju Jang, Michelle Sinapuelas, Jack Kamm, Deborah Nolan, and others who have inspired the work, encouraged me to write an article describing the study, and generously given me feedback.

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Submitted: 18 November 2012 Revised: 24 October 2013 Accepted: 28 October 2013

© 2014 E. M. Stone. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Inquiry and the National Science Education Standards: A Guide for Teaching and Learning (2000)

Chapter: 2 inquiry in the national science education standards.

2 Inquiry in the National Science Education Standards

When educators see or hear the word “inquiry,” many think of a particular way of teaching and learning science. Although this is one important application for the word, inquiry in the Standards is far more fundamental. It encompasses not only an ability to engage in inquiry but an understanding of inquiry and of how inquiry results in scientific knowledge.

Because of the importance of inquiry, the content standards describing what all students need to know and be able to do include standards on science as inquiry. These inquiry standards specify the abilities students need in order to inquire and the knowledge that will help them understand inquiry as the way that knowledge is produced. In this way, the Standards seek to build student understanding of how we know what we know and what evidence supports what we know.

The abilities and understanding of inquiry are neither developed nor used in a vacuum. Inquiry is intimately connected to scientific questions — students must inquire using what they already know and the inquiry process must add to their knowledge. The geologist investigating the cause of the dead cedar forests along the Pacific Coast used his scientific knowledge and inquiry abilities to develop an explanation for the phenomenon. Mrs. Graham’s fifth grade students used their observations and the information they gathered about plants to recognize the factors affecting the growth of trees in their schoolyard and to solve the “three-tree problem.” For both scientist and students, inquiry and subject matter were integral to the activity. Their scientific knowledge deepened as they developed new understandings through observing and manipulating conditions in the natural world.

What is inquiry in education? The Standards note:

Inquiry is a multifaceted activity that involves making observations;

posing questions; examining books and other sources of information to see what is already known; planning investigations; reviewing what is already known in light of experimental evidence; using tools to gather, analyze, and interpret data; proposing answers, explanations, and predictions; and communicating the results. Inquiry requires identification of assumptions, use of critical and logical thinking, and consideration of alternative explanations. (p. 23)

Developing the ability to understand and engage in this kind of activity requires direct experience and continued practice with the processes of inquiry. Students do not come to understand inquiry simply by learning words such as “hypothesis” and “inference” or by memorizing procedures such as “the steps of the scientific method.” They must experience inquiry directly to gain a deep understanding of its characteristics.

Yet experience in itself is not sufficient. Experience and understanding must go together. Teachers need to introduce students to the fundamental elements of inquiry. They must also assist students to reflect on the characteristics of the processes in which they are engaged.

This chapter addresses the several perspectives on inquiry included in the National Science Education Standards. It first provides some historical background to place the role of inquiry in context. It then gives the actual content standards on Science as Inquiry: what should students know and be able to do? A description of a set of elements or features essential to inquiry-oriented teaching and learning sets the stage for a discussion of instructional models that can help teachers structure activities to foster student inquiry. Finally, several myths that misrepresent inquiry in school science programs are described and debunked.

INQUIRY IN SCHOOL SCIENCE: HISTORICAL PERSPECTIVES

Inquiry has had a role in school science programs for less than a century (Bybee and DeBoer, 1993; DeBoer, 1991). Before 1900, most educators viewed science primarily as a body of knowledge that students were to learn through direct instruction. One criticism of this perspective came in 1909, when John Dewey, in an address to the American Association for the Advancement of Science, contended that science teaching gave too much emphasis to the accumulation of information and not enough to science as a way of thinking and an attitude of mind. Science is more than a body of knowledge to be learned, Dewey said; there is a process or method to learn as well (Dewey, 1910).

By the 1950s and 1960s, the rationale for inquiry as an approach to

School classroom 1906

teaching science was becoming increasingly evident. If students were to learn the methods of science, then how better to learn than through active engagement in the process of inquiry itself? The educator Joseph Schwab (1960, 1966) was an influential voice in establishing this view of science education. Schwab argued that science should be viewed as conceptual structures that were revised as the result of new evidence. For example, the geologist described in the previous chapter followed this approach in developing an explanation for the widespread death of trees. Science teaching and learning should reflect this perspective on science, Schwab said.

The implications of Schwab’s ideas were, for their time, profound. His view suggested that teachers should present science as inquiry and that students should use inquiry to learn science subject matter. To achieve these changes, Schwab (1960) recommended that science teachers look first to the laboratory and use these experiences to lead rather than follow the classroom phase of science teaching. That is, students should work in the laboratory before being introduced to the formal explanation of scientific concepts and principles. Evidence should build to explanations and the refinement of explanations.

Schwab also suggested that science teachers consider three possible approaches in their laboratories. First, laboratory manuals or textbook materials could be used to pose questions and describe methods to investigate the questions, thus allowing students to discover relationships they do not already know. Second, instructional materials could be used to pose questions, but the methods

School classroom 1950

and answers could be left open for students to determine on their own. Third, in the most open approach, students could confront phenomena without textbook- or laboratory-based questions. Students could ask questions, gather evidence, and propose scientific explanations based on their own investigations.

Schwab proposed an additional approach, which he referred to as an “enquiry into enquiry.” (Schwab chose to use this variation of the spelling of the word.) In this approach, teachers provide students with readings and reports about scientific research. They discuss the details of the research: the problems, data, role of technology, interpretations of data, and conclusions reached by the scientists. Where possible, students read about alternative explanations, different and perhaps conflicting experiments, debates about assumptions underlying the research and the use of evidence, and other issues of scientific inquiry. Through this approach, students build an understanding of what constitutes scientific knowledge and how scientific knowledge is produced.

The work of Schwab, Dewey, and others, including Bruner and Piaget in the 1950s and 1960s, influenced the nature of curriculum materials developed in those decades and into the early 1970s. Russia’s launch of the Sputnik satellite in 1957 further spurred the development of these materials, many of which were supported by the National Science Foundation and other federal agencies and private foundations. Underlying many of these instructional materials was the commitment to involve students in doing rather than being told or only reading about science. This reform placed as much, if not more, emphasis on learning the processes of science as on mastering the subject matter of science alone. Teaching models were

Space flight July 19, 1946

based on theories of learning that emphasized the central role of students’ own ideas and concrete experiences in creating new and deepened understandings of scientific concepts.

Throughout the country, use, or at least awareness, of these new curriculum materials prompted educators to provide students with more laboratory and other “hands-on” experiences, more opportunities to pursue their own questions, and more focus on understanding larger scientific concepts rather than disconnected facts. Although the effective use of these new materials was not as widespread as anticipated (Weiss, 1978; Harms and Kahl, 1980; Harms and Yager, 1981), this new view of school science did prompt more study and careful thinking about major issues in science education. Furthermore, and of special significance to this volume, the changes of the 1950s, 1960s, and 1970s widely disseminated the idea of helping students to develop the skills of inquiry and an understanding of science as inquiry.

INQUIRY IN THE NATIONAL SCIENCE EDUCATION STANDARDS

The developers of the National Science Education Standards (National Research Council, 1996) had this historical perspective on which to base their work. Studies of teaching and learning in science classrooms had led to two observations. First, most teachers were still using traditional, didactic methods (Stake and Easley, 1978; Harms and Yager, 1981; Weiss, 1987). Examination of science classrooms revealed that many students were mastering disconnected facts in lieu of broader understandings, critical reasoning, and problem-solving skills. Some teachers, however, were using the new curriculum materials, such as those from the Biological Sciences Curriculum Study (BSCS), Science Curriculum Improvement Study (SCIS), Elementary Science Study (ESS), Intermediate Science Curriculum Study (ISCS), and Physical Sciences Study Committee (PSSC). Their students were spending large amounts of time in inquiry-based

activities. They were making observations, manipulating materials, and conducting laboratory investigations. As a result, they were developing cognitive abilities, such as critical thinking and reasoning, as well as learning science content (Bredderman, 1982; Shymansky et al., 1983).

Those developing national standards were committed to including inquiry as both science content and as a way to learn science. Therefore, rather than simply extolling the virtues of “hands-on” or “laboratory-based” teaching as the way to teach “science content and process,” the writers of the Standards treated inquiry as both a learning goal and as a teaching method. The concept of inquiry thus appears in several different places in the Standards .

INQUIRY IN THE CONTENT STANDARDS

The content standards for Science as Inquiry include both abilities and understandings of inquiry (Tables 2-1 , 2-2 and 2-3 ). The general standards for inquiry ( Table 2-1 ) are the same for all three grade spans (K-4, 5-8, 9-12). The more detailed fundamental abilities of inquiry and fundamental understandings about inquiry increase in complexity from kindergarten through grade 12, reflecting the cognitive development of students (Tables 2-2 and 2-3 ).

Table 2-1 . Content Standard for Science as Inquiry

Abilities Necessary to Do Scientific Inquiry

Table 2-2 presents the key abilities from the inquiry standards. These “cognitive abilities” go beyond what have been termed science “process” skills, such as observation, inference, and experimentation (Millar and Driver, 1987). Inquiry abilities require students to mesh these processes with scientific knowledge as they use scientific reasoning and critical thinking to develop their understanding of science.

The basis for moving away from the traditional process approach is to encourage students to participate in the evaluation of scientific knowledge. At each of the steps involved in inquiry, students and teachers ought to ask “what counts?” What data do we keep? What data do we discard? What patterns exist in the data? Are these patterns appropriate for this inquiry? What explanations account for the patterns? Is one explanation better than another?

In justifying their decisions, stu-

Table 2-2 . Content Standard for Science as Inquiry: Fundamental Abilities Necessary to Do Scientific Inquiry

dents ought to draw on evidence and analytical tools to derive a scientific claim. In turn, students should be able to assess both the strengths and weaknesses of their claims. The development and evolution of knowledge claims, and reflection upon those claims, underlie the inquiry abilities presented in Table 2-2 .

Note that the abilities from one grade level to the next are very similar but become more complex as the grade level increases. For example, K-4 students “use data to construct a reasonable explanation,” while 5-8 students “recognize and analyze alternative explanations and procedures,” and 9-12 students analyze “alternative models” as well. The abilities are designed to be developmentally appropriate to the grade level span.

Table 2-3 . Content Standard for Science as Inquiry: Fundamental Understandings About Scientific Inquiry

Appendix A-1 , which is taken directly from the Standards , provides more elaboration for these abilities for each grade span.

Understandings About Scientific Inquiry

Table 2-3 presents the fundamental understandings about the nature of scientific inquiry from the Standards . Although in some cases these “understandings” appear parallel to the “abilities” displayed in Table 2-2 , they actually represent much more. Understandings of scientific inquiry represent how and why scientific knowledge changes in response to new evidence, logical analysis, and modified explanations debated within a community of scientists. The work of the geologist described in Chapter 1 , for example, was guided by his initial question and the evidence-to-explanation nature of scientific inquiry.

As with the abilities of inquiry, the understandings of inquiry are very similar from one grade to the next but increase in complexity. For example, K-4 students understand that “scientists develop explanations using observations (evidence) along with what they already know about the world (scientific knowledge),” while students in grades 5-8 know that “scientific explanations emphasize evidence, have logically consistent arguments, and use scientific principles, models, and theories.” Students in grades 9-12 understand that scientific explanations must abide by the rules of evidence, be open to possible modifications, and satisfy other criteria.

Appendix A-2 , taken directly from the Standards , provides more elaboration for these understandings for each grade span.

LEARNING THROUGH INQUIRY AND ITS IMPLICATIONS FOR TEACHING

Having defined inquiry in part as a set of student learning outcomes, the next question becomes: What is teaching through inquiry, and when and how should it be done?

The science teaching standards provide a comprehensive view of science teaching ( Table 2-4 ). These standards apply to the many teaching strategies, including inquiry, that make up an effective teacher’s repertoire. Although the teaching standards refer to inquiry, they are also clear that “inquiry is not the only strategy for teaching science” (p. 23). Nevertheless, inquiry is a central part of the teaching standards. The standards say, for example, that teachers of science “plan an ‘inquiry-based’ science program,” “focus and support inquiries,” and “encourage and model the skills of scientific inquiry.”

Because the teaching standards are so broad, it is helpful for our purposes

Table 2-4 . Science Teaching Standards

to focus more on inquiry in classrooms: to propose a working definition that distinguishes inquiry-based teaching and learning from inquiry in a general sense and from inquiry as practiced by scientists. The following definition is derived in part from the abilities of inquiry, emphasizing questions, evidence, and explanations within a learning context. Inquiry teaching and learning have five essential features that apply across all grade levels (see Table 2-5 ).

Learners are engaged by scientifically oriented questions. Scientifically oriented questions center on objects, organisms, and events in the natural world; they connect to the science concepts described in the content standards. They are questions that lend themselves to empirical investigation, and lead to gathering and using data to develop explanations for scientific phenomena. Scientists recognize two primary kinds of scientific questions (Malley, 1992). Existence questions probe origins and include many “why” questions. Why do objects fall towards the earth? Why do some rocks contain crystals? Why do humans have chambered hearts? Many “why” questions cannot be addressed by science. There are also causal/functional questions, which probe mechanisms and include most of the “how” questions. How does sunlight help plants to grow? How are crystals formed?

Students often ask “why” questions. In the context of school science, many of these questions can be changed into “how” questions and thus lend themselves to scientific inquiry. Such change narrows and sharpens the inquiry and contributes to its being scientific.

In the classroom, a question robust and fruitful enough to drive an inquiry generates a “need to know” in students, stimulating additional questions of “how” and “why” a phenomenon occurs. The initial question may originate from the learner, the teacher, the instructional materials, the Web, some other source, or some combination. The teacher plays a critical role in guiding the identification of questions, particularly when they come from students. Fruitful inquiries evolve from questions that are meaningful and relevant to students, but they also must be able to be answered by students’ observations and scien-

Table 2-5 . Essential Features of Classroom Inquiry

tific knowledge they obtain from reliable sources. The knowledge and procedures students use to answer the questions must be accessible and manageable, as well as appropriate to the students’ developmental level. Skillful teachers help students focus their questions so that they can experience both interesting and productive investigations.

An example of a question that meets these criteria for young students is: how do mealworms respond to light? One for older students is: how do genes influence eye color? An example of an unproductive question for younger students is: why do people behave the way they do? This question is too open, lending itself to responses that may or may not have a scientific basis. It would be difficult to gather evidence supporting such proposed answers as, “it is human nature” or “some supernatural force wills people to behave the way they do.” An example of an unproductive question for older students is: what will the global climate be like in 100 years? This question is scientific, but it is also very complex. It requires an answer that will almost assuredly not consider all the evidence and arguments that would go into a prediction. Students might consider individual factors, for example, how would increasing cloud cover influence climate change? Or they might consider causal relationships, for example, what effect would 5 degrees warmer (or cooler) temperatures have on plants? currents? weather?

Learners give priority to evi dence , which allows them to develop and evaluate explanations that address scientifically oriented questions . As the Standards note, science distinguishes itself from other ways of knowing through use of empirical evidence as the basis for explanations about how the natural world works. Scientists concentrate on getting accurate data from observations of phenomena.

They obtain evidence from observations and measurements taken in natural settings such as oceans, or in contrived settings such as laboratories. They use their senses, instruments such as telescopes to enhance their senses, or instruments that measure characteristics that humans cannot sense, such as magnetic fields. In some instances, scientists can control conditions to obtain their evidence; in other instances they cannot control the conditions or control would distort the phenomena, so they gather data over a wide range of naturally occurring conditions and over a long enough period of time so that they can infer what the influence of different factors might be (AAAS, 1989). The accuracy of the evidence gathered is verified by checking measurements, repeating the observations, or gathering different kinds of data related to the same phenomenon. The evidence is subject to questioning and further investigation.

The above paragraph explains what counts as evidence in science. In their classroom inquiries, students use evidence to develop explanations for scientific phenomena. They observe plants, animal, and rocks, and carefully describe their characteristics. They take measurements of temperature, distances, and time, and carefully record them. They observe chemical reactions and moon phases and chart their progress. Or they obtain evidence from their teacher, instructional materials, the Web, or elsewhere, to “fuel” their inquiries. As the Standards note, “explanations of how the natural world changes based on myths, personal beliefs, religious values, mystical inspiration, superstition, or authority may be personally useful and socially relevant, but they are not scientific” (p. 201).

Learners formulate explanations from evidence to address scientifically oriented questions. Although similar to the previous feature, this aspect of inquiry emphasizes the path from evidence to explanation rather than the criteria for and characteristics of the evidence. Scientific explanations are based on reason. They provide causes for effects and establish relationships based on evidence and logical argument. They must be consistent with experimental and observational evidence about nature. They respect rules of evidence, are open to criticism, and require the use of various cognitive processes generally associated with science — for example, classification, analysis, inference, and prediction, and general processes such as critical reasoning and logic.

Explanations are ways to learn about what is unfamiliar by relating what is observed to what is already known. So, explanations go beyond current knowledge and propose some new understanding. For science, this means building upon the existing knowledge base. For students, this

means building new ideas upon their current understandings. In both cases, the result is proposed new knowledge. For example, students may use observational and other evidence to propose an explanation for the phases of the moon; for why plants die under certain conditions and thrive in others; and for the relationship of diet to health.

Learners evaluate their explanations in light of alternative explanations, particularly those reflecting scientific understanding. Evaluation, and possible elimination or revision of explanations, is one feature that distinguishes scientific from other forms of inquiry and subsequent explanations. One can ask questions such as: Does the evidence support the proposed explanation? Does the explanation adequately answer the questions? Are there any apparent biases or flaws in the reasoning connecting evidence and explanation? Can other reasonable explanations be derived from the evidence?

Alternative explanations may be reviewed as students engage in dialogues, compare results, or check their results with those proposed by the teacher or instructional materials. An essential component of this characteristic is ensuring that students make the connection between their results and scientific knowledge appropriate to their level of development. That is, student explanations should ultimately be consistent with currently accepted scientific knowledge.

Learners communicate and justify their proposed explanations. Scientists communicate their explanations in such a way that their results can be reproduced. This requires clear articulation of the question, procedures, evidence, proposed explanation, and review of alternative explanations. It provides for further skeptical review and the opportunity for other scientists to use the explanation in work on new questions.

Having students share their explanations provides others the opportunity to ask questions, examine evidence, identify faulty reasoning, point out statements that go beyond the evidence, and suggest alternative explanations for the same observations. Sharing explanations can bring into question or fortify the connections students have made among the evidence, existing scientific knowledge, and their proposed explanations. As a result, students can resolve contradictions and solidify an empirically based argument.

Taken as a whole, these essential features introduce students to many important aspects of science while helping them develop a clearer and deeper knowledge of some particular science concepts and processes. The path from formulating scientific questions, to establishing criteria for evidence, to proposing, evaluating,

and then communicating explanations is an important set of experiences for school science programs.

Teaching approaches and instructional materials that make full use of inquiry include all five of these essential features. Each of these essential features can vary, of course. These variations might include the amount of structure a teacher builds into an activity or the extent to which students initiate and design an investigation. For example, every inquiry engages students in scientifically oriented questions. However, in some inquiries students pose the initial question; in others students choose alternatives or

sharpen the initial question; and in others the students are provided the question. Research demonstrates the importance of students’ taking ownership of a task, which argues for engaging students in identifying or sharpening questions for inquiry. But all variations appropriate for the particular learning goal are acceptable, as long as the learning experience centers on scientifically oriented questions that engage students’ thinking.

Sometimes inquiries are labeled as either “full” or “partial.” These labels refer to the proportion of a sequence of learning experiences that is inquiry-based. For example, when a teacher or textbook does not engage students with a question but begins by assigning an experiment, an essential element of inquiry is missing and the inquiry is partial. Likewise, an inquiry is partial if a teacher chooses to demonstrate how something works rather than have students explore it and develop their own questions or explanations. If all five of the essential elements of classroom inquiry are present, the inquiry is said to be full.

Inquiry-based teaching can also vary in the amount of detailed guidance that the teacher provides. Table 2-6 describes variations in the amount of structure, guidance, and coaching the teacher provides for students engaged in inquiry, broken out for each of the five essential features. It could be said that most open form of

inquiry-based teaching and learning occurs when students’ experiences are described by the left-hand column in Table 2-6 . However, students rarely have the abilities to begin here. They first have to learn to ask and evaluate questions that can be investigated, what the difference is between evidence and opinion, how to develop a defensible explanation, and so on. A more structured type of teaching develops students’ abilities to inquire. It helps them learn how to determine what counts. The degree to which teachers structure what students do is sometimes referred to as “guided” versus “open” inquiry. (Note that this distinction has roots in the history recounted earlier in the chapter as Schwab’s three approaches to “labora-

Table 2-6 . Essential Features of Classroom Inquiry and Their Variations

tories” which vary in their degree of structure and guidance by teachers or materials.) Table 2-6 illustrates that inquiry-based learning cannot simply be characterized as one or the other. Instead, the more responsibility learners have for posing and responding to questions, designing investigations, and extracting and communicating their learning, the more “open” the inquiry (that is, the closer to the left column in Table 2-6 ). The more responsibility the teacher takes, the more guided the inquiry (that is, the closer to the right column on Table 2-6 ).

Experiences that vary in “openness” are needed to develop the inquiry abilities in Table 2-2 . Guided inquiry can best focus learning on the development of particular science concepts. More open inquiry will afford the best opportunities for cognitive development and scientific reasoning. Students should have opportunities to participate in all types of inquiries in the course of their science learning.

How does a teacher decide how much guidance to provide in an inquiry? In making this decision, a key element is the intended learning outcomes. Whether the teacher wants students to learn a particular science concept, acquire certain inquiry abilities, or develop understandings about scientific inquiry (or some combination) influences the nature of the inquiry.

Below are examples of learning experiences designed to incorporate some form of inquiry. (Note the emphasis on series of lessons or learning experiences, rather than single lessons, illustrating that inquiries require time to unfold and for

students to learn.) Each example considers not only the learning outcomes and the teaching strategy but the way the teacher will assess whether students have achieved the intended outcome. Assessment is a critical aspect of inquiry because it sharpens and defines the design of learning experiences. When teachers know what they want students to demonstrate, they can better help them learn to do so.

As one example, consider a series of lessons in which the learning outcome is for students to strengthen all the fundamental abilities of inquiry. In Chapter 1 , when Mrs. Graham was presented with an interesting question from her students, she recognized an opportunity for her students to engage in a learning activity where they could complete a full inquiry originating with their question about the trees and culminating in communication of scientific explanations based on evidence. The inquiry incorporated all five essential features, with student engagement described by the left column in Table 2-6 . Through her assistance and coaching, Mrs. Graham helped the students learn how to clarify their questions and identify possible explanations that could be tested by scientific investigations. She helped them learn the importance of examining alternative explanations and comparing them with the evidence gathered. She helped students understand the relationship between

evidence and explanation. As a result, the students not only learned some science subject matter related to the growth of trees, they also developed specific inquiry abilities.

A second example focuses on developing student understandings about scientific inquiry. A high school biology teacher is planning student learning activities for a unit on biological evolution. Several of the classroom investigations and discussions focus on factors leading to adaptation in organisms. Because of the interesting

historical development of these scientific ideas, the teacher decides to take advantage of the opportunity to develop students’ understanding of how scientific inquiry works. The assessment for this learning outcome is for students to be able to describe the place of logic, evidence, criticism, and modification in the account of a scientific discovery. Based on readings about past and current investigations of evolution on the Galapagos Islands (including Darwin’s On the Origin of Species and The Beak of the Finch by Jonathan Weiner), students discuss and answer the following

questions: What led to past and current investigation of the finches on the islands? How have investigations differed, and how have they been similar? Have the scientific explanations derived from these investigations been logically consistent? Based on evidence? Open to skeptical review? Built on a knowledge base of other experiments? Following the readings and discussion of the questions, the teacher would have student groups prepare oral reports on the topic “The Role of Inquiry in Science.”

This learning activity does not contain all of the essential features of classroom inquiry, but many features are present. The activity engages students in scientifically oriented questions. It promotes discussion of the priority of evidence in developing scientific explanations. It connects those explanations to accepted scientific knowledge. And it requires students to communicate their understandings of scientific inquiry to others. This activity thus could be an integral part of a sequence of learning opportunities that in total contains all five essential features of inquiry.

As a final example, consider a series of lessons that seeks to have students develop an understanding of the concept of density. One way to determine the best teaching strategy for this particular outcome would be to think about how students might demonstrate that they understand density. One performance assessment for older elementary students might be to provide them with objects of different densities, a scale, and a water-filled flask with volume markings on the side. Students would then be asked to select objects and, using the scale and flask, determine their densities. Given this assessment, what kinds of inquiry learning experiences would help students understand density well enough to be successful? One teaching strategy would be a series of laboratory activities framed by questions requiring the gathering and use of evidence to develop explanations about mass and volume relationships. Students would connect their explanations to scientific explanations provided by the teacher and their text, so all five essential features of classroom inquiry would be incorporated.

PROVIDING COHERENT INQUIRY-BASED INSTRUCTION — INSTRUCTIONAL MODELS

How can the features of inquiry be combined in a series of coherent learning experiences that help students build new understandings over time? Instructional models offer a particularly useful way for teachers to improve their use of inquiry.

Instructional models originated in observations of how people learn. As early as the turn of the century, Herbart’s (1901) ideas about teaching

included starting with students’ interest in the natural world and in interactions with others. The teacher crafted learning experiences that expanded concepts students already knew and explained others they could not be expected to discover. Students then applied the concepts to new situations. Later, Dewey (1910) built upon the idea of reflective experience in which students began with a perplexing situation, formulated a tentative interpretation or hypothesis, tested the hypothesis to arrive at a solution, and acted upon the solution. Dewey’s prior experience as a science teacher explains the obvious connection between reflective thinking and scientific inquiry (Bybee, 1997).

Piaget’s theory of development contributed much to the elaboration of instructional models (Piaget, 1975; Piaget and Inhelder, 1969). In his view, learning begins when individuals experience disequilibrium: a discrepancy between their ideas and ideas they encounter in their environments (that is, what they think they know and what they observe or experience). To bring their understanding back into equilibrium, they must adapt or change their cognitive structure through interaction with the environment.

Piaget’s work was the basis for the learning cycle, an instructional model, proposed by Atkin and Karplus (1962) and used in the SCIS elementary science curriculum. Although the learning cycle has undergone elaboration and modification over time, its phases and normal sequence are typically represented as exploration, invention, and discovery. Exploration refers to relatively unstructured experiences when students gather new information. Invention refers to the formal statement of a new concept — often a definition — in which students interpret newly acquired information by restructuring their prior concepts. Discovery involves applying the new concept to a novel situation.

Research on how people learn (discussed in detail in Chapter 6 ) suggests a dynamic and interactive view of human learning. Students bring to a learning experience their current explanations, attitudes, and abilities. Through meaningful interactions with their environment, with their teachers, and among themselves, they reorganize, redefine, and replace their initial explanations, attitudes, and abilities. An instructional model incorporates the features of inquiry into a sequence of experiences designed to challenge students’ current conceptions and provide time and opportunities for reconstruction, or learning, to occur (Bybee, 1997).

A number of different instructional models have been developed that can help teachers organize and sequence inquiry-oriented learning experiences for their students. All can incorporate the essential features of inquiry. They

Table 2-7 . Common Components Shared by Instructional Models

seek to engage students in important scientific questions, give students opportunities to explore and create their own explanations, provide scientific explanations and help students connect these to their own ideas, and create opportunities for students to extend, apply, and evaluate what they have learned. Common components or phases that are shared by instructional models are shown in Table 2-7 .

Instructional models have helped teachers and those who support them — in particular, curriculum developers — to design instruction in ways that attend to how learning occurs and afford students opportunities to engage in scientific inquiry. The primary disadvantage of instructional models applies to models in general: by definition, they simplify the world. Teachers and others can be misled into thinking of them as lockstep, prescriptive devices — rather than as general guides for designing instruction that help learning to unfold through inquiry, which must always be adapted to the needs of particular learners, the specific learning goals, and the context for learning.

SOME MYTHS ABOUT INQUIRY-BASED LEARNING AND TEACHING

A number of myths about inquiry-based learning and teaching have at times been wrongly attributed to the National Science Education Standard s. These myths threaten to inhibit progress in science education reform either by characterizing inquiry as too difficult to achieve or by neglecting the essential features of inquiry-based learning. Listed below are responses to five of these mistaken beliefs.

Myth 1: All science subject matter should be taught through inquiry. Teaching science effectively requires a variety of approaches and strategies. It is not possible in practice to teach all science subject matter through inquiry, nor is it desirable to do so. Teaching all of science using only one method would be ineffective, and it would probably become boring for students.

Myth 2: True inquiry occurs only when students generate and pursue their own questions. For students to develop the ability to ask questions, they must “practice” asking questions. But if the desired outcome is learning science subject matter, the source of the question is less important that the nature of the question itself. It is important to note, however, that in today’s science classrooms students rarely have opportunities to ask and pursue their own questions. Students will need some of these opportunities to develop advanced inquiry abilities and to understand how scientific knowledge is pursued.

Myth 3: Inquiry teaching occurs easily through use of hands-on or kit-based instructional materials. These materials can increase the probability that students’ thinking will be focused on the right things and learning will occur in the right sequence. However, the use of even the best materials does not guarantee that students are engaged in rich inquiry, nor that they are learning as intended. A skilled teacher remains the key to effective instruction. He or she must pay careful attention to whether and how the materials incorporate the five essential features of inquiry. Using these five features to review materials as well as to assess classroom practice should enhance the kinds and depth of learning.

Myth 4: Student engagement in hands-on activities guarantees that inquiry teaching and learning are occurring. Although participation by students in activities is desirable, it is not sufficient to guarantee their mental engagement in any of the essential features of inquiry.

Myth 5: Inquiry can be taught without attention to subject matter. Some of the rhetoric of the 1960s was used to promote the idea that learning science processes should be the only meaningful outcome of science education. Today, there are educators who still maintain that if students learn the processes of science, they can learn any content they need by applying these processes. But as stated at the beginning of this chapter, student understanding of inquiry does not, and cannot, develop in isolation from science subject matter. Rather, students start from what they know and inquire into things they do not know. If, in some instances, a

teacher’s desired primary outcome is that students learn to conduct an inquiry, science subject matter serves as a means to that end. Scientific knowledge remains important. The abilities and understandings outlined in the Standards extend beyond the processes of science to engage students in a full complement of thinking and learning science.

This chapter has provided the definitions of inquiry and inquiry-based teaching that undergird the Standards. Chapter 3 will present a series of classroom vignettes that illustrate how elementary, middle, and high school teachers design different kinds of inquiries to achieve different learning outcomes. Chapter 4 will look at assessment: within the context of good instruction, how can the achievement of different learning outcomes best be assessed? Subsequent chapters then turn to how teachers can be prepared and supported to use these strategies in their classrooms.

Humans, especially children, are naturally curious. Yet, people often balk at the thought of learning science—the "eyes glazed over" syndrome. Teachers may find teaching science a major challenge in an era when science ranges from the hardly imaginable quark to the distant, blazing quasar.

Inquiry and the National Science Education Standards is the book that educators have been waiting for—a practical guide to teaching inquiry and teaching through inquiry, as recommended by the National Science Education Standards. This will be an important resource for educators who must help school boards, parents, and teachers understand "why we can't teach the way we used to."

"Inquiry" refers to the diverse ways in which scientists study the natural world and in which students grasp science knowledge and the methods by which that knowledge is produced. This book explains and illustrates how inquiry helps students learn science content, master how to do science, and understand the nature of science.

This book explores the dimensions of teaching and learning science as inquiry for K-12 students across a range of science topics. Detailed examples help clarify when teachers should use the inquiry-based approach and how much structure, guidance, and coaching they should provide.

The book dispels myths that may have discouraged educators from the inquiry-based approach and illuminates the subtle interplay between concepts, processes, and science as it is experienced in the classroom. Inquiry and the National Science Education Standards shows how to bring the standards to life, with features such as classroom vignettes exploring different kinds of inquiries for elementary, middle, and high school and Frequently Asked Questions for teachers, responding to common concerns such as obtaining teaching supplies.

Turning to assessment, the committee discusses why assessment is important, looks at existing schemes and formats, and addresses how to involve students in assessing their own learning achievements. In addition, this book discusses administrative assistance, communication with parents, appropriate teacher evaluation, and other avenues to promoting and supporting this new teaching paradigm.

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Writing Program at New College

Elements of inquiry: reflection, critical thinking, and research.

Talking informally about their research, many scholars will share a personal connection to the work they do in their professional roles as teachers and researchers. That connection might be grounded in a sense of social responsibility, that is, some commitment to create change. They may talk about the way their research connects meaningfully with personal, family, or community history. It may also be that a scholar pursues a particular research topic because she, he, they find the subject intriguing.  A passion to understand drives research . Often we are curious and driven to know more about issues and questions for no obvious reason at all beyond  fascination .

Far from secondary or extracurricular, personal interests and connections to research can be a powerful pathway for learning and gaining knowledge. As you make your way through this course, we ask that you pursue  twin   paths of inquiry : a path of  reflection  and a path of  research . This course will encourage you down both, thinking and writing about the lived experience that have brought you to your current interests, and pursuing research understood more traditionally as the gathering, analysis, and integration of the work of other scholars and researchers. Both activities will take you on a search for information and knowledge. Click  here  to continue reading.

Critical Thinking: An Engine for Inquiry

You have probably heard teachers talk about “critical thinking” as a method of problem solving. At the risk of being repetitive we will briefly discuss the foundational importance of critical thinking here.

Critical thinking is the activity that joins reflection and research in a process of inquiry, a careful analysis of our own experience and knowledge undertaken even as we gather more information and increase our knowledge. Definitions abound, but we ask you to adopt this definition of critical thinking at least for the duration of this course:

Critical thinking is the habit or practice of non-prejudicial and uncompromising analysis and inquiry, thinking from multiple perspectives, and adopting positions in light of all available information.

But more than just accept this definition without question, let’s put the concept of Critical Thinking to work with you answering some questions found in the  Exercise Tab .

When you are finished, check out the video, titled simply “Critical Thinking.” It offers an extended overview of this important concept.

Reflection Begins with a Decision

Question:   How do you decide what issue to pursue as a topic for inquiry?

Answer:   Choose the one that has already  chosen you .

Some issues make a claim on our interest and grab our attention through the force of their importance. These issues have impact in our lives. There is no end to the variations on  “the call to write,” as scholar and writing teacher John Trimbur  has termed the motivating factors that get writers and researchers working.  [ Read more .]

Choose a Focus

So then, let’s decide: what is the issue? The problem? The occasion? What is the question to answer? What is the challenge to which we must respond individually, locally, as well as a broader society?  You must be the one who makes these decisions in the context of the research and writing for this course.  Therefore, as we have suggested, it is crucial that you choose something that holds real interest for you, something important that demands your attention. Here’s an idea: choose something to research that you would be interested in even if you were not enrolled in a writing course!

Of course, it is impossible to officially require that students care about their work, but we offer this choice as an opportunity for you to experience how your own personal interests can be your guide to important research and writing. Our classroom community is the space in which you can follow your own interests with support, collaboratively.

When you are ready, please proceed to the  Exercise Tab.

Research and Inquiry

What academics include in their definition of  research  will vary from discipline to discipline. In some sciences, laboratory experimentation is a primary form of research, but those experiments will be different depending on the science. Some social scientists—sociologists or anthropologists, for example—often conduct extensive interviews or “ethnographies” to gather information about the experience, attitudes, or way of life among a particular segment of the population. This form of research requires methods of analysis entirely different from those of researchers in the “hard sciences” of biology, chemistry, physics, etc. Scholars in the humanities undertake other research methodologies. Historians and literary critics employ specific kinds of reading methodologies to interpret “primary texts,” perhaps hard copies aging somewhere in an archive, perhaps digitized and readily available.

You get the point;  research  is not a single activity. Research practices are multiple and adaptable.

A common and fundamental element unites most research methodologies, however. Academic research is driven by questions. These questions emerge around gaps or problems within the overall body of knowledge that makes up a discipline. This kind of research question focuses on how to build on and correct previous scholarship.

Research questions also emerge in direct response to situations “in the real world,” for there isn’t much truth to the old stereotype that academic researchers are cut off from reality and work among abstract ideas in the “ivory tower.” Certainly, we hope your direction for inquiry will be “worldly,” so to speak, engaging issues all the more important for their immediate relevance in the social world.

Whatever the focus, the time for developing research questions has arrived.

Formulating a Research Question

One way to summarize the difference between high school and college work is that in high school you were trained to answer questions that were put to you. As a student in the university, you must decide which questions are most important to ask, and more, which are most important to pursue with all your intellectual energy.

Carefully posing questions is a very important activity for academic researchers, the ranks of whom you have now joined. A research question emerges from interest and necessity and points to the direction further inquiry will take. But as your work in the previous section brainstorming possible topics suggests, coming up with a research question capable of moving you in a clear direction of inquiry will also take time and careful thought. 

This is why we believe that the questioning process must precede the answering process. While students are often told to begin research projects with a thesis statement, that is, the main claim they will support with their research, our point is that before a student can arrive at a thesis, she or he must first gather strong knowledge and certainty about a topic in order even to conceive of let alone argue a thesis.  Again, a process of inquiry is first necessary.[ Read More .]

Revising the Research Question

Once you receive feedback from your classmates, teacher, and others, you will be in a place where you can revise your research question. Remember that the final decision is yours. You will have to decide which advice for revision to take and which to ignore. However, we offer the following questions for you to consider as you revise:

• Is there agreement among your readers that your research question is too broad?
• Have you been good advice for narrowing your research question, or can you think of a good way to focus the question?
• Is there a consensus among readers that your research question is too narrow? What ideas do you have for making it more general?
• Have you received feedback that suggests you have some misunderstanding of the issue at hand?
• Have your readers provided you with specifics about what it is you misunderstand?
• Did you receive comments about the clarity of your question? Were your readers specific about how to clarify your statement of the question?

After reviewing your initial research question, all of the response initiated by this question, as well as your answers to the above questions, you are ready to move from working exercises to a formal presentation of your Research Question.

You can access the full assignment description  here .

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Critical Inquiry and Inquiry-Oriented Education

Opinion: K.P. Mohanan, Professor, Indian Institute of Science Education and Research (IISER), Pune

Human violence has multiple roots. Someone who stabs another in a fit of road-rage is acting under blind emotions. Someone who cannot kill humans but is prepared to kill animals has not expanded the scope of their ethical considerations beyond humans. And someone who wages war against another country is guided by ideological or economic factors, unaffected by ethics. To deal with violence, then, education must incorporate strands that aim at the emotional, ethical and intellectual foundations for peace.

Educating emotion requires helping the young liberate themselves from negative emotions such as anger, hostility, hatred, cruelty, intolerance, selfishness and competitiveness, while strengthening positive emotions such as empathy, compassion, love, and the spirit of altruism.

Educating the intellect for peace involves helping learners protect themselves from ideologies of violence. It should also empower them to change systems and practices that either promote violence or fail to prevent violence.

Ethical foundations draw on both emotion and intellect. Enriching the natural ethical instincts is a matter of emotions. Expanding the scope of ethical considerations is a matter of both emotions and reasoning. And connecting ethical values and principles to one’s actions and practices is a matter of reasoning.

In sum, we need a form of education that combines the emotional and the intellectual.  In this article, my concern is with the intellectual part.

Intellectual education needs to include not only the information and knowledge to work towards a non-violent world but also the abilities of critical thinking and inquiry to investigate the causes of violence, and to find ways to dissolve those causes. This means that Inquiry-Oriented Education (IOE), which seeks to develop the capacity for rational inquiry, has to be recognised as an important strand of education. What follows are my reflections on the role of rational inquiry in education and of critical inquiry as a specific form of rational inquiry.

What is Rational Inquiry?

Inquiry is the  investigation of a question on the basis of our own experience and reasoning, to look for an answer and arrive at a conclusion.

It involves:

• Questions  whose answers we wish to find out
• Methodological strategies  to look for answers
• Answers to the questions,  and  conclusions  based on them
• Rational justification  (proof, evidence, arguments) for the conclusions
• Thinking critically about  our own or others’ conclusions and justification

Rational inquiry  is inquiry that is committed to the following axioms:

• Rejecting Logical Contradictions : We must reject statements that are logically contradictory
• Accepting Logical Consequences : If we accept a set of statements, then we must also accept their logical consequences

By ‘logical contradiction’, we mean a combination of a statement and its negation. Thus, the statement that the earth is flat and the earth is not flat constitutes a logical contradiction. A logical consequence of a set of statements is a conclusion derived from them through logic. Thus, the conclusion that all humans are vertebrates is a logical consequence of these statements: (i) all humans are primates; (ii) all primates are mammals; and (iii) all mammals are vertebrates.

For readers who wish to go beyond this brief sketch, a wide range of examples of rational inquiry for school and college education are available at  www.schoolofthinq.com

Inquiry-Oriented Education, which seeks to develop the capacity for rational inquiry, has to be recognised as an important strand of education.

What is Critical Inquiry?

There are many situations where we do not realise our ignorance. We also take many beliefs and practices for granted, without questioning. When we subject such domains to critical thinking, we are pursuing  a special kind of rational inquiry, called  critical inquiry, which begins with doubting and questioning what has been taken for granted (analogous to ‘interrogating/cross-examining’ an ‘expert witness’ including ourselves) and demonstrating that we don’t know what we think we know.

Questions for critical inquiry are triggered by critical thinking.  Critical thinking is a set of mental processes for evaluating the merit of something . ‘Merit’ here could be the truth of a statement (e.g., the statement, ‘That the earth is round’ is true.), the  usefulness  of a product, action, practice, or policy to achieve a given goal (e.g., death penalty to effectively deter crime), the  ethical desirability  of an action, practice, or policy (e.g., the ethical rightness of the death penalty), the  beauty  of a work of art (e.g., Is da Vinci’s Mona Lisa a great painting?), or the  value  of something that we (ought to) strive for (e.g., we ought to liberate ourselves from anger and hatred).

Mathematical and scientific inquiries offer fruitful emotion-free terrains for the practice of critical inquiry.

Examples of Critical Inquiry

Critical inquiry into issues of terrorism, communal violence, forced migrations, xenophobia, nationalist and religious ideologies that promote violence, and the relation between economic policies and violence, are of direct relevance to education for peace. Such issues, however, are emotionally charged. They might be seductive for beginners, but precisely because of their emotional appeal, there is a danger that when investigating them, feelings replace thinking and assertions of personal opinions replace rational conclusions.

My experience suggests that for beginners to engage with such topics with adequate detachment, clarity and rigour, they need to strengthen their mental equipment in two ways: by striving for emotional maturity, in order to detach feelings from reflection and reasoning; and by strengthening and sharpening their intellectual capacity, using topics that would not create emotional storms.

Mathematical and scientific inquiries offer fruitful emotion-free terrains for the practice of critical inquiry. Let me sketch an example.

Suppose we begin a class activity for eighth graders with an innocent-sounding question:  How many angles does a triangle have?  The textbook answer is: Three. We can now initiate critical inquiry:  What is an angle such that triangles have three angles and rectangles have four?

Most novices would think of this as a trivial question. But then, the function of critical inquiry is to challenge complacency.

What is an angle?  A student’s answer might be: “If two straight lines meet in such a way that they do not form a single straight line, what lies between them is an angle.” If so, the combination of two straight lines in Fig. 1 forms an angle, but not in Fig. 2.

What is a right angle? What is an acute angle? What is an obtuse angle? What is a straight angle?  The standard textbook answers are: “A right angle measures 90º; an acute angle is less than a right angle; an obtuse angle is more than a right angle (but less than two right angles); and a straight angle is two right angles.”

We now proceed to rigorous reasoning. Given these ‘definitions’, it follows that angle ABC in Fig. 1 is an obtuse angle; while angle DEF in Fig. 2 is a staight angle. Since any straight line can be viewed as being made up of two straight lines at a straight angle, there is a straight angle at every point in a straight line.

How many angles does a straight line have?  Since every finite straight line has infinitely many points, it has infinitely many straight angles. Therefore, it has infinitely many angles. Since a triangle is made of three straight lines, it has infinitely many angles. This conclusion negates the textbook answer to the question we started with.

We now have to either accept the conclusion that triangles and rectangles have infinitely many angles, or re-define the concept of angle such that we abandon the concept of straight angle from the textbook.

If schools around the world could engage in discussions pursuing rational inquiry into principles and concepts of ethics, there would perhaps be far less violence in the world.

This begins an inquiry into questions whose answers we realise we don’t know:  What is an angle?

This example illustrates the strategy of ‘problematisation’ in critical inquiry: we begin with questions on what we think we know and take for granted; we engage critically with the answer; and realise that we don’t know what we thought we knew, triggering further inquiry.

As I said, math and science offer rich terrains for emotion-free practice of critical inquiry. Once learners acquire the necessary sharpness and strength of mind, they can be guided into critical inquiry in emotion-riddled terrains. We now explore two such examples.

2. Freedom Fighters and Terrorists

We give students the following hypothetical story.

Suppose a country, Arraya, rules over an island, Parumbi. The people of Parumbi don’t want Arraya to govern them, but the people of Arraya want Parumbi under them. Parumbians take up arms to achieve their goal. Their supporters describe them as ‘freedom fighters’, and their activity as an ‘independence struggle’. But the government of Arraya and its supporters describe them as ‘terrorists’, and their activity as ‘terrorism’.

We then give them the following real world story:

An article, “Terrorism, Not Freedom Struggle” (The Times of India, 10 August 2001) stated that “rejecting Islamabad’s description of terrorism in Jammu and Kashmir as freedom struggle,” India’s external affairs minister said that under no circumstance should India accept “Islamabad’s attempt to confer cross-border terrorism a kind of diplomatic legitimacy  1  …” Pakistan’s newspaper Business Recorder quoted Harry Truman as having warned that “once a government is committed to silencing the voice of dissent, it has only one way to go. To employ increasingly repressive measures, until it becomes a source of terror to all its citizens and creates a country where everyone lives in fear.” It went on to say: “Nothing illustrates the Indian policy, vis-à-vis occupied Kashmir, better than the above quoted remark of the American leader 2 .”

The students’ task is to spell out how we would distinguish between ‘freedom fighters’ and ‘terrorists’ and to define ‘terrorism’ and ‘independence struggle’ such that we can engage in a rational debate on whether a particular movement qualified as an independence struggle or as terrorism.

3. Nation and Nationalism

Activity 1 Write down the answers to the following questions: What is your nationality? Do you feel good when you hear your national anthem or see your national flag? Are there nations that you dislike or are hostile to? Write the names of those nations.

Activity 2 Now consider the following question: What is a nation?

Discussion : Two meanings of the term ‘nation’ emerged:

• People-nation : nation as a people united by a shared ancestry, language, and culture. (e.g. ‘Naga-nation,’ ‘Navaho- nation,’ ‘Palestine as a stateless nation’). People-nation prompts loyalty and, devotion to the people with shared ancestry, language, and culture.
• State-nation : nation as a government that rules a population in a given geographical region. (e.g. India, Pakistan, Vietnam, South Korea, United States of America, Australia, Nigeria, Argentina, and Germany). State-nations are results of war, conquest and power negotiations; they don’t require shared ethnicity, language, or culture.

A promising avenue for emotion-education is perhaps something along the lines of mindfulness meditation: ‘looking’ internally at the contents of one’s own experience . . .

Activity 3 Consider the concept of nationalism : We may define it as: a form of collective identity that prompts loyalty and devotion to  one’s nation .

Discussion : Given the two distinct concepts of nation, we needed to recognise the corresponding concepts of nationalism: people-nationalism and state-nationalism. People-nationalism might perceive the rulers as ‘foreign’, prompting the political separation of one’s people from those rulers. State-nationalism would perceive those involved in that separation as ‘traitors’. State-nationalism then is loyalty and devotion to one’s rulers and is identical to ‘patriotism’.

Activity 4 Let us go back to the questions we asked earlier: What is your nationality? Do you feel good when you hear your national anthem or national flag?

Discussion : Is your concept of nationality grounded in people-nation or state-nation? Do you feel good when you hear the national anthem or see the national flag? Do you feel patriotism rise in your heart? Does that feeling come from loyalty to the people, or to the state?

What would your nationality be now?

Which national anthem and national flag would produce feelings of patriotism in your grandchildren? Which nations are your grandchildren likely to hate?

Now answer the same questions by assuming that there were no wars anywhere in the world after the tenth century, and that the political map continued without change till today.

After thinking through these questions, go back to the concepts of state-nation and peoples-nation and write a one-page reflection on the concepts of nation, nationality, nationalism, and patriotism, and the role of violence in the origin and evolution of nations.

An Example of Ethical Inquiry

As a form of rational inquiry, ethical inquiry seeks to help develop the capacity to construct and evaluate ethical theories at individual and collective levels and to deduce the ethical judgements derived from those theories.

In a class session that I did for 6th Graders in Pune, India, the children came up with this ethical principle:  It is immoral to kill humans and other creatures . During the subsequent discussion, one child said that the principle doesn’t apply to enemies. The entire class agreed that it is okay to kill enemies. The principle was revised as:  It is immoral to kill fellow creatures other than enemies .

Some students even suggested that killing enemies is our ethical duty. This resulted in the following dialogue:

At this point, they were no longer sure about their position on enemies. I gave them a few minutes to discuss the problem in groups and come up with a concept of ‘enemy’ such that killing enemies is okay. After some discussion, most groups came up with the following statements:

Those who want to kill others are our enemies.

Those enemies exist in both India and Pakistan.

I would have liked to raise the question: Is it morally right to kill someone who has killed another? This could have taken us to fairly complex issues like mercy-killing, honour-killing, war, abortion and death penalty. I did not pursue that line of inquiry, for I wasn’t sure if it was age-appropriate for the children.

If schools around the world could engage in discussions of this kind, pursuing rational inquiry into principles and concepts of ethics, there would perhaps be far less violence in the world.

  Contemplative Inquiry

As mentioned earlier, the education of emotions has an important role to play in minimising human violence. A promising avenue for emotion-education is perhaps something along the lines of mindfulness meditation: ‘looking’ internally at the contents of one’s own experience, including sensory and non-sensory experience, as well as the experience of emotions. Meditative techniques such as attending to breathing, body scan, loving-kindness and observing thought are forms of looking at the inner world 3 .

The so-called  contemplative inquiry  in this tradition is a form of rational inquiry that takes the results of such introspection as the grounds of inquiry to arrive at rational conclusions about oneself. This allows us to address questions as, “Am I a covert racist?” “Am I as ethical as I think?”, “Do I carry hatred in me?”, as part of inquiry into a fundamental question: “Who am I?”

Instead of merely experiencing emotions such as anger or hostility, we can employ contemplative inquiry with the rational-perceptual part of the mind examining with equanimity the emotional suffering part. The outcome of attention then forms the basis for rational investigation of oneself.

Inquiry-Oriented Education

Helping the young to develop the capacity to engage in these diverse modes of rational inquiry, combined with practices that enhance positive emotions and dissolve negative ones, is an imperative that institutionalised education can no longer afford to ignore in today’s world. Mathematical, scientific, conceptual, ethical and contemplative inquiries play significant roles in this enterprise, which would involve incorporating the strand of Inquiry-Oriented Education into schooling at the primary, secondary, as well as tertiary levels. UNESCO MGIEP has currently undertaken such a move in a collaborative endeavour with ThinQ 4  in its LIBRE programme.

1   http://timesofindia.indiatimes.com/india/Terrorism-not-freedom-struggle-Jaswant/articleshow/1086523490.cms

2   http://www.brecorder.com/index.php?option=com_news&view=single&id=1108304

3  http://greatergood.berkeley.edu/article/item/how_to_choose_a_type_of_mindfulness_meditation

4   www.schoolofthinq.com

K.P. Mohanan  received his Ph.D. from the Massachusetts Institute of Technology (MIT) and taught at the University of Texas in Austin, MIT, Stanford University and the National University of Singapore (NUS). At NUS, he initiated the General Education Programme for undergraduate students and, as part of this programme, created a web course on Academic Knowledge and Inquiry.

In January 2011, he moved to IISER-Pune, where he created a three-course package on rational inquiry, covering scientific, mathematical, and conceptual inquiries. He is currently engaged in developing courses and programmes on different types of inquiry-based learning for high school and college students.

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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Inquiry: A Fundamental Concept for Scientific Investigation

• First Online: 27 October 2022

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This chapter provides a brief description of ‘inquiry’, a very important but rarely includes in the research methodology books. Initially, the chapter explains the conceptual definition of inquiry with the phases that develop ideas about inquiry. Then the chapter discusses the different characteristics of inquiry. Then, it provides a brief description of the theories and sources of inquiry in social research. The processes, steps, and methods of inquiry are explained with ‘20 questions’ inquiry process. Then the chapter includes a brief description of the position of inquiry in education learning. Finally, the chapter explains the importance of inquiry in social research.

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Islam, M.R. (2022). Inquiry: A Fundamental Concept for Scientific Investigation. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_1

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Question of the Day Examples

By Med Kharbach, PhD | Last Update: May 25, 2024

The importance of questioning in the classroom cannot be overstated, as it is a fundamental tool for fostering engagement, critical thinking, and deeper understanding. According to Patrícia Albergaria Almeida (2012), effective classroom questioning shifts the focus from teacher-centered to student-centered learning, encouraging higher-order thinking and active participation. Almeida’s research highlights that while teachers ask a high volume of questions—between 300 and 400 daily—students ask significantly fewer, typically only one question per week. This disparity underscores the need for greater awareness and strategies to promote student questioning, as it is vital for uncovering students’ conceptual understanding and reasoning processes.

Similarly, Rodolfo A. Neirotti (2021) emphasizes that questioning is crucial for understanding and exploring the world around us. Questions drive curiosity and foster an analytical mindset, allowing students to connect new information with prior knowledge and make sense of complex concepts. Neirotti argues that questioning helps improve interactions, stimulate creativity, and support scientific inquiry, which are essential for intellectual growth and problem-solving.

In today’s post, I compiled an extensive list of Question of the Day examples that you can use with your students or colleagues to spark engagement, foster critical thinking, and promote a dynamic learning environment. These questions are carefully categorized to cover diverse themes, including Cultural Appreciation, Environmental Awareness, Historical Perspectives, STEM Curiosities, Creative Expression, Global Citizenship, Philosophical Inquiry, Health and Wellness, Innovative Thinking, and Interpersonal Skills.

Question of The Day Examples

Here are some engaging “Question of the Day” prompts to spark curiosity and foster a dynamic learning environment.

1. Cultural Appreciation

Understanding and appreciating diverse cultures is essential in our interconnected world. This category encourages students to explore traditions, customs, and values from various cultures, fostering a sense of global awareness and respect. Through these questions, students will learn about the richness of cultural diversity and the importance of inclusivity.

• What is one tradition from another culture that you find interesting and why?
• How do different cultures celebrate the New Year?
• Can you name a traditional dish from another country and describe it?
• What are some common cultural symbols from around the world?
• How do various cultures honor their ancestors?
• What is a unique holiday celebrated in another country?
• How do different cultures approach education?
• What is one art form unique to a specific culture?
• How do people in different countries greet each other?
• What are some traditional clothing items from different cultures?
• How do various cultures celebrate weddings?
• What are some unique musical instruments from around the world?
• How do different cultures celebrate coming-of-age ceremonies?
• What is a popular sport in another country that is less known here?
• How do various cultures view and use traditional medicine?

2. Environmental Awareness

Our planet faces numerous environmental challenges, and it’s crucial to raise awareness about sustainability and conservation. This category focuses on questions that highlight the significance of protecting our environment. Students will explore topics like climate change, recycling, and renewable energy, inspiring them to take action towards a greener future.

• What is one simple way you can reduce your carbon footprint?
• How does recycling help the environment?
• What are the effects of deforestation on wildlife?
• How does pollution affect marine life?
• What are the benefits of using renewable energy sources?
• How can planting trees help combat climate change?
• What are the consequences of plastic waste in the oceans?
• How can we conserve water in our daily lives?
• What are some endangered species and why are they at risk?
• How does composting benefit the environment?
• What is the importance of biodiversity?
• How do oil spills impact the environment?
• What are some ways to promote sustainable agriculture?
• How does urbanization affect natural habitats?
• What role do bees play in our ecosystem?

Related: 100 Engaging Philosophical Questions for Kids

3. Historical Perspectives

History offers invaluable lessons and insights into our present and future. This category prompts students to delve into significant historical events and figures, encouraging them to think critically about the past. By understanding history, students can better appreciate the complexities of the world and the progress we’ve made.

• What is one historical event you would like to witness and why?
• How did the invention of the printing press change the world?
• What are some lessons we can learn from ancient civilizations?
• How did the Industrial Revolution impact society?
• What is the significance of the Magna Carta?
• How did the discovery of electricity revolutionize life?
• What are the key causes of the World Wars?
• How did the civil rights movement shape modern society?
• What is the impact of the Renaissance on art and culture?
• How did explorers like Christopher Columbus change the world?
• What is the historical significance of the Great Wall of China?
• How did the Cold War influence global politics?
• What can we learn from the fall of the Roman Empire?
• How did the Space Race affect technological advancement?
• What was the impact of the Silk Road on trade and culture?

4. STEM Curiosities

Science, Technology, Engineering, and Math (STEM) are fields that drive innovation and shape our future. This category is designed to spark curiosity and interest in STEM topics. Through these questions, students will explore fascinating concepts and recent advancements, encouraging them to think like scientists and engineers.

• How do vaccines work to protect us from diseases?
• What are black holes and why are they important to study?
• How does coding contribute to creating video games?
• What are some recent breakthroughs in renewable energy?
• How does 3D printing work and what are its uses?
• What is the role of DNA in heredity?
• How do self-driving cars navigate and avoid obstacles?
• What are the benefits and risks of artificial intelligence?
• How does the internet work?
• What are the basic principles of quantum physics?
• How do weather satellites predict storms?
• What are some cutting-edge materials used in construction?
• How do we measure the distance between stars?
• What are the applications of nanotechnology in medicine?
• How does the human brain process information?

5. Creative Expression

Creativity is a vital part of personal and academic growth. This category inspires students to express themselves artistically and imaginatively. Whether through art, music, writing, or design, these questions encourage students to explore their creative potential and understand the value of artistic expression.

• If you could create a new art form, what would it be?
• How does music influence your mood and creativity?
• What story would you tell if you wrote a book?
• How would you design a dream home?
• What inspires you to create art?
• If you could compose a song, what would it be about?
• How would you direct a movie with no dialogue?
• What is your favorite way to express yourself creatively?
• If you could build a sculpture out of any material, what would you use?
• How do colors influence your artwork?
• What role does creativity play in solving everyday problems?
• How would you choreograph a dance to tell a story?
• If you could design a video game, what would its theme be?
• How do different cultures influence artistic styles?
• What would you paint if you had a giant canvas and no restrictions?

6. Global Citizenship

Being a responsible global citizen means understanding and addressing global issues. This category promotes awareness of topics like human rights, global health, and social justice. Through these questions, students will learn about their role in the global community and how they can contribute to a more equitable world.

• How can we support fair trade practices globally?
• What are the impacts of global warming on different regions of the world?
• How can we help refugees in our communities?
• What are the benefits of learning a second language?
• How do international organizations like the UN help maintain peace?
• What are some ways to reduce global poverty?
• How does access to education vary around the world?
• What is the importance of protecting human rights?
• How can we promote gender equality globally?
• What are the effects of global health crises on different countries?
• How does global trade affect local economies?
• What role can individuals play in combating climate change?
• How does cultural exchange benefit global understanding?
• What are the consequences of deforestation in the Amazon rainforest?
• How can we support clean water initiatives worldwide?

7. Philosophical Inquiry

Philosophy encourages deep, critical thinking about life’s fundamental questions. This category challenges students to consider philosophical ideas and ethical dilemmas. By engaging with these questions, students will develop their reasoning skills and explore different perspectives on complex issues.

• What is the meaning of happiness?
• Do humans have free will?
• What is the nature of reality?
• Is there such a thing as absolute truth?
• What is the purpose of life?
• Can we ever truly know another person’s mind?
• What makes an action morally right or wrong?
• Is it possible to achieve true equality?
• What is the value of art in society?
• Can machines possess consciousness?
• What is the role of government in our lives?
• How do we define beauty?
• Is there life after death?
• What are the limits of human knowledge?
• How do we determine what is just?

8. Health and Wellness

Promoting health and wellness is essential for a balanced life. This category focuses on questions that encourage students to think about their physical and mental well-being. Topics include nutrition, exercise, mindfulness, and stress management, helping students develop healthy habits and self-awareness.

• What are the benefits of a balanced diet?
• How does exercise impact mental health?
• What are some effective stress management techniques?
• Why is sleep important for overall health?
• How can mindfulness improve daily life?
• What are the signs of a healthy friendship?
• How does staying hydrated affect your body?
• What are the benefits of spending time in nature?
• How can setting goals improve mental health?
• What role does laughter play in well-being?
• How can you create a personal wellness plan?
• What are the benefits of practicing gratitude?
• How does music influence your mood?
• What is the importance of regular medical check-ups?
• How can volunteering boost your happiness?

9. Innovative Thinking

Innovation drives progress and solves problems. This category encourages students to think creatively and entrepreneurially. Through these questions, students will explore ways to address challenges and create new opportunities, fostering a mindset of innovation and proactive problem-solving.

• What problem in your community would you like to solve with an invention?
• How can we make renewable energy more accessible?
• What new technology could improve education?
• How can design thinking be applied to everyday problems?
• What is an example of an innovative solution to a global issue?
• How can we use technology to reduce food waste?
• What startup idea do you think would succeed today?
• How can we promote entrepreneurship in young people?
• What is the future of transportation?
• How can we make healthcare more affordable and effective?
• What role does creativity play in innovation?
• How can businesses become more environmentally sustainable?
• What is the next big thing in technology?
• How can we encourage more women in STEM fields?
• What innovative approach could solve the housing crisis?

10. Interpersonal Skills

Effective communication and strong interpersonal skills are key to personal and professional success. This category helps students develop social skills, empathy, and leadership qualities. These questions encourage students to reflect on their interactions with others and improve their ability to collaborate and connect.

• How can you show empathy in a conversation?
• What are some effective ways to resolve conflicts?
• How can you improve your active listening skills?
• What are the benefits of giving and receiving constructive feedback?
• How can you build trust in a team?
• What are some ways to practice effective communication?
• How do you handle difficult conversations?
• What are the qualities of a good leader?
• How can you be more assertive without being aggressive?
• What role does body language play in communication?
• How can you improve your public speaking skills?
• How do you build and maintain healthy relationships?
• What are some strategies for networking?
• How can you be a better collaborator?
• What are the benefits of understanding different communication styles?

Related: Attendance Questions for Your Class

Importance of Questions in Learning

For those interested in exploring the significance of questions in educational settings, several key research papers provide valuable insights and practical strategies. For instances, Robin Alexander’s “Towards Dialogic Teaching” (2005) emphasizes the role of dialogue and questioning in fostering a more interactive and engaging classroom environment. Allison and Shrigley (1986) discuss techniques for teaching children to ask operational questions in science, highlighting the importance of inquiry-based learning. On their part, Arzi and White (1986) explore the types and impacts of students’ questions in science education, offering a research-based perspective on promoting student curiosity.

Similarly, Paul Black and colleagues (2002) in “Working Inside the Black Box” focus on how questioning and formative assessment can enhance learning outcomes. Blosser (1995) provides practical advice on asking effective questions, while Browne and Keeley (1998) offer a guide to critical thinking through the art of questioning. Carlsen (1991) analyzes classroom questioning from a sociolinguistic perspective, providing a deeper understanding of its dynamics.

For a problem-based learning approach, Chin and Chia (2004) demonstrate how student questions drive knowledge construction. Penick, Crow, and Bonnsteter (1996) argue that questions are fundamental to effective science teaching while Rop (2002) investigates the meaning and impact of student inquiry questions from the teacher’s viewpoint.

As for Rosenshine, Meister, and Chapman (1996), they provided an extensive review of intervention studies on teaching students to generate questions. Finally, Shodell (1995) advocates for a question-driven classroom to stimulate student engagement and learning.

These resources collectively underscore the transformative power of questioning in education, offering both theoretical insights and practical approaches to enhance teaching and learning.

• Almeida, P. A. (2012). Can I ask a question? the importance of classroom questioning. Procedia – Social and Behavioral Sciences , 31, 634-63. https://doi.org/10.1016/j.sbspro.2011.12.116.
• Alexander, R. (2005). Towards dialogic teaching . York, UK: Dialogos.
• Allison, A.W., & Shrigley, R.L. (1986). Teaching children to ask operational questions in science. Science Education , 70, 73–80.
• Arzi, H.J. & White, R.T. (1986). Questions on students’ questions. Research in Science Education , 16, 82–91.
• Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2002). Working inside the black box: Assessment for learning in the classroom . London: King’s College London
• Blosser, P.E. (1995). How to ask the right questions. Arlington , VA: National Science Teachers Association
• Browne, M.N., & Keeley, S.M. (1998). Asking the right questions: A guide to critical thinking. Englewood Cliffs , NJ: Prentice Hall.
• Carlsen, W.S. (1991). Questioning in classrooms: A sociolinguistic perspective. Review of Educational Research , 61, 157–178.
• Chin, C., & Chia, L.G. (2004). Problem-based learning: Using students’ questions to drive knowledge construction. Science Education , 88, 707–727.
• Chin, C., & Osborne, J. (2008). Students’ questions: a potential resource for teaching and learning science. Studies in Science Education , 44, 1-39.
• Neirotti, R. A. (2021). The importance of asking questions and doing things for a reason. Braz J Cardiovasc Surg , 36(1): I-II. doi: 10.21470/1678-9741-2021-0950 . PMID: 33594859; PMCID: PMC7918389.
• Penick, J.E., Crow, L.W., & Bonnsteter, R.J. (1996). Questions are the answers. Science Teacher , 63, 26–29.
• Rop, C.J. (2002). The meaning of student inquiry questions: A teacher’s beliefs and responses. International Journal of Science Education , 24(7), 717–736.
• Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching students to generate questions: A review of the intervention studies. Review of Educational Research , 66, 181–221.
• Shodell, M. (1995). The question-driven classroom. American Biology Teacher , 57, 278–281.

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Meet Med Kharbach, PhD

Dr. Med Kharbach is an influential voice in the global educational technology landscape, with an extensive background in educational studies and a decade-long experience as a K-12 teacher. Holding a Ph.D. from Mount Saint Vincent University in Halifax, Canada, he brings a unique perspective to the educational world by integrating his profound academic knowledge with his hands-on teaching experience. Dr. Kharbach's academic pursuits encompass curriculum studies, discourse analysis, language learning/teaching, language and identity, emerging literacies, educational technology, and research methodologies. His work has been presented at numerous national and international conferences and published in various esteemed academic journals.

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