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Trends and Hot Topics of STEM and STEM Education: a Co-word Analysis of Literature Published in 2011–2020

• Published: 23 February 2023

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• Ying-Shao Hsu   ORCID: orcid.org/0000-0002-1635-8213 1 , 2 ,
• Kai-Yu Tang   ORCID: orcid.org/0000-0002-3965-3055 3 &
• Tzu-Chiang Lin   ORCID: orcid.org/0000-0003-3842-3749 4 , 5  

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This study explored research trends in science, technology, engineering, and mathematics (STEM) education. Descriptive analysis and co-word analysis were used to examine articles published in Social Science Citation Index journals from 2011 to 2020. From a search of the Web of Science database, a total of 761 articles were selected as target samples for analysis. A growing number of STEM-related publications were published after 2016. The most frequently used keywords in these sample papers were also identified. Further analysis identified the leading journals and most represented countries among the target articles. A series of co-word analyses were conducted to reveal word co-occurrence according to the title, keywords, and abstract. Gender moderated engagement in STEM learning and career selection. Higher education was critical in training a STEM workforce to satisfy societal requirements for STEM roles. Our findings indicated that the attention of STEM education researchers has shifted to the professional development of teachers. Discussions and potential research directions in the field are included.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Ying-Shao Hsu

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Hsu, YS., Tang, KY. & Lin, TC. Trends and Hot Topics of STEM and STEM Education: a Co-word Analysis of Literature Published in 2011–2020. Sci & Educ (2023). https://doi.org/10.1007/s11191-023-00419-6

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Integrating Critical Approaches into Quantitative STEM Equity Work

Meaghan i. pearson.

† Combined Program in Education and Psychology, University of Michigan, Ann Arbor, MI 48109

Sarah D. Castle

‡ Program in Mathematics Education, Michigan State University, East Lansing, MI 48824

Rebecca L. Matz

§ Center for Academic Innovation, University of Michigan, Ann Arbor, MI 48109

Benjamin P. Koester

ǁ Department of Physics, University of Michigan, Ann Arbor, MI 48109

W. Carson Byrd

¶ National Center for Institutional Diversity, University of Michigan, Ann Arbor, MI 48109

The recent anti-racist movements in the United States have inspired a national call for more research on the experiences of racially marginalized and minoritized students in science, technology, engineering, and mathematics (STEM) fields. As researchers focused on promoting diversity, equity, and inclusion, we contend that STEM education must, as a discipline, grapple with how analytic approaches may not fully support equity efforts. We discuss how researchers and educational practitioners should more critically approach STEM equity analyses and why modifying our approaches matters for STEM equity goals. Engaging with equity as a process rather than a static goal, we provide a primer of reflective questions to assist researchers with framing, analysis, and interpretation of student-level data frequently used to identify disparities and assess course-level and programmatic interventions. This guidance can inform analyses conducted by campus units such as departments and programs, but also across universities and the scientific community to enhance how we understand and address systemic inequity in STEM fields.

INTRODUCTION

Over the past 2 years, the world watched as police in the United States killed Black, Hispanic and Latinx Americans ( Egbuonu, 2020 ); hate crimes toward Asians, Asian Americans, and Pacific Islanders surged ( Gover et al. , 2020 ); and disproportionate numbers of Black, Hispanic and Latinx, and Native American people fell victim to the COVID-19 pandemic ( Alcendor, 2020 ). Questions arose across countries, institutions, and communities about how these devastating and brutal events reflect underlying systemic inequities that exist across the globe (e.g., Collins et al. , 2021 ). Higher education in particular witnessed another reinvestment in diversity, equity, and inclusion (DEI) efforts with reinvigorated commitments to faculty cluster hires, curricular revisions, and changes to admissions criteria ( Heinecke and Beach 2020 ; Peoples and Dillard, 2020 ). History has shown, however, that diversity initiatives can serve as mechanisms for institutions to send out signals of advancement without actually translating to systemic changes ( Ahmed, 2012 ; Ray, 2019a , b ; Johnson, 2020 ; Thomas 2020 ).

Likewise, science, technology, engineering, and mathematics (STEM) fields are focusing significant attention toward improving DEI in reflection on student enrollment and success data marred by gendered, racialized, and classed patterns ( Asai, 2020 ; Griffin et al. , 2020 ; McGee, 2020a , b ). Quantitative analyses in and focused on STEM education that rely on commonly available demographic variables (herein we consider gender, race, income, and parental education) are ever more present ( Li et al. , 2020 ). The key issue we address here with STEM equity analyses is that demographic variables are often used automatically in “assessing student success” without situating these student characteristics in relation to the overlapping structural inequities that shape students’ experiences and academic performance.

Because of this tendency, there are often mismatches between the outcomes of equity analyses and how they are interpreted to inform institutional efforts that promote DEI in STEM. By inadequately grappling with the theoretical framing of students’ identities in relation to inequities, current methodological approaches guiding equity analyses can contribute to individualizing inequality ( Byrd, 2021 ). As a consequence, educational initiatives are developed that prioritize changing students and not the STEM environments that perpetuate such inequalities.

STEM equity analyses are conducted by researchers in many different settings across academia—for example, STEM departments and programs do assessment work, institutional research offices conduct internal studies, and even individual instructors may be equipped to investigate their own courses. These analyses are also conducted by federal agencies, policy institutes, and corporations. That is, whether explicitly or implicitly described as “equity analyses” within a department, program, or organization, STEM equity analyses are part and parcel of everyday assessment and evaluation in STEM. Therefore, our intention here is to provide information that is applicable for researchers across the broader STEM community. Fostering an authentic discussion of issues raised by these questions will lead to better analyses that support justice for students who have historically been excluded from and continue to experience marginalization in STEM fields.

The purpose of this essay is twofold. We first discuss the importance of integrating critical perspectives in STEM equity research that relies on quantitative analyses. Then, through a series of critical questions, we aim to engender reflection and conversation with researchers and practitioners who do STEM equity research so that we all can use quantitative data more responsibly and accurately. This discussion was sparked by our own challenges in working with institutional data to better represent and understand the experiences and outcomes of marginalized and minoritized students 1 in STEM through the interdisciplinary Sloan Equity and Inclusion in STEM Introductory Courses (SEISMIC) collaboration. We (the authors) represent the fields of chemistry and biology education, physics and astronomy, mathematics and mathematics education, educational psychology, and sociology and engage in quantitative research regularly in our positions as faculty, staff, and graduate students. We identify mostly as cis-heterosexual men and women, and all of us, except for one author, identify as white. Nonetheless, we all identify with and embrace the need for our fields and institutions to continually improve research and decision making aimed at tackling campus inequalities and injustices.

WHAT ARE CRITICAL APPROACHES TO RESEARCH?

As researchers, the lens through which we view the world has implications for the research questions we seek to explore and our methodological and analytical choices. Those who receive their academic training in STEM fields are generally socialized to adopt a postpositivist lens ( Guba and Lincoln, 1994 ; Harding, 2006 ). Postpositivists assume that objectivity is achievable, where truths about the world remain credible if those seeking it follow the scientific method. This rationale works when observing scientific and mathematical phenomena like gravity or volcanic eruptions but breaks down when studying human experiences. Humans are situated in social contexts that have been shaped by historical events, structural forces, and interaction with other human beings. Thus, humans are more than objects of inquiry; humans and their experiences are a by-product of the structures in which they are embedded ( Horkheimer, 1972 ; Bohman, 2005 ; Devetak, 2005 ).

In contrast to postpositivism, a critical lens assumes that what can be known about the world is socially constructed. Critical theorists separate themselves from traditional theorists across fields ( Bohman, 2005 ). Critical theories explore how historical events and society have shaped present-day experiences and understandings of how the world functions ( Horkheimer, 1972 ). So, whereas traditional theoretical approaches place the phenomenon of interest at the center of analysis, critical theorists seek to place societal contexts that shape a phenomenon as the focal point. Currently, several critical perspectives—that is, feminist, race, queer, disability, and decolonial theories—are accepted in the critical canon ( Bell, 1995 ; hooks, 2000 ; Watson, 2005 ; Siebers, 2008 ; Mignolo, 2012 ). The common theme in these theoretical perspectives is their assertion that society has produced oppressive structures (e.g., patriarchy, racism, sexism, colonialism, and ableism) that harm those who are not white, cisgendered, male, heterosexual, able-bodied, wealthy, and Western individuals. As a result, there exists the need for society and those in it to interrogate how those oppressive ideologies show up in ourselves and connect to oppressive structures in the world around us. From there, we can begin to imagine new strategies for how to make life better for those who find themselves on the margins ( Bohman, 2005 ; Devetak, 2005 ; hooks, 2000 ).

WHY ARE CRITICAL APPROACHES NEEDED IN STEM EQUITY RESEARCH?

Before we can discuss why a critical approach is needed, we must first define the goal of conducting STEM equity research. Over the past 2 years, we have seen increases in the numbers of people wanting to participate in research practices that focus on equity efforts in STEM, many of whom aim to increase the representation of marginalized and minoritized students in STEM fields for the betterment of our institutions and the U.S. economy ( President’s Council of Advisors on Science and Technology, 2020 ). However, placing the United States and our institutions as the motivations for our work and not the students, who often find themselves pushed out of the fields they once found joy in ( McGee, 2020a ), is a striking concern we must all sit with. We argue that engaging in critical research is an effort to re-center the students, to create safer and healthier environments for them to pursue their passions.

STEM equity researchers must grapple with the historical events that have shaped what STEM fields look like today. STEM environments have led to scientific discoveries and innovation, but these environments also have a history of reproducing systemic inequities that harm individuals ( Graves, 2001 ; Roberts, 2011 ; Wilder, 2013 ; Gholson, 2016 ; Joseph et al. , 2019 ; Saini, 2019 ; Cech and Waidzunas, 2021 ; Reinholz and Ridgway, 2021 ). Additionally, it is important to acknowledge that STEM fields have a history of conducting research, creating theories, and making measurements that primarily centered white, cisgendered, male, heterosexual, able-bodied, wealthy individuals ( Harding, 2006 ). Consequently, the prioritization of individuals from privileged groups in STEM has produced research and policies that are susceptible to the structural inequities and personal biases that have historically harmed and excluded marginalized and minoritized communities.

Thus, we argue that the necessity of integrating critical approaches is directly linked to the need for revision in STEM education. Kiese Laymon, a Black Mississippian writer and professor, notes that “revision is a dynamic practice of revisitation, premised on ethically reimagining the ingredients, scope, and primary audience of one’s initial vision” ( Laymon, 2021 , para. 16). Laymon (2021) also argues that the current racial inequities we see in American society are a product of America’s failure to wrestle with and acknowledge its history of “anti-black terror” (para. 28). These arguments apply to STEM equity research, in which our inability to confront our institutions’ historical legacy of slavery, indigenous erasure, and exclusion of those who were not white, cisgendered, male, heterosexual, able-bodied, wealthy in our research and institutions is tied to the lack of representation we see in STEM fields. We assert that STEM equity researchers must commit to an act of revision in which we reflect on our motivations, historical and societal influences, and research processes in the hopes that we can imagine and work toward a more equitable future.

WHAT DO CRITICAL APPROACHES LOOK LIKE IN QUANTITATIVE RESEARCH?

For the purposes of this essay, we outline how to incorporate critical approaches in quantitative STEM equity research. Quantitative analyses are pivotal tools for examining DEI in STEM, but it is imperative to understand that researchers’ positions in relation to race, gender, socioeconomic status, where they are located within universities, academic training, and other characteristics can shape how they approach data and analyses. Researchers employing quantitative methods within a postpositivist framework tend to eschew acknowledging how the positionality of researchers impacts analytic decision making (Zuberi and Bonilla Silva, 2008; Gillborn et al. , 2018 ; López et al. , 2018 ). Additionally, centering individual demographic variables (e.g., race, gender, and ability) instead of structural inequities positions marginalized and minoritized students as solely responsible for their lack of representation in STEM fields. These unrecognized beliefs can lead to misinterpretations of people’s experiences, which in turn negatively affects campus decision making and policies ( Sultana, 2007 ).

Critical quantitative (QuantCrit) approaches are helpful for those interested in studying ways to improve the experiences of marginalized and minoritized students from a quantitative standpoint. The foundational elements of QuantCrit are tied to critical race theory but are also aligned with other perspectives of the critical canon. Critical race theory explores where and how racism prevents people of color from accessing social and economic opportunities ( Bell, 1995 ; Ladson-Billings, 2009 ). Critical race theorists are also interested in subverting deficit-framing projections by documenting the ways that people of color actively resist and cultivate joy despite racist structures ( Devetak, 2005 ; Harper, 2010 ; Delgado and Stefancic, 2017 ). Relying on critical race theory, QuantCrit theory allows researchers to revise traditional notions of viewing relationships among racial groups as causal, instead seeing them as a reflection of historical and existing structural racism that differentially affects racial and ethnic groups ( Zuberi, 2001 ).

Scholars who use QuantCrit: 1) grapple with the historical and present-day reality of racism; 2) recognize how the practice of naively using statistics can uphold white supremacy (e.g., achievement gaps); 3) interrogate how social categorizations such as race and ethnicity are varied, contested, and fluid over time ( Omi and Winant, 2015 ) and how these shifts can impact analyses and interpretations; 4) integrate the voices of racially marginalized and minoritized individuals through qualitative and mixed-methods approaches to account for limitations in quantitative interpretations; and 5) embrace research methods, including quantitative approaches, to pursue equity goals that align with a social justice liberatory agenda (see Gillborn et al. , 2018 ). Recently, those who employ QuantCrit approaches have begun to extend these tenets beyond focusing solely on racism, incorporating how individuals are impacted by overlapping structural inequities ( Crenshaw, 1989 ; Collins, 2000 ; Jang, 2018 ). Accordingly, using QuantCrit approaches provides researchers with the ability to use statistical practices as an analytical tool for improving the social conditions of marginalized and minoritized populations.

CRITICAL QUESTIONS: INTEGRATING QUANTCRIT APPROACHES IN STEM EQUITY ANALYSES

As STEM equity researchers who were originally trained in a postpositivist paradigm, we understand firsthand that learning how to be a QuantCrit researcher is difficult. As described, STEM fields have a history of pushing out marginalized and minoritized students ( Gholson, 2016 ; Joseph et al. , 2019 ; McGee, 2020a , b ; Cech and Waidzunas, 2021 ; Reinholz and Ridgway, 2021 ) and using harmful statistical approaches that contribute to negative perceptions of students ( Zuberi, 2001 ; Zuberi and Bonilla-Silva, 2008 ). Instead of repeating harm, we as STEM education researchers can revise our paradigm to reflect more equity-centered approaches. Relying on Gutiérrez’s (2013) sociopolitical framework and critical perspectives ( Crenshaw, 1989 ; Bohman, 2005 ; Cooper, 2018 ), we define equity as the process of reckoning with how historical events have shaped and continue to reinforce unequal power imbalances in a given context and actively working to dismantle those power imbalances so that society can restructure itself to better sustain and empower all. Importantly, this definition emphasizes continual adaptation as a goal of equity in order to accommodate changing perspectives of how we understand power, inequality, and injustice in our work.

Gutiérrez (2002) similarly argues that equity is a process, rather than a static goal, reflective of individual, institutional, and societal processes. As institutions and fields evolve through space (i.e., geographic location, institution, and classroom) and time, there will always be a need to reimagine new equitable practices. Therefore, here, we use the structure of questions rather than asserting definitive guidelines to follow, reifying our commitment to equity as a process with no universal, one-size-fits-all approach to equity analyses. These questions can assist researchers in adjusting their methodological approaches to the contexts of the educational environments in their studies as well as in delineating which data are collected and selected for analyses. To this end, imperfection and improvement represent the norm of equity analyses and can provide clarifications with each iteration. As a result, we advise that researchers use the following eight questions (summarized in Table 1 ) as self-reflective tools, rather than as an exhaustive list of questions to consider with every analysis.

Critical questions: A guide to integrating critical approaches in STEM equity quantitative analyses

How Does Lived Experience Affect How One Approaches Research?

The lenses through which researchers view the world are influenced by their lived experiences accumulated through a multitude of interpersonal interactions and exposure to and engagement with different research perspectives, methodologies, and theories. When using quantitative approaches, researchers often implicitly regard themselves as objective observers, with numbers viewed as neutral ( Guba and Lincoln, 1994 ). This practice becomes especially concerning in social and educational research, as scholars have uncovered how numbers and data have been used to reinforce social inequities. For instance, Zuberi and Bonilla-Silva (2008) highlight how “statistical analysis was developed to explain the racial inferiority of colonial and second-class citizens in the new imperial era” (p. 5). These harmful statistical practices still permeate education research today, with researchers studying achievement gaps in higher education without adequate explanation of structural barriers ( Ladson-Billings, 2006 ; Gutiérrez, 2008 ). It is important to realize that researchers are human beings who are situated in societal contexts that privilege some groups over others ( Bohman, 2005 ). As a result, researchers are prone to have conscious and unconscious biases that influence the development of research questions and decision-making practices for measurement and analyses ( Harding, 1992 ).

A common practice in qualitative research is to write positionality statements ( Secules et al. , 2021 ). In these statements, researchers discuss how their backgrounds and experiences impacted their academic trajectories and relationships to research. Harding (1992) argues that being upfront about one’s biases, values, and experiences reflects “strong objectivity,” because it allows the audience to understand how a researcher’s lived experience and personal biases might impact the study. Given that positionality statements are not a common practice in quantitative research, we understand that researchers might be hesitant to include them in their work. However, a core component of being a critical scholar is constantly reflecting on how society influences one’s view of the world and in return how one chooses to do research ( Guba and Lincoln, 1994 ). By grappling with their positionality, researchers can better understand the strengths and limitations of their lenses and thus their work. Before and throughout the research process, we advise that researchers take time to reflect on, and perhaps write about, their positionality. Additionally, researchers should assess whether their lived experiences or academic training prepared them to conduct their research.

What Theoretical Assumptions Are Present in Conceptualizations of Equity Practices?

Before analyzing data, researchers should first assess their definitions of equity. Rodriguez et al. (2012) outlined, for example, three different equity models using the language of parity, fairness, and individuality. Equity models based on parity focus on getting minoritized students to obtain similar levels of academic success as majority groups members, and equity models based on fairness aim to get different groups of students to achieve similar levels of progress on tasks and assignments ( Rodriguez et al. , 2012 ). An inherent problem in these models is the assumption that academic success is contingent upon the behaviors and beliefs of majority groups. The equity of parity model also does not account for the historical trauma and discrimination that has hindered marginalized and minoritized students’ academic success in STEM programs. Individual students have their own sets of privileges and disadvantages that influence their needs and experiences in STEM learning environments. Therefore, STEM equity models should accept that conceptualizations of equity will vary across groups and situations, and not neatly align with cut-and-dried societal hierarchies.

Rodriguez et al. (2012) advocate for researchers to use equity models of individuals in which researchers attend to the factors that have harmed marginalized and minoritized students’ access to STEM fields and develop conceptualizations of success for each individual group. Going further, Gutiérrez (2013) argues that focusing on individual groups is not enough, rather that equitable practices within STEM contexts must contend with the ways that identity and power manifest in our courses and institutions. First, Gutiérrez (2013) describes how equity for an identity group can fluctuate depending on the context and time frame. As a result, researchers should unpack their justifications for focusing on a specific identity group when conceptualizing equity. For example, researchers will explain that they are studying an identity group (e.g., women or students of color) due to their lack of representation in STEM fields. However, each institution, department, and classroom has its own set of historical origins and structural factors that have shaped the present-day experiences for each identity group. Equity models should describe and embrace these variations ( Hancock, 2007 ).

Additionally, Gutiérrez (2013) comments on how STEM skill sets (e.g., quantitative literacy) are perceived as necessary tools for professional and personal development, creating a system in which individuals who fail to adopt these skill sets are rendered less valuable. These ideological assumptions shape STEM learning spaces as sociopolitical institutions wherein marginalized and minoritized students are blamed for their lack of representation without addressing systemic inequities. Thus, when focusing on ways to center equity in STEM analyses, we suggest that researchers avoid using language that solely focuses on marginalized and minoritized students in relation to their academic outcomes and more so on how their educational experiences are shaped by history, power, and context.

What Analytical and Interpretive Choices Can Be Made to Focus on Excellence?

A popular practice in STEM equity research is to observe the achievement gaps existing between majority and marginalized and minoritized students ( Gouvea, 2021 ). A wealth of research explores how students who belong to underserved racial, gender, ability, and socioeconomic groups underperform academically in comparison to their privileged counterparts (e.g., Bastedo and Jaquette, 2011 ; Matz et al. , 2017 ; Whitcomb and Singh, 2021 ). Focusing solely on gaps is harmful, because it centers students’ identities as the reasons behind their academic failures. Additionally, the research on achievement gaps over the years has not substantially improved the experiences of marginalized and minoritized students in STEM; in fact, research has shown that overreliance on the documentation of these gaps has contributed to negative societal constructions of the academic abilities of students from minoritized backgrounds ( Gutiérrez, 2008 ; Martin, 2012 ). Embracing critical approaches in STEM equity research necessitates that researchers use proactive approaches wherein efforts are pushed toward addressing what institutions can do to better support students.

Rather than framing analyses with gaps, those conducting equity analyses should focus on how different factors positively relate to advancements, gains, and excellence of students ( Gutiérrez, 2008 ; Harper, 2010 ). For example, research shows that LGBTQ+ students experience fewer stressors when they attend colleges that have academic studies, policies, and student clubs supportive of LGBTQ+ individuals ( Woodford et al. , 2018 ). By shifting the onus to institutional components, Woodford et al. (2018) showcase how university programs and policies are directly tied to the success of LGBTQ+ students. Practitioners can use these findings to create supportive institutional and classroom environments for LGBTQ+ students.

We further caution against excellence-based approaches that solely center grades or degree attainment. Despite increases in STEM degrees conferred to racially minoritized students, there still exists a lack of representation in STEM fields ( Fry et al. , 2021 ). Furthermore, the financial and psychological burdens that racially marginalized and minoritized students report while enrolled in and after college suggests that “success” postgraduation is not equitable across all groups ( Keels et al ., 2017 ; Davis et al. , 2020 ; McGee, 2020b ). Students of color often conceptualize “success” as tied to their ability to give back to their communities, which is different from traditional conceptualizations of success ( McGee and Martin, 2011 ; Pérez Huber et al. , 2018 ; Lopez, 2020 ; McGee, 2020a ). As a result, we recommend that researchers adopt definitions of excellence within STEM contexts based on the conceptualizations of their population of interest and then use those definitions in analyses ( Pérez Huber et al. , 2018 ; Weatherton and Schussler, 2021 ).

What Theoretical Linkages Exist between the Constructs and Demographic Variables of Interest?

Too often, quantitative STEM equity analyses are conducted with a “kitchen sink” approach in which full combinatorics are used to study intersections of student constructed identities. Including several independent demographic variables without adequately accounting for past research or theoretical linkages among them hinders the interpretation of research findings. How and why these demographic variables are used in analyses impacts conversations about what inequities look like, for whom, and what should be done.

Recently, scholars have begun to discuss the need for a “race re-imaging” wherein commonly used measures such as motivation or institutional support are re-evaluated and adapted to fit the lived experiences of racially marginalized and minoritized experiences historically left out of educational psychology research ( DeCuir-Gunby and Schutz, 2014 ; Lopez, 2020 ; Matthews and López, 2020 ). Research indicates that racially marginalized and minoritized students’ value in STEM is strengthened through their ability to understand how STEM educational skill sets can uplift their communities ( McGee and Bentley, 2017 ; Gray et al. , 2020 ), countering the individualistic culture of STEM learning environments ( Battey and Leyva, 2016 ; Carter, 2017 ). Therefore, STEM utility and motivational measures that ignore social justice and community engagement may miss out on the ways that STEM and racial identities intersect ( McGee, 2020a ; Miller-Cotto and Lewis, 2020 ). As STEM equity researchers, we can apply these ideals to questions of pre-existing assumptions we may hold about the relationships between our constructs of interest and different demographic variables (i.e., gender, socioeconomic status, ability, and sexuality) selected for our studies before running analyses. Then, if deficit-based theoretical linkages emerge, we recommend researchers find outside studies promoting strength-based approaches or adopt qualitative or mixed approaches that can better speak to the associations between the demographic variables and constructs of interest.

What Should Be Considered when Using Standardized Test Scores as a Metric for “Prior Preparation”?

Standardized test scores (ACT/Scholastic Aptitude Test [SAT]) must be incorporated cautiously considering who is and has been most likely to do well on them given structural inequalities that privilege certain families over others ( Rothstein, 2004 ; Soares, 2007 ; Zwick, 2013 ; Carnevale et al. , 2020 ). As researchers focused on equity, we must acknowledge the racist origins of standardized assessments. In the early 1900s, standardized assessments were intentionally used by eugenicists as justifications for racial purity in American educational systems ( Lemann, 2000 ; Harris et al. , 2011 ; Soares, 2007 ). Today, standardized assessments are still used in admissions decisions and placement into undergraduate STEM courses, even though research has shown that they are weak and inadequate predictors of college retention for racially minoritized students ( Sedlacek, 2004 ). Although many institutions have either modified admissions policies to be test optional or completely eliminated standardized tests in admissions review due to the impact of the COVID-19 pandemic, how long institutions will continue with such policies and what possible alternative assessments may be used in place of ACT and SAT test scores remains to be seen. Further, students who come from economically privileged families have access to high schools and test preparation resources that increase their chances of doing well on standardized assessments. The economic privileges continue once these students enter higher education ( Borg et al. , 2012 ; Carnevale et al. , 2020 ). As a result, causal linkages between standardized assessments and degree attainment generally fail to account for wealth as a confounding variable. Therefore, we encourage the use of other metrics to capture the academic preparation of students.

College course grades and high school grade point average (GPA), while also imperfect measures, are stronger predictors for student adjustment and success in college over standardized test scores ( Byrd et al. , 2014 ; Koester et al. , 2016 ; Galla et al. , 2019 ). Unlike standardized assessment scores, a student’s high school course grades and college course work better reflect the level of mastery for a given subject. Additionally, researchers could consider using Advancement Placement (AP) scores. The AP program provides high school students with the chance to engage in college-level curricula ( Kolluri, 2018 ). Research has shown that passing AP tests is related to positive college outcomes across students from different racial and socioeconomic backgrounds ( Dougherty et al. , 2006 ; Fischer et al. , 2021 ). It is important to note that even these metrics are not perfect indicators of academic preparation, given the intersecting inequalities that exist in K–12 educational systems (see Lewis and Diamond, 2015 ). Therefore, researchers should account for the current limitations that exist in how we assess students’ prior academic preparation.

What Measures Capture Structural Inequalities That Exist in STEM Higher Education?

Equity analyses that only use individual-level variables provide great insight into how academic outcomes vary across different social groups. However, interpretations that come from these types of analyses often place sole responsibility on minoritized and marginalized students to persevere through systemic barriers ( McGee, 2020a ). Using complex multilevel models, researchers have assessed the impacts of various structural components on student outcomes, such as campus and classroom climate, policies, and institutional characteristics (e.g., selectivity and public vs. private status; Espinosa, 2011 ; Leath and Chavous, 2018 ; Ohland et al. , 2018). For example, Espinosa (2011) found that women of color who attended private colleges were more likely than their peers enrolled at public institutions to persist in their STEM programs. Espinosa (2011) attributes the positive effect of private institutions to the large amounts of educational resources available that counteract a lack of academic preparation among women of color. Espinosa (2011) further showcases that experiences for women of color vary based on the STEM contexts in which they are situated. In contrast, Leath and Chavous (2018) show that Black women enjoy college less when they feel like they must conceal their racial and ethnic identity. Leath and Chavous (2018) demonstrate how tumultuous racial climates contribute to Black women’s college experiences. These studies allow researchers to gain insight into the underlying mechanisms and structural components (e.g., type of college and racist campus climate) that contribute to student experiences, persistence, and success. Also, these researchers illustrate a story in which the institution is held accountable for variations in student outcomes ( Hancock, 2007 ).

There are, however, limitations to these approaches. For one, institutional climate measures may aggregate students’ perceptions about how well students from different backgrounds get along with one another. Although these measures are reflective of structural components, they still rely on individual-level perceptions and do not generally account for different conceptualizations of climate across social groups. Second, multilevel models require large sample sizes at the individual level to maintain statistical power ( Snijders, 2005 ), but researchers attempting to collect information from a diverse set of participants are often blocked by a lack of financial resources and time ( Hancock, 2007 ). As a result, researchers may be forced to make difficult analytical decisions like aggregating multiple social groups together (e.g., combining non-white students) that gloss over the variation between different groups of students and hinder our understanding of how structural inequalities on campus, and in STEM programs specifically, differentially impact their experiences and outcomes. Additionally, because STEM equity researchers often find themselves working with institutional data, individual variables may be the only ones available, and as we describe in more detail later, may not include documentation about how these data were collected, which can influence modeling strategies and subsequent interpretations.

As a result, we emphasize that models based only on individual-level variables (e.g., race, gender, and ability) can only suggest variations across existing groups; they are not explanations for the underlying mechanisms that influence these variations. Ultimately, much work remains in delineating the best practices for integrating structural features into analyses and appropriately contextualizing them within STEM equity research. In the meantime, we recommend that researchers do their best to incorporate structural components in analyses wherever possible.

How Do Changes in Institutional Categories for Demographic Variables over Time Affect Analyses?

When working with institutional data, researchers may need to track how their institutions’ social categories have changed over time ( Viano and Baker, 2020 ; Byrd, 2021 ). Categorizations like gender, race and ethnicity, income, and parental education are not fixed; these categories fluctuate over time, even if slowly. As a broad example, the race and ethnicity categories on the U.S. census that inform data collection across society have changed with every census administration ( Brown, 2020 ). At one time, for example, Irish immigrants were not viewed as “white” due to a few factors including their socioeconomic position and religious beliefs, but as the Irish gained economic mobility in a deepening Jim Crow era, their ascension to whiteness was solidified in the United States ( Omi and Winant, 2015 ). Here we see that race is not static, but a by-product of social and political change.

In addition to race and ethnicity, gender categorizations in the United States have also evolved, with social surveys moving beyond binary conceptualizations and shifting toward more gender-inclusive (i.e., transgender, gender non-conforming, nonbinary) categories ( Westbrook and Saperstein, 2015 ). Indeed, such variation has always existed ( D’Ignazio and Klein, 2020 ), and it is important to note that individual perceptions of social categorizations are also subject to change ( Freeman et al. , 2011 ). Similarly, sometimes the same information is collected about students in multiple contexts (e.g., when both the financial aid and registrar’s office have information about students’ first- or continuing-generation status). Identifying areas of discordance from different data-collection mechanisms over time can more properly contextualize analyses, particularly when merging multiple data sets for the same students.

Consequently, when working with secondary data sources, we recommend that researchers seek to obtain information about how social categorizations were solicited and defined as well as how they may have changed over time. Including this information in studies, even if only as supplemental material, will help to produce research that is better contextualized. Researchers should also reach out to campus offices that maintain and analyze student-level data for additional student information that may not be included in existing data sets to improve clarity about how groups are constructed and how this might influence analyses and interpretations.

Are Quantitative Analyses the Best Tools for Answering the Proposed Research Questions?

Using qualitative and mixed-methods approaches, scholars have provided in-depth commentary on the ways that systemic inequities have shaped marginalized and minoritized students’ experiences in STEM contexts ( McGee and Bentley, 2017 ; Allaire, 2019 ). At the same time, the rise of big data has encouraged educational systems to examine the experiences of marginalized and minoritized students at the macro level ( Daniel, 2019 ). Although quantitative approaches offer unique benefits, some research questions require alternative approaches to better capture the lived experiences of minoritized students ( Covarrubias, 2011 ). For example, Jack (2019) studied the experiences of low-income students at elite institutions, showing that those who graduated from private high schools were able to navigate elite institutions better than their low-income peers who attended public high schools in their communities. A common practice in educational research tends to clump the experiences of low-income students together when studying inequity. Jack (2019) demonstrates how qualitative research has the power to capture variations within groups not easily noticeable when groups are combined in quantitative analyses. Therefore, before conducting analyses, we recommend that researchers first identify the main goals of a research project and assess whether quantitative analyses are most applicable and viable given the data available or to be collected, regardless of sample size.

Although higher education has contributed to the advancement of society, our institutions have also participated in creating and reproducing systemic inequities ( Patton, 2016 ). Our institutions, as well as the research community, can and should play a role in making the experiences of all students more equitable by first examining, with the students themselves, what those experiences are that can inform campus decision making. The misuse of quantitative data in STEM equity analyses can, even when unintended, reinforce deficit interpretations about marginalized and minoritized students and mask the role of systemic inequities. Integrating critical approaches in STEM equity analyses can provide insight into how institutions bear responsibility for the lack of diversity, representation, and differential experiences in STEM fields reflecting an unequal opportunity structure on our campuses.

In this essay, we aimed to inspire those conducting STEM equity research from a quantitative perspective to commit to an act of revision ( Laymon, 2021 ). As researchers in STEM education, we understand that career success is often dependent upon one’s ability to adopt beliefs that research and numbers are objective. We also know that researchers are encouraged to search for “silver bullets” or universal approaches in their work. In fact, we still fail at upholding all of the recommendations we have offered. However, understanding the value that statistical practices have in equity policy initiatives, we are committed to working through present-day limitations that come with the quantification of human experiences. By being upfront where our work falls short, we get closer to discovering new analytical approaches that can be used for liberatory purposes. Finally, we hope to contribute to a critical discourse and prompt reflection in an effort to make a meaningful impact that ultimately promotes equity and inclusion on our campuses and in STEM fields.

Acknowledgments

We thank Juniar Lucien for organizational contributions and insightful feedback. Additionally, this work was supported by the SEISMIC project (seismicproject.org), which is funded by the Alfred P. Sloan Foundation. SEISMIC is managed at the University of Michigan for the participating institutions, which include Arizona State University, Indiana University, Michigan State University, Purdue University, University of California Davis, University of California Irvine, University of California Santa Barbara, University of Michigan, University of Minnesota, and University of Pittsburgh.

1 For the purposes of this paper, we define minoritized and marginalized students as those who belong to identity groups that have been impacted by structural inequities (e.g., racism, sexism, and ableism) and are less represented in STEM in comparison to the American population ( National Science Foundation, 2021 ).

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• Open access
• Published: 21 September 2018

Students’ perceptions of STEM learning after participating in a summer informal learning experience

• Thomas Roberts   ORCID: orcid.org/0000-0002-9521-5877 1 ,
• Christa Jackson 2 ,
• Margaret J. Mohr-Schroeder 3 ,
• Sarah B. Bush 4 ,
• Cathrine Maiorca 5 ,
• Maureen Cavalcanti 6 ,
• D. Craig Schroeder 7 ,
• Ashley Delaney 2 ,
• Lydia Putnam 3 &
• Chaise Cremeans 8  

International Journal of STEM Education volume  5 , Article number:  35 ( 2018 ) Cite this article

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Informal learning environments increase students’ interest in STEM (e.g., Mohr‐Schroeder et al. School Sci Math 114: 291–301, 2014) and increase the chances a student will pursue a STEM career (Kitchen et al. Sci Educ 102: 529–547, 2018). The purpose of this study was to examine the impact of an informal STEM summer learning experience on student participants, to gain in-depth perspectives about how they felt this experience prepared them for their in-school mathematics and science classes as well as how it influenced their perception of STEM learning. Students’ attitudes and perceptions toward STEM are affected by their motivation, experience, and self-efficacy (Brown et al. J STEM Educ Innov Res 17: 27, 2016). The academic and social experiences students’ have are also important. Traditionally, formal learning is taught in a solitary form (Martin Science Education 88: S71–S82, 2004), while, informal learning is brimming with chances to connect and intermingle with peers (Denson et al. J STEM Educ: Innovations and Research 16: 11, 2015).

We used a naturalistic inquiry, phenomenological approach to examine students’ perceptions of STEM while participating in a summer informal learning experience. Data came from students at the summer informal STEM learning experiences at three diverse institutions across the USA. Data were collected from reflection forms and interviews which were designed to explore students’ “lived experiences” (Van Manen 1990 , p. 9) and how those experiences influenced their STEM learning. As we used a situative lens to examine the research question of how participation in an informal learning environment influences students’ perceptions of STEM learning, three prominent themes emerged from the data. The informal learning environment (a) provided context and purpose to formal learning, (b) provided students opportunity and access, and (c) extended STEM content learning and student engagement.

Conclusions

By using authentic STEM workplaces, the STEM summer learning experience fostered a learning environment that extended and deepened STEM content learning while providing opportunity and access to content, settings, and materials that most middle level students otherwise would not have access to. Students also acknowledged the access they received to hands-on activities in authentic STEM settings and the opportunities they received to interact with STEM professionals were important components of the summer informal learning experience.

In the United States, we currently face a shortage of science, technology, engineering, and mathematics (STEM) majors and graduates (National Science Board 2016 ; The Committee on STEM Education National Science and Technology Council 2013 ) while at the same time STEM occupations are expected to grow (Langdon et al. 2011 ; U.S. Bureau of Labor Statistics 2018 ). This two-fold issue necessitates STEM education in the U.S. becomes and remains a priority. According to the National Research Council ( 2011 ), this priority must include broadening women’s and minorities’ participation in STEM and increasing STEM literacy for all students, regardless of whether they plan to pursue a STEM major or career. Informal learning environments have been shown to increase students’ interest in STEM (e.g., Mohr-Schroeder et al. 2014 ) and have been shown to increase the chances a student will pursue a STEM career (Kitchen et al. 2018 ; Kong et al. 2014 ).

Researchers identify interest and motivation as important components in inspiring students to pursue STEM learning because it contributes to students’ learning and success in retaining STEM content (Bell et al. 2009 ). In response to President Barack Obama’s Call to Action (The President’s Council of Advisors on Science and Technology (PCAST 2010 ), states, school districts, and individual schools, as well as researchers in the United States (U.S.) have increased their focus on improving students’ motivation and interest in STEM, particularly at the middle school level. According to Brown et al. ( 2016 ), the educational deficit in STEM areas has led to a workforce gap in many STEM professions. Informal learning environments can support student STEM knowledge and skills (e.g., Denson et al. 2015 ), positively impact student interest in STEM (e.g., Denson et al. 2015 ; Mohr-Schroeder et al. 2014 ), and increase the likelihood to pursue a STEM career while attending college (Kitchen et al. 2018 ; Kong et al. 2014 ).

Targeting elementary and middle school students for STEM

Studies have shown that students who have an increased interest in science, mathematics, and engineering in the early years of their education are more likely to pursue that interest resulting in a STEM-related career (After-School Alliance 2015 ). Unfortunately, before the eighth grade, many students have concluded that the STEM subjects are too challenging, boring, and/or uninteresting (PCAST 2010 ), which limits their participation in STEM subjects and activities. Research has shown the importance of motivating students to learn STEM content in the elementary and middle grades. “Students who express interest in STEM in eighth grade are up to three times more likely to ultimately pursue STEM degrees later in life than students who do not express such an interest” (PCAST 2010 , p. 19). Research has shown that students’ learning is delayed during summer breaks (McCombs et al. 2011 ), and students from low-socioeconomic households have more knowledge loss during summer months due to their lack of access to summer learning. Furthermore, summer learning deficits are accumulated and by ninth grade, two-thirds of the receivement gap (Chambers 2009 ) among low socioeconomic students can be attributed to unequal access to summer learning experiences (Alexander et al. 2007 ; McCombs et al. 2011 ). Therefore, it is imperative to prepare and inspire each and every student, specifically students of color, females, and students from low socioeconomic backgrounds, to learn STEM (PCAST 2010 ).

Informal learning experiences

Informal STEM learning experiences have the potential to support students’ learning and engagement in a formal STEM learning environment. Informal STEM learning experiences address the limitations of the formal school experience by providing opportunities (Bell et al. 2009 ; Meyers et al. 2013 , Popovic and Lederman 2015 ) that build students’ awareness of and interest in the STEM fields. Students who struggle in the formal and more traditional STEM courses tend to be more interested and motivated in STEM when it is presented in a more engaging, hands-on way. Informal STEM learning environments are naturally composed in a way to promote learning through real-world modeling and examples (Martin 2004 ; Meredith 2010 ; Popovic and Lederman 2015 ). Informal STEM learning environments also help students understand concepts and their ability to recall information (Allsopp et al. 2007 ; Popovic and Lederman 2015 ). Participation in short-term STEM summer experiences (Bell et al. 2009 ; Kitchen et al. 2018 ; King 2017 ; Mohr-Schroeder et al. 2014 ) or other long-term informal STEM programs (After-School Alliance 2011 ; Baran et al. 2016 ; Barker et al. 2014 ; Klanderman et al. 2013 ; Massey and Lewis 2011 ) have been shown to increase students’ interest in STEM.

Factors that influence students’ perceptions of STEM

Students’ attitudes and perceptions toward STEM are affected by their motivation, experience, and self-efficacy (Brown et al. 2016 ; Turner and Patrick 2004 ; Weinberg et al. 2011 ). Brown et al. ( 2016 ) studied the relationships between STEM curriculum and students’ attitudes and found student interest played a more important role in intention to persist in STEM when compared with self-efficacy. These discrepancies may be remedied by exposing students to a greater longevity of experience with activities which foster self-determination and interest-led, inquiry-based projects (Boekaerts 1997 ; Honey et al. 2014 ; Moote et al. 2013 ).

The academic and social experiences students’ have are also important. More specifically, middle level students are especially impacted by peers because:

During adolescence, students are often reluctant to do anything that causes them to stand out from the group, and many middle-grades students are self-conscious and hesitant to expose their thinking to others. Peer pressure is powerful, and a desire to fit in is paramount. (NCTM, 2000 , p. 268)

Traditionally, formal learning is taught in a solitary form (Martin 2004 ), while informal learning is brimming with chances to connect and intermingle with peers (Barker et al. 2014 ; Denson et al. 2015 ; Klanderman et al. 2013 ).

Many educators approach work with students through a problem-solving framework to develop positive STEM perceptions. The STEM Task Force Report ( 2014 ) argued for the use of problem-solving and project-based frameworks because of their use of “real-world issues [which] can enhance motivation for learning and improve student interest, achievement, and persistence” (p. 9). Important to the positive STEM perception development of underrepresented students in STEM are opportunities to participate in authentic STEM learning experiences. For these reasons, a need exists for informal learning environments, such as the See Blue See STEM model , to provide students with meaningful exposure to a STEM community in which to participate, practice, and belong (O'Connell et al. 2017 ).

Our work directly aligns to the priorities outlined by the National Research Council as we provide informal STEM learning opportunities to elementary and middle school students—focusing on students from underrepresented populations in STEM (i.e., Black, Latinx, Native American, and females). We believe in STEM literacy for each and every student is feasible and can be supported by access and opportunities to authentic learning experiences. The purpose of this study is to examine the impact of an informal STEM summer learning experience on student participants, to gain in-depth perspectives about how students perceived this experience prepared them for their in-school mathematics and science classes as well as how it influenced their perception of STEM learning. The research question for this study was: How does participating in an informal learning environment influence middle level students’ perceptions of STEM learning?

Theoretical framework

To examine how participation in an informal learning environment influences students’ perceptions of STEM learning, we used situated learning theory. Situated learning and related theoretical perspectives (i.e., cognitive apprenticeship) have been utilized in investigating the connections between informal learning and STEM education (e.g., Larkins et al. 2013 ). More generally, situated learning has been used to study learning and attitudes toward STEM (e.g., Guzey et al. 2016 ). Applied to perceptions of STEM learning, such a theoretical lens allows the experiences of students to be explored in the context of the authentic activity where students experience STEM learning.

Central to the situated perspective is how interactions between learner and environment (Brown et al. 1989 ; Kirshner and Whitson 1997 ; Lave 1991 ; Lave and Wenger 1991 ), mediated by social interactions create opportunities for learners to acquire knowledge. The learning that occurs arises from legitimate peripheral participation (Lave 1991 ; Lave and Wenger 1991 ) in authentic activity (Brown et al. 1989 ). Through opportunities to acquire and apply knowledge and practice skills, learners develop deeper understandings (Brown et al. 1989 ) from the meaningful context in which those opportunities exist (Luehmann 2009 ; Sullivan 2008 ). The community is an essential element of the meaningful context and is a powerful vehicle for transforming perspectives and understandings (e.g., Johri and Olds 2011 ). Informal learning promotes access and opportunities to participate in authentic STEM learning, and therefore, influences perceptions (NRC 2009 ).

Situated STEM learning results from an integration of STEM content within a community practice where “learning is authentic and relevant, therefore representative of an experience found in actual STEM practice” (Kelley and Knowles 2016 , p. 4). The See Blue See STEM model is one such informal environment, with targeted efforts to reach student populations underrepresented in STEM and capitalize on the transformational potential of engaging students in hands-on interactive sessions with STEM professionals. The STEM teaching and learning summer experience mirrors that of the work of professionals in the field. Employing a situative perspective in this study provides a context for broadening understanding of how authentic experiences in an informal environment can transform students’ perceptions toward STEM learning across contexts.

We used a naturalistic inquiry, phenomenological approach to examine students’ perceptions of STEM while participating in a summer informal learning experience. Naturalistic inquiry, falling in the constructivist paradigm, allows for multiple realities to be created by the students (Lincoln and Guba 1985 ). The meanings students create are constructed by their participation in specific settings (Crotty 1998 ). The phenomenological approach allowed us to focus on the “lived experience” of student participants in an informal learning environment (Creswell 2014 ). Students’ participation in the informal learning environment allows for the meaning students place on their experiences to be investigated (Merriam 2009 ).

See blue see STEM summer informal learning experience model

The See Blue See STEM Summer Experience is a 1-week summer informal STEM learning experience for middle level students. Founded in 2010 in Kentucky, the See Blue See STEM model provides a variety of STEM content experiences for students to participate during the summer to spark their interest in STEM. The See Blue See STEM model’s goal is to expose middle level students, particularly underrepresented populations, to a variety of STEM fields and STEM professionals in their workplace environment through authentic, hands-on instruction to increase students’ interest in a STEM career. The See Blue See STEM model was named a Top 5 model for Broadening Participation at the 2015 EPSCoR National Conference (Mohr-Schroeder 2015 ), and was replicated at Iowa State University and California State University—Long Beach during Summer 2017.

Throughout the See Blue See STEM model, focus is given to ensure high-quality, authentic, hands-on sessions with STEM content faculty from Colleges of Engineering, Education, Arts and Sciences, Medicine, as well as STEM professionals and/or informal STEM learning partners. The selection of presenters, which varies from year-to-year, provides opportunities for students to engage in a variety of STEM fields in their authentic research environments. The eight mathematical practices (NGA Center for Best Practices and CCSSO 2010 ) and the eight science and engineering practices (NGSS Lead States 2013 ) are present throughout the sessions.

In the See Blue See STEM model, all students participate in robotics (e.g., Lego Mindstorm EV3, Vex) or EDISON, which provides an engaging and motivating platform for students to actively build, explore, investigate, inquire, and communicate together to develop their programming and problem-solving skills. Curriculum topics and content are different each year in order to allow repeating students to have exposure to a variety of STEM content. In addition, the students engage in different content sessions each day. Students engage in robotics or EDISON every day for 3 hours and content sessions the other 3 hours of the day (see Table  1 for a sample 2-day schedule). This model, similar to Kelley and Knowles’ ( 2016 ) approach to STEM, allows students to work in a community of practice that is situated in authentic contexts and facilitated by a STEM expert.

For example, California State University-Long Beach students completed the Follow the Flow Challenge with local engineers from a community partner organization. The local engineers from a community partner introduced engineering and described the career paths and college courses they took to become engineers. Then they introduced the challenge, Follow the Flow , where students designed and built “a water flow system to move beads through terraced layers” (Finio 2018 ). The engineers engaged with the students and supported them as they completed the engineering design process. As students designed, built, and tested their structures, the engineers fostered their thinking and allowed them to engage in both the Standards for Mathematical Practice (e.g., attend to precision) and NGSS Science and Engineering Practices (e.g., planning and carrying out investigations).

At the University of Kentucky, students modeled with 3-D pens. This session was facilitated by a professor of mathematics education, and the students used 3-D pens to create a variety of three-dimensional mathematical shapes (e.g., cube, dodecahedron, pyramid). Once students created and named the shapes, they were challenged to design structures that incorporated those shapes. The students designed, built, and improved their structures while attending to the mathematically important shapes they were using. This allowed them to engage in the engineering design process while also utilizing the SMPs (e.g., modeling with mathematics and using appropriate tools strategically) and the NGSS Science and Engineering Practices (such as planning and carrying out investigations and obtaining, evaluating, and communicating information) in a community of practice.

These examples illustrate the pedagogical approach used within the See Blue See STEM model. The students engage in authentic activities that are facilitated by experts in the field. Students work in a community of practice to plan, create, and refine their ideas by using the engineering design process. Technological tools are used when appropriate, such as the 3-D pens to create structures. Mathematics and science content knowledge is applied, while the emphasis is placed on the practices of these domains. In other words, students are engaging in the processes that are important components of the disciplines.

Participants

In order to recruit students to attend the summer informal STEM learning experience, an informational flyer and website address is sent out via statewide listservs and to middle schools in the region where the summer experience is held. Although the summer experience is open to all students, the camp focuses on attracting underrepresented populations in STEM fields, especially females and students of color. We define underrepresented populations in STEM fields as female, Black, Hispanic/Latinx, American Indians or Alaska Natives, and Native Hawaiians or Other Pacific Islanders (National Science Foundation 2017 ). The directors of the summer learning experiences work directly with family resource directors at each of the area high needs elementary and middle schools in order to identify and specially invite underrepresented students. These students are guaranteed a place in the camp, provided a scholarship based on financial need, and provided transportation, if needed, to and from camp.

The summer informal STEM learning experience was comprised of incoming 5th–8th graders at all three sites. According to students’ self-identified data, the one institution’s summer experience population between 2012 and 2017 was 39% females, 8% Black, 5% Asian, 1% Hispanic/Latinx, 75% White, 5% other (e.g., mixed race), 6% no response, and 43% from underrepresented populations in STEM. The second institution’s summer experience population in 2017 was 55% females, 36% Black, 6% Asian, 39% Hispanic/Latinx, 15% White, and 3% other (e.g., mixed race), and 91% from underrepresented populations in STEM. The summer experience population in 2017 at the third institution was 59% females, 76% Hispanic/Latinx, 12% Asian, 12% other (e.g., mixed race), and 94% from underrepresented populations in STEM.

Data collection

Data for this paper came from students at the summer informal STEM learning experiences at the three diverse institutions across the United States. Data were collected from reflection forms and interviews which were designed to explore students’ “lived experiences” (Van Manen 1990 , p. 9) and how those experiences influenced their STEM learning. During the last 2 days of the week of the summer informal learning experience, student participants, for which we had IRB consent and assent, participated in a semi-structured interview lasting approximately 5 min each. The interview protocol was refined by the authors year to year to better ascertain students’ experiences and perceptions (see Table  2 for the latest interview protocol). The interviews were conducted during the 2015, 2016, and 2017 summer informal STEM learning experiences. Over 40% of students were interviewed to gain an understanding of students’ perceptions of STEM, what they enjoyed most about the STEM learning experience, what was most challenging, and how the informal learning experience will help them in their STEM classes in a formal school setting. The interviews were audio recorded. The interviewer also took notes to conduct member checks during and at the end of the interview.

In addition, the student participants completed a session reflection form (Fig.  1 ) at the end of each STEM content session (i.e., once a day). The STEM content session reflection was a handwritten by the students and were given to the students during the 2012, 2013, 2014, 2015, 2016, and 2017 summer informal STEM learning experiences. The purpose of the form was to collect students’ perceptions of the STEM content session, what the students learned, and provide feedback to the presenters. This data collection process occurred across all three sites. The final data set for this paper consisted of 320 qualitative artifacts. Of the 320 artifacts, 254 were unique interview transcripts from students across all 3 sites, with the majority (85%) coming from the founding site. Seventy-eight percent (197 of 254) of the students interviewed were from underrepresented populations in STEM. The remaining artifacts (66) were session reflections from across all 3 sites, with the majority (85%) coming from the founding site.

Daily reflection and feedback form the students completed after each session

Data analysis

All data were transcribed and a pseudonym was assigned to each participant. In order to create a reflection artifact, we took the transcribed session reflections from each unique participant in a content session offered in the summer informal learning experience and created one document with all participant reflections contained within it. For example, for the engineering design session at one university, all session reflections for that content session were combined together into one document to create a rich artifact that would help the authors draw out the “lived experience” of the students during that particular session.

We used an inductive approach to analyze the data, which incorporated systematic methods of managing data through reduction, organization, and connection (Dey 1993 ; LeCompte 2000 ). One member of the research team used initial coding to develop an early code list (Saldaña 2016 ). The initial coding primarily employed descriptive coding, “summarizing in a word or short phrase… the basic topic of a passage of qualitative data” (Saldaña 2016 , p. 102). During this first cycle coding process, the descriptions began to paint a picture of the students’ most salient perceptions related to their participation in the summer informal STEM learning experience.

We, then, used the preliminary codes to establish further codes, which were used to code an initial set of interview transcripts and reflections. The entire author team then met to review the list of codes and revise the codes as necessary. All disagreements were discussed until a consensus was reached. Once a consensus was reached on the codes, a subset of the researchers ( n  = 4) coded the interviews and reflections using Dedoose (Dedoose Version 8.0.35 2018 ). Inter rater and intra rater reliability standards were set at 90% agreement. All four researchers exceeded the threshold of 90% agreement on both intra rater (ranged from 91 to 94%) and inter rater reliability (94.3%) which exceeded the minimum threshold of 90% needed for reliability analyses (James et al. 1993 ).

After the data were coded, four of the researchers conducted second cycle coding by pattern coding to appropriately group and label similarly coded data as a way to attribute meaning (Saldaña 2016 ). Pattern coding helped the researchers identify common themes, as well as divergent cases, looking across categories to see if there are underlying patterns to the responses (Delamont 1992 ). Once the initial themes were drafted, the entire research team reviewed the themes and supporting data to add clarity and content validity to the themes. During this review process, important questions were raised about the appropriateness of the themes and whether they were well supported. This process resulted in further identification of metathemes. All discrepancies were resolved during the final development of the overall themes.

Results and discussion

As we used a situative lens to examine the research question of how participation in an informal learning environment influences students’ perceptions of STEM learning, three prominent themes emerged from the data. The informal learning environment (a) provided context and purpose to formal learning, (b) provided students opportunity and access, and (c) extended STEM content learning and student engagement.

Context and purpose to formal learning

During the STEM summer learning experience, students programmed Lego robotics and completed several challenges. They were able to witness the applicability of STEM content. Jude explained, “I learned how to program and make robots, and I also learned how to use science and technology and math and engineering to build them” (Interview 2017). The students were at ease learning the STEM disciplines because they knew they were not learning the content in isolation. For example, Janae stated, “I learned a lot about mathematics. Like, the robotics. There’s a lot of logic in it, you know.” (Interview 2016). Luis further elaborated he had to “calculate how far it [the robot] is from the wall. And how far from the object… It [taught] me how to measure things more like without really thinking about it that much” (Interview 2016). The students recognized the importance of the STEM disciplines, and applied those skills to accomplish specific tasks during the robotics sessions.

Not only were the students able to apply STEM content to solve problems, they were able to see how what they were learning during the STEM summer learning experience was preparing them to be successful in the formal school setting. For example Kya explained,

I’m really interested in science and math, and so this place really helped me get ready for this year, this coming school year. I learned [many] things about science than I thought I would because almost every material is a polymer. I saw what smoking can do to your lungs and that is going to help me with health this year. Because my health teacher she like talks about how to stay healthy, what not to do, we have a conversation about drugs in the middle of the year. And so this is really going to help me. (Interview 2017)

Kya realized she would be able to take the knowledge she gained from camp and use it in her science and health classes. Similarly, James argued that one cannot do engineering without mathematics.

Engineering also focuses on math–like how if you measure a plane, if you measure the length or width of a plane, it shows… like the length and width, like base times height, length times width, stuff like that. And then you can do the math. Cuz [sic] in order to build a plane [you have] to do math, so it shows you different ways to do math problems while doing fun things. (Interview 2015)

It is important to note that students expressed they did not understand why they had to learn mathematics or science in school. To them, these subjects were disconnected from the real world, and they had to take the classes because that was what they were told they had to do in school. However, Erin articulated the importance of knowing the applicability of the discipline.

It’s helping me, and like showing me when I will need to use that math in real life problems, and it’s like helping me like understand why we need to learn math because I don’t like math very much. (Interview 2016)

The STEM summer learning experience provided a reason for students, like Erin, to learn subjects like mathematics in school, particularly for students who do not necessarily like the subject. Leslie agreed with Erin on how the camp provided meaning to the mathematics they were learning in school.

It's incorporating some of the math we've already learned into like STEM.It's giving us different ways to like apply the math that we will learn. So that we know why different equations or whatever what they’re for engineering would use some of the stuff like how to apply it into everyday life. So, it kind of gives meaning behind it. (Interview 2015)

The applicability of the activities completed during the STEM summer learning experience not only provided more context for the subjects students have in school, it also gave credence to help students understand why they learn the subjects in school. Luis commented the STEM summer learning experience helps “me in math classes because it gives me different scenarios to work with, and it helps me look at the problem in different ways not just in the same ordinary way” (Interview 2016). David realized mathematics involves more than understanding the basics. He suggested, “math isn’t all about like just 1 plus 1 stuff. It also involves calculating lots of things, not just equations” (Interview 2016). Shelby summarized the sentiments of many students in the camp,

I guess I feel like it’s giving us new ways to understand and see things, and it’s giving us things that we haven’t learned about; we’re just kinda getting it into our brains so we’re more prepared for our science classes. And, I just feel like it’s preparing us for what what’s going to be ahead of us and giving us ways to see things. (Interview 2016)

The students’ perceptions of the activities helped them to not only understand the purpose of the content they were learning, but realize the connections to what they are learning during the STEM summer learning experience can help them excel in the subjects they learn in school. In other words, providing the same content in a new environment was a catalyst for a positive change in how the students perceived future STEM content.

Opportunity and access

Students recognized access to STEM curriculum and materials was often limited because of funding and resources in public schools. Cristina pointed to the lack of technology in her small, rural school as a major barrier to accessing STEM content. “I don’t have a lot of robotics in my school or computer things, and so I don’t learn a lot about these topics” (Interview 2017). Other schools offered STEM content such as robotics as an elective or after school club. However, this prohibited some students with working parents because “the schedule would be weird,” (Shelby Interview 2016) even though the student may “just love programming” (Shelby Interview 2016). The STEM summer learning experience provided access to robotics and other activities often offered outside of the school day or during times when students may have to choose between fine arts, academic support services, and other electives.

Students also commented their access to rigorous core content was also lacking in the formal school setting. Several students commented they had limited exposure to STEM because STEM was not part of their curriculum, and many stated they have science class “only once a year so [they] don’t really do anything” (Luis Interview 2016). However, other students stated they did have STEM as a part of their school curriculum, but were yearning for challenging content and an opportunity to dive deeply into STEM learning. Hilda explained that in schools, a teacher may not be able to “go into super detail just so she can get everything done in the year” (Interview 2017). The students were excited about the opportunities and access the STEM summer learning experience provided because they were “learning things [they] didn’t know before” (Jude Interview 2017). They were “learn[ing] new stuff and visit[ing] new stuff that [they have] never been before” (Frances Interview 2017). Students remarked about “want[ing] to learn more. That’s why [they] came back” (Melsia Interview 2015). When provided the opportunity to access STEM, students were engrossed in the learning and eager to experience the activities. For example, accessing STEM content inspired students to think about potential applications in the real world. After engaging in a session discussing the development and use of virtual reality (VR), Anthony noted VR could help:

...teaching other students about other worlds. Like, such, like not other worlds, other places, area. Like under the ocean or in space. We could, we could really use that in classrooms and sometimes even at home too with your, with parents if they sort of like, kind of forget some, some useful information. We could help them by using the VR. And we could probably add triggers to the VR and hand motions to see your hand, like an avatar hand so that we could see, pick up, and build some of our things. (Interview 2017)

A STEM concept previously perceived as science fiction was now a learning tool that could be evaluated and improved upon. Beyond connecting to formal classroom learning, students were also making connections between their experiences during the STEM summer learning experience and the application of those experiences to their personal lives and the real world. Once students have access to “all this in action, and [they can] see how it applies to real life” (Tamara Interview 2016), it is transformative.

The STEM summer learning experience partners are from a range of professions providing students experiences that are both broad and rich. The pedagogical approach of the STEM summer learning experience balanced guided learning and student exploration. Instead of sessions where professionals imparted knowledge to students, they were engaged in “little activities that they have that help [students] learn easily” (Shaun Interview 2016). Students remarked this was an essential element to their rich learning experience. Students had access to deep experiences with robotics and coding in the mornings and “the afternoon sessions are different for [them] every day, with different professors” (Leslie Interview 2015). Their access was not just brief encounters with STEM professionals. Students were spending hours with professors, STEM career professionals, and college students engaging in their real work in an authentic setting. One of the most discussed experiences was a field trip to an alternative energy research center. Simone remarked, “I think that was pretty cool because we got to walk around and kind of engaging conversations and stuff with professionals. So… and I learned a lot too, so that was fun” (Interview 2016). For many students, work with STEM professionals humanizes and normalizes the individuals. Denise reflected,

I’ve learned a lot here over the past couple of days. What I’m probably going to take with me is how there’s different types of people, and we’ve kinda gone over the fact that most people outside of like engineering world they think that scientists and engineers are people that don’t really have friends or are kinda secluded. But I’ve kinda learned that it’s not like that at all. They’re just normal people who do normal things like everyone else. (Interview 2017)

Adri also stated, “It’s really fun and it’s cool to see like campus and like what some people do as their jobs. And to learn that you could do that too” (Interview 2017). Learning about professionals’ “job and about their life story and how they got to where they were at” (Michael Interview 2017) brings them down from the pedestal and onto an equal playing field. In other words, it makes the professional attainable to the students.

STEM content learning and student engagement

Students had an opportunity to experience activities they never experienced such as programming robots, cutting pigs, and playing with flies. These experiences extended their STEM content knowledge and piqued their interest and engagement. Several students expressed they really enjoyed doing the hands-on activities because that is how they learn. Shalea articulated,

I mean at my school we don’t really have many hands-on activities. It’s more of visual and audio learning. Like we do a lot of tests; we do a lot of things like that. And we really don’t get to do hands-on experimenting, and I am a pretty big hands-on learner, so it is hard for me to stay focused. It is hard for me to learn as fast as other people because I am more of a hands-on person. So, when there is a hands-on activity, I am really happy because I get [to] learn. I get to see. I get to feel. I get to touch, and I like how STEM camp Footnote 1 incorporates that in a fun and awesome way. When we dissected pigs that helped [me] learn about biology in a way where it wasn’t like in health class, where here is a diagram of the human body. Here is a textbook. Read it through. We are going to read it through, we are going to learn about it. No, in this one we learned it in a fun and interesting way. We played bingo with pig parts. (Interview 2017).

Frankie stated,

This is like a summer school but way more fun. First of all, you have two snacks in a day and I usually have to wait a while for snack, and you get to learn about programming and not just boring writing in the boring workbook. I like the more hands-on. It really helped me I think. (Interview 2015)

More importantly, the students were excited that “we actually get to do things like robotics, and we get to like build. We got to learn the process of how like doctors take our DNA” (Adri Interview 2017). Jesse also commented, “I like building things. I mean like it’s fun. Like you get to do things with your hands. You get to move things. You get to like make your imagination things come true sometimes” (Interview 2017). The hands-on nature of the camp also allowed students to not just see how the content is used, but to practice doing the content. Paige described the process of DNA extraction:

Um, we took Gatorade, and mixed it with salt. So, it was like a Gatorade salt solution, and we put it in our mouth so it could absorb some DNA from like our cheeks without bursting it. And we take that and we put it in some detergent, and then we added some clear liquid. I think it was some sort of rubbing alcohol I don’t know. Then you could see like that parts of your DNA like build up. (Interview 2017)

Instead of simply learning about DNA, the students extracted DNA to explore biological concepts. Timmy reiterated, “I just like doing hands-on stuff and I love that STEM Camp lets us do like a bunch of things like hands-on activities and let us learn things and not just tell us what to do, but let us actually do them” (Interview 2016). The summer learning experience was “[m]ore actually doing things” (Jessica Interview 2016). For example, Sally said she liked “building the dam because we got to make up a lot of ideas and try to solve a problem with just the materials that we had” (Interview 2015). Lisa specifically mentioned the power of doing when she stated, “I actually [did] it myself. We didn’t have someone telling us what to do. Who gets to do that? And it helped me learn and that was really cool” (Interview 2017). The emphasis was on doing and seeing.

Students were not simply reading about STEM concepts or watching a video. Students’ learning about STEM was particularly peaked when they were able to interact with materials from STEM fields. Lab materials, software, and technology dominated conversations when students had access to supplies that were not readily available to them. Karena mentioned she “really enjoyed using the microscopes. Looking at larva. And being in the biology class in general. I loved looking at the organs” (Interview 2017). Learning about anatomy from diagrams, videos, and textbooks is not as rich of an experience for students as holding a human heart and brain in their hands.

Students commented on how the hands-on activities and experimenting made the content come to life. Josh, for example, emphasized, “I got to actually see a real brain, lung, organs, things like that, which I’ve never seen before, which was pretty interesting” (Interview 2016). Seeing organs was only part of the experience, though. Dolly described her experience dissecting pigs,

I know it sounds really weird, but at first it’s one of those things where you’re really nervous and like eh, ah, mmm, should I really do this. And it’s one of those things where your stomach, it’s like right before a rollercoaster like you’re stomach’s all tied up in knots and your brains like you want to like this. I’m asking you “do you want to do it, you can quit this right now, do you want to do this” but part of you is like “you know what I’m just gonna do this, I’m just gonna do this” and once you finally get to the top of that rollercoaster you’re like “hey, this is really really fun. I don’t want to stop.” And it’s like one of those things where it seems really gross and nerve-racking, but once you actually like start doing it you’re like “hey this is kinda fun in a disturbing way.” (Interview 2016)

The students were excited they had the opportunity to learn about biology and other science disciplines because many students commented, “We don’t do a lot of science at my school so it’s good to learn more about it” (Taylor Interview 2016). Stephan articulated they have to focus on specific standards, but “[STEM Camp] helps me with more background knowledge around all the subjects” (Interview 2017). The students understand that in the formal learning environment, their teachers may not have the time to go deeper into a subject. Hilda expressed,

I would say STEM camp, it kind of just, it kinda gives you a little bit of everything. Especially with, like, our science stuff. Our science teacher, she teaches us like, everything. She doesn’t go into super detail just so she can get everything done in the year…And this place, …the other half of the day you go into detail about every, like, little thing. Like today we were extracting our own DNA. And we’re talking about, like, the chromosomes and DNA and all this stuff inside of it. (Interview 2017)

Other students emphasized physical science concepts. For example, Jada explained, “I like the lessons. Like the lessons in the science lab because they were really fun cause we got to mix these chemicals together and see how they reacted to other chemicals and stuff. And it was really cool” (Interview 2017). The STEM camp experiences laid the groundwork for connections among disciplines and professions. While students “like to go into the lab and really, really experimenting with the lab coats and stuff,” (Suzanne Interview 2017), it is engaging in the STEM content in those environments that is so important to making it come to life.

[Students] were downstairs taking a tour of the engineering… [They] learned how the vibration and the echo and everything… how like if you talk nobody can hear you ‘cause the uh ectoplasm, something like that. Ectoplasm like, it’s on the walls and it’s pretty hard. So they have to use those little square things, I don’t really know what it’s called but it’s connected to the wall. And like you can like, it’s kinda loud in there but out there, you can’t even hear nothing. Like that’s awesome. I like doing that. (Melsia Interview 2015)

The students emphasized the active nature of participating in the content. Students saw real organs, dissected pigs, extracted DNA, mixed chemicals, made boats, and built levees. Students stressed learning through the activity. This same perspective was also evident as students’ engaged in robotics. Alyssa explained why she liked robotics:

Um I like how everything is really interactive and you learn stuff while having fun um because while you’re programming robots, you’re writing code and you’re programming, but it’s a lot of fun and you turn it into a competition and they make racetracks to make it more appealing to kids. (Interview 2016)

As the students completed the robotics challenges, they anticipated and looked forward to “going on to harder ones every time” (Becka Interview 2017). The students deepened their understanding of programming, and they did not give up when their robot did not do what was expected.

Like if you’re programming and you don’t do it right you can go back and fix it. So, it’s like kind of like a trial and error, with fixing things, and like if I do something wrong, I can go back and try to fix it. I just think about, like how, what did I do wrong that I could’ve done better. If it’s turning too far, then we’ll bring down the rotations. And then, if it’s uhm going too short, then we’ll just bring the rotations up more. (Cory Interview 2017)

Jordan shared,

I liked um programming the robots and learning um a little bit in each subject. When I’ve done something wrong, I’d go back and I would make the number or something, cuz you have to make the numbers, I would make the number a little bit higher, and if that wasn’t right I would make it in the middle between those two. It would teach me right from wrong. (Interview 2015)

Students did not talk about learning how to program by reading about it or by listening to a lecture or by completing a worksheet. The students, instead, emphasized learning to program by doing the programming. When they were wrong, as the student above described, they tried again. It was a problem solving process to program the robot to do what they wanted.

Melanie described, “I mean it’s fun having to programming the robots, but it’s really challenging. Program the robots, they do the wrong thing, but then you correct them and their mistakes” (Interview 2016). Unfortunately, many students discussed how they did not experience these “fun activities” at their school. The students were excited about all of the experiments and learning.

I’m addicted [to robotics], basically. Like as soon as they say like 10 minutes [left], I’m like rushing to get things done because it’s so fun and it takes, it actually takes effort. Not like you know, breeze through it and kinda know everything–like sometimes that happens at school. (Tonia Interview 2016)

Students expressed the STEM summer learning experience allowed them to be creative and use their mathematical skills when working with the robots. In a broader perspective, access to the professionals, curriculum, content, and environments central to the STEM camp experience built students up where they had previously felt inadequate or poorly adept. Morgan added,

Well I'm not really good at math, but I think this morning we learned about like different things that use different shapes that you've already learned like the great pyramids….sometimes there are shapes that you can use. Sometimes you have to draw them out and can use them to make cubes, pyramids, and things like…some of my sisters told me—she told me math in 7th grade is really hard to understand if you don't understand shapes so like…maybe because some things we've learned - some things don't apply but some—they will actually apply to what we learned but some of them will. (Interview 2015)

For this student, understanding something she perceived as really hard was a victory and confidence booster. For other students, they feel more prepared for classes because it’s helping me and like showing me when I will need to use that math in real life problems, and that I will need to, and it’s like helping me like understand why we need to learn math; because I don’t like math very much. (Erin Interview 2016)

Dolly said the STEM summer learning experience is

...preparing me for some daring things I might do. Like, you gotta be brave, you’ve gotta be willing to like actually throw yourself out in the world saying “hey I’m just gonna do this” because if you’re doing like a science project or something…you have to be optimistic about work, you’ve gotta be outgoing and I think going here is making be braver to do that…we’re being able to interact with things that will make me like learn be able to learn more things. (Interview 2016)

Students were able to connect their new learning to their futures. Some students thought more short term, “If I learn more about this topic it will better prepare me for middle school” (Walter 2014 Math Modeling Reflection), while other students were connecting their experiences to their distant future, “I want to become a doctor when I grow up, and to do so I need to know a lot about anatomy. Dissecting animals really helps me learn more” (Heather 2016 Anatomy Reflection). Students gained STEM knowledge because they were given the opportunity to access and engage in STEM activities and persevere.

The goal of the STEM summer learning experience was to (1) provide upper-level elementary and middle level students, particularly underrepresented populations, access to a variety of STEM fields and STEM professionals in their workplace environment through authentic, hands-on learning activities, and (2) increase students’ interest in a STEM career. While one of the goals centered on STEM careers, the benefits of participating in the STEM summer learning experience also extended to students’ perceptions of future STEM learning. This study highlighted how the STEM summer informal learning environment influenced students’ perceptions of STEM learning. Specifically, the STEM summer learning experience provided students with context and purpose for formal STEM content. The use of project/problem-based learning allowed students to connect to real-world issues (STEM Task Force Report, 2014 ), such as seeing how mathematics is needed in the design and construction of planes, in the programming of robots, and in the calculations students used in measuring distance for their robotics challenges. By using authentic STEM workplaces, the STEM summer learning experience fostered a learning environment that extended and deepened STEM content learning while providing opportunity and access to content, settings, and materials that most middle level students otherwise would not have access to. Denise’s comments epitomized how interacting with STEM professionals normalized and humanized them. It allowed her to connect to the STEM community as a place where she can participate, practice, and belong (O'Connell et al. 2017 ).

Students also acknowledged the access they received to hands-on activities in authentic STEM settings and the opportunities they received to interact with STEM professionals were important components of the summer informal learning experience. In an era of budget cuts and pressure to cover material that will appear on standardized tests, schools are often limited in the access they can provide to in-depth content and authentic settings. Unfortunately, this contributes to the “receivement gap” (Chambers 2009 , p. 418) that many students, particularly Black and Latinx students, experience. While policymakers focus extensively on outputs, such as achievement scores, less attention is focused on the inputs in and structures of education. The result is a system that does not provide equitable access or opportunity to authentic, engaging learning experiences that bring the content to life. As the students’ own comments showed, their participation in the summer informal STEM learning experience addressed the limitations of formal schooling through the experiences provided (Bell et al. 2009 ; Meyers et al. 2013 ). Thus, in the current system, one implication of this study is the importance of high-quality informal STEM learning experiences, such as those provided by the See Blue See STEM model, to increase students’ access and opportunity to engaging activities that contextualize and give purpose to their formal learning.

The findings of this research can also be considered to design authentic learning experiences in informal settings and to create purposeful contexts and settings in informal experiences. Providing access to meaningful contexts for learning (STEM Task Force Report 2014 ) and authentic settings is critical. While it is unrealistic to think every informal STEM learning environment would have access to scientists’ labs, creating partnerships with people in STEM careers is one way to provide a broader picture of what STEM is, where STEM happens, and who does STEM. This provides the meaningful exposure to a STEM community (O'Connell et al. 2017 ) and influences how students participate, practice, and belong in that STEM community.

While this study is important in highlighting the students’ perceptions of how participating in an informal STEM learning environment prepares them for future STEM learning, further research is needed to examine lasting impacts of participating in this type of informal learning. Exploring students’ future course taking patterns, success and perseverance in STEM-related courses, and choice of college majors and/or career are all areas needing further research.

NOTE: Informal summer learning experiences are colloquially known as camp.

Abbreviations

Next-generation science standards

Presidential Council of Advisors on Science and Technology

Science, technology, engineering, and mathematics

United States

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This work was supported by the National Science Foundation under Grant Numbers 1348281 and 1560013, the Fluor, and AstraZeneca. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the Fluor, and AstraZeneca.

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MMS, MC, CJ, and CM managed collection of the paper-based surveys and reflections. MMS, MC, CJ, CM, TR, and AD managed collection interview data. TR and CJ developed the initial research question for this paper. TR, CJ, CM, and AD analyzed the qualitative data. All authors participated in creating, revising, and testing the code list. All authors participated in writing, revising, and approving the final manuscript.

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TR, Assistant Professor, Bowling Green State University. TR’s research interests include informal STEM education, African American students’ relationship with and understanding of mathematics and STEM, how elementary preservice teachers think about equitable teaching of mathematics, and how elementary preservice teachers develop mathematics and STEM teaching identities.

CJ, Associate Professor, Iowa State University. CJ’s research interest focuses on effective mathematics instruction at the elementary and middle levels, the preparation of prospective and in-service mathematics teachers, STEM Education, STEM literacy, STEM curricula development, strategies to help students who struggle in mathematics, and STEM teachers’ conceptions of equity.

MMS, Professor of STEM Education, University of Kentucky. MMS’s current line of research includes Preservice teachers’ perceptions of struggling learners, transdisciplinary STEM education, informal STEM learning environments, and broadening participation in STEM.

SBB, Associate Professor of K-12 STEM Education, University of Central Florida. SBB’s most current lines of research include deepening student and teacher understanding of mathematics through transdisciplinary STE(A)M problem-based inquiry and mathematics, science, and STE(A)M teacher professional development effectiveness.

CM, Assistant Professor, California State University, Long Beach. CM’s research interests include how preservice teachers incorporate mathematical modeling and the engineering design process into their mathematics classrooms, how do preservice teachers implement problem-based learning and integrated STEM education influences students’ motivation toward and perceptions of STEM.

MALC, Education Resource Specialist, The Ohio State University College of Medicine, Columbus, OH. MALC’s research interests include how to prepare resilient and proficient STEM professionals, and how the design of curriculum and learning experiences can support STEM literacy and equitable STEM pathways.

DCS, STEM Teacher, Fayette County Public Schools, Lexington, KY. DCS’ research interests include broadening participation in STEM and STEM informal learning environments.

AD, doctoral student, Iowa State University. AD’s current line of research includes early childhood and elementary STEM curriculum, transdisciplinary STEM education, broadening participation in STEM, and family engagement in mathematics and STEM experiences.

LRP, Undergraduate Research Assistant, University of Kentucky.

CAC, Undergraduate Research Assistant, Morehead State University.

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Roberts, T., Jackson, C., Mohr-Schroeder, M.J. et al. Students’ perceptions of STEM learning after participating in a summer informal learning experience. IJ STEM Ed 5 , 35 (2018). https://doi.org/10.1186/s40594-018-0133-4

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Quantitative STEM: Experimental Methods and Applications

J M LeBeau 1 , S D Findlay 2 , L J Allen 3 and S Stemmer 4

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 371 , Electron Microscopy and Analysis Group Conference 2011 (EMAG 2011) 6–9 September 2011, Birmingham, UK Citation J M LeBeau et al 2012 J. Phys.: Conf. Ser. 371 012053 DOI 10.1088/1742-6596/371/1/012053

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High-angle annular dark-field (HAADF) imaging in scanning transmission electron microscopy (STEM) is highly sensitive to both the atomic number and the Debye-Waller factor of the atom columns. We will discuss the experimental requirements for a quantitative understanding of STEM image contrast, in particular the determination of the precise specimen thickness. We show that near perfect agreement can be achieved between theory and experiment and demonstrate that quantitative STEM allows for column-by-column counting of all the atoms in an arbitrarily shaped specimen.

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