problem solving in educational management

• Author Rights
• Diversity, Equity & Inclusion

• JOLE 2023 Special Issue
• Editorial Staff
• 20th Anniversary Issue
• The Development of Problem-Solving Skills for Aspiring Educational Leaders

Jeremy D. Visone 10.12806/V17/I4/R3

Introduction

Solving problems is a quintessential aspect of the role of an educational leader. In particular, building leaders, such as principals, assistant principals, and deans of students, are frequently beset by situations that are complex, unique, and open-ended. There are often many possible pathways to resolve the situations, and an astute educational leader needs to consider many factors and constituencies before determining a plan of action. The realm of problem solving might include student misconduct, personnel matters, parental complaints, school culture, instructional leadership, as well as many other aspects of educational administration. Much consideration has been given to the development of problem-solving skills for educational leaders. This study was designed to answer the following research question: “How do aspiring educational leaders’ problem solving skills, as well as perceptions of their problem-solving skills, develop during a year-long graduate course sequence focused on school-level leadership that includes the presentation of real-world scenarios?” This mixed-methods study extends research about the development of problem-solving skills conducted with acting administrators (Leithwood & Steinbach, 1992, 1995).

The Nature of Problems

Before examining how educational leaders can process and solve problems effectively, it is worth considering the nature of problems. Allison (1996) posited simply that problems are situations that require thought and/or actions. Further, there are different types of problems presented to educational leaders. First, there are  well-structured problems , which can be defined as those with clear goals and relatively prescribed resolution pathways, including an easy way of determining whether goals were met (Allison, 1996).

Conversely,  ill-structured problems  are those with more open-ended profiles, whereby the goals, resolution pathways, or evidence of success are not necessarily clear. These types of problems could also be considered  unstructured  (Leithwood & Steinbach, 1995) or  open-design  (Allison, 1996). Many of the problems presented to educational leaders are unstructured problems. For example, a principal must decide how to discipline children who misbehave, taking into consideration their disciplinary history, rules and protocols of the school, and other contextual factors; determine how best to raise student achievement (Duke, 2014); and resolve personnel disputes among staff members. None of these problems point to singular solutions that can be identified as “right” or “wrong.” Surely there are responses that are less desirable than others (i.e. suspension or recommendation for expulsion for minor infractions), but, with justification and context, many possible solutions exist.

Problem-Solving Perspectives and Models

Various authors have shared perspectives about effective problem solving. Marzano, Waters, and McNulty (2005) outlined the “21 Responsibilities of the School Leader.” These responsibilities are highly correlated with student achievement based upon the authors’ meta- analysis of 69 studies about leadership’s effect on student achievement. The most highly correlated of the responsibilities was  situational awareness , which refers to understanding the school deeply enough to anticipate what might go wrong from day-to-day, navigate the individuals and groups within the school, and recognize issues that might surface at a later time (Marzano et al., 2005). Though the authors discuss the utility of situational awareness for long- term, large-scale decision making, in order for an educational leader to effectively solve the daily problems that come her way, she must again have a sense of situational awareness, lest she make seemingly smaller-scale decisions that will lead to large-scale problems later.

Other authors have focused on problems that can be considered more aligned with the daily work of educational leaders. Considering the problem-type classification dichotomies of Allison (1996) and Leithwood and Steinbach (1995), problems that educational leaders face on a daily basis can be identified as either well-structured or unstructured. Various authors have developed problem-solving models focused on unstructured problems (Bolman & Deal, 2008; Leithwood & Steinbach, 1995; Simon, 1993), and these models will be explored next.

Simon (1993) outlined three phases of the decision-making process. The first is to find problems that need attention. Though many problems of educational leaders are presented directly to them via, for example, an adult referring a child for discipline, a parent registering a complaint about a staff member, or a staff member describing a grievance with a colleague, there is a corollary skill of identifying what problems—of the many that come across one’s desk— require immediate attention, or ultimately, any attention, at all. Second, Simon identified “designing possible courses of action” (p. 395). Finally, educational leaders must evaluate the quality of their decisions. From this point of having selected a viable and positively evaluated potential solution pathway, implementation takes place.

Bolman and Deal (2008) outlined a model of reframing problems using four different frames, through which problems of practice can be viewed. These frames provide leaders with a more complete set of perspectives than they would likely utilize on their own. The  structural frame  represents the procedural and systems-oriented aspects of an organization. Within this frame, a leader might ask whether there is a supervisory relationship involved in a problem, if a protocol exists to solve such a problem, or what efficiencies or logical processes can help steer a leader toward a resolution that meets organizational goals. The  human resource frame  refers to the needs of individuals within the organization. A leader might try to solve a problem of practice with the needs of constituents in mind, considering the development of employees and the balance between their satisfaction and intellectual stimulation and the organization’s needs. The  political frame  includes the often competing interests among individuals and groups within the organization, whereby alliances and negotiations are needed to navigate the potential minefield of many groups’ overlapping aims. From the political frame, a leader could consider what the interpersonal costs will be for the leader and organization among different constituent groups, based upon which alternatives are selected. Last, the  symbolic frame  includes elements of meaning within an organization, such as traditions, unspoken rules, and myths. A leader may need to consider this frame when proposing a solution that might interfere with a long-standing organizational tradition.

Bolman and Deal (2008) identified the political and symbolic frames as weaknesses in most leaders’ consideration of problems of practice, and the weakness in recognizing political aspects of decision making for educational leaders was corroborated by Johnson and Kruse (2009). An implication for leadership preparation is to instruct students in the considerations of these frames and promote their utility when examining problems.

Authors have noted that experts use different processes than novice problem solvers (Simon, 1993; VanLehn, 1991). An application of this would be Simon’s (1993) assertion that experts can rely on their extensive experience to remember solutions to many problems, without having to rely on an extensive analytical process. Further, they may not even consider a “problem” identified by a novice a problem, at all. With respect to educational leaders, Leithwood and Steinbach (1992, 1995) outlined a set of competencies possessed by expert principals, when compared to their typical counterparts. Expert principals were better at identifying the nature of problems; possessing a sense of priority, difficulty, how to proceed, and connectedness to prior situations; setting meaningful goals for problem solving, such as seeking goals that are student-centered and knowledge-focused; using guiding principles and long-term purposes when determining the best courses of action; seeing fewer obstacles and constraints when presented with problems; outlining detailed plans for action that include gathering extensive information to inform decisions along the plan’s pathway; and responding with confidence and calm to problem solving. Next, I will examine how problem-solving skills are developed.

Preparation for Educational Leadership Problem Solving

How can the preparation of leaders move candidates toward the competencies of expert principals? After all, leading a school has been shown to be a remarkably complex enterprise (Hallinger & McCary, 1990; Leithwood & Steinbach, 1992), especially if the school is one where student achievement is below expectations (Duke, 2014), and the framing of problems by educational leaders has been espoused as a critically important enterprise (Bolman & Deal, 2008; Dimmock, 1996; Johnson & Kruse, 2009; Leithwood & Steinbach, 1992, 1995; Myran & Sutherland, 2016). In other disciplines, such as business management, simulations and case studies are used to foster problem-solving skills for aspiring leaders (Rochford & Borchert, 2011; Salas, Wildman, & Piccolo, 2009), and attention to problem-solving skills has been identified as an essential curricular component in the training of journalism and mass communication students (Bronstein & Fitzpatrick, 2015). Could such real-world problem solving methodologies be effective in the preparation of educational leaders? In a seminal study about problem solving for educational leaders, Leithwood and Steinbach (1992, 1995) sought to determine if effective problem-solving expertise could be explicitly taught, and, if so, could teaching problem- processing expertise be helpful in moving novices toward expert competence? Over the course of four months and four separate learning sessions, participants in the control group were explicitly taught subskills within six problem-solving components: interpretation of the problem for priority, perceived difficulty, data needed for further action, and anecdotes of prior experience that can inform action; goals for solving the problem; large-scale principles that guide decision making; barriers or obstacles that need to be overcome; possible courses of action; and the confidence of the leader to solve the problem. The authors asserted that providing conditions to participants that included models of effective problem-solving, feedback, increasingly complex problem-solving demands, frequent opportunities for practice, group problem-solving, individual reflection, authentic problems, and help to stimulate metacognition and reflection would result in educational leaders improving their problem-solving skills.

The authors used two experts’ ratings of participants’ problem-solving for both process (their methods of attacking the problem) and product (their solutions) using a 0-3 scale in a pretest-posttest design. They found significant increases in some problem-solving skills (problem interpretation, goal setting, and identification of barriers or obstacles that need to be overcome) after explicit instruction (Leithwood & Steinbach, 1992, 1995). They recommended conducting more research on the preparation of educational leaders, with particular respect to approaches that would improve the aspiring leaders’ problem-solving skills.

Solving problems for practicing principals could be described as constructivist, since most principals do solve problems within a social context of other stakeholders, such as teachers, parents, and students (Leithwood & Steinbach, 1992). Thus, some authors have examined providing opportunities for novice or aspiring leaders to construct meaning from novel scenarios using the benefits of, for example, others’ point of view, expert modeling, simulations, and prior knowledge (Duke, 2014; Leithwood & Steinbach, 1992, 1995; Myran & Sutherland, 2016; Shapira-Lishchinsky, 2015). Such collaborative inquiry has been effective for teachers, as well (DeLuca, Bolden, & Chan, 2017). Such learning can be considered consistent with the ideas of other social constructivist theorists (Berger & Luckmann, 1966; Vygotsky, 1978) as well, since individuals are working together to construct meaning, and they are pushing into areas of uncertainty and lack of expertise.

Shapira-Lishchinsky (2015) added some intriguing findings and recommendations to those of Leithwood and Steinbach (1992, 1995). In this study, 50 teachers with various leadership roles in their schools were presented regularly with ethical dilemmas during their coursework. Participants either interacted with the dilemmas as members of a role play or by observing those chosen. When the role play was completed, the entire group debriefed and discussed the ethical dilemmas and role-playing participants’ treatment of the issues. This method was shown, through qualitative analysis of participants’ discussions during the simulations, to produce rich dialogue and allow for a safe and controlled treatment of difficult issues. As such, the use of simulations was presented as a viable means through which to prepare aspiring educational leaders. Further, the author suggested the use of further studies with simulation-based learning that seek to gain information about aspiring leaders’ self-efficacy and psychological empowerment. A notable example of project-based scenarios in a virtual collaboration environment to prepare educational leaders is the work of Howard, McClannon, and Wallace (2014). Shapira-Lishchinsky (2015) also recommended similar research in other developed countries to observe the utility of the approaches of simulation and social constructivism to examine them for a wider and diverse aspiring administrator candidate pool.

Further, in an extensive review of prior research studies on the subject, Hallinger and Bridges (2017) noted that Problem-Based Learning (PBL), though applied successfully in other professions and written about extensively (Hallinger & Bridges, 1993, 2017; Stentoft, 2017), was relatively unheralded in the preparation of educational leaders. According to the authors, characteristics of PBL included problems replacing theory as the organization of course content, student-led group work, creation of simulated products by students, increased student ownership over learning, and feedback along the way from professors. Their review noted that PBL had positive aspects for participants, such as increased motivation, real-world connections, and positive pressure that resulted from working with a team. However, participants also expressed concerns about time constraints, lack of structure, and interpersonal dynamics within their teams. There were positive effects found on aspiring leaders’ problem-solving skill development with PBL (Copland, 2000; Hallinger & Bridges, 2017). Though PBL is much more prescribed than the scenarios strategy described in the Methods section below, the applicability of real-world problems to the preparation of educational leaders is summarized well by Copland (2000):

[I]nstructional practices that activate prior knowledge and situate learning in contexts similar to those encountered in practice are associated with the development of students’ ability to understand and frame problems. Moreover, the incorporation of debriefing techniques that encourage students’ elaboration of knowledge and reflection on learning appear to help students solidify a way of thinking about problems. (p. 604)

This study involved a one-group pretest-posttest design. No control group was assigned, as the pedagogical strategy in question—the use of real-world scenarios to build problem-solving skill for aspiring educational leaders—is integral to the school’s curriculum that prepares leaders, and, therefore, it is unethical to deny to student participants (Gay & Airasian, 2003). Thus, all participants were provided instruction with the use of real-world scenarios.

Participants.  Graduate students at a regional, comprehensive public university in the Northeast obtaining a 6 th -year degree (equivalent to a second master’s degree) in educational leadership and preparing for certification as educational administrators served as participants. Specifically, students in three sections of the same full-year, two-course sequence, entitled “School Leadership I and II” were invited to participate. This particular course was selected from the degree course sequence, as it deals most directly with the problem-solving nature and daily work of school administrators. Some key outcomes of the course include students using data to drive school improvement action plans, communicating effectively with a variety of stakeholders, creating a safe and caring school climate, creating and maintaining a strategic and viable school budget, articulating all the steps in a hiring process for teachers and administrators, and leading with cultural proficiency.

The three sections were taught by two different professors. The professors used real- world scenarios in at least half of their class meetings throughout the year, or in approximately 15 classes throughout the year. During these classes, students were presented with realistic situations that have occurred, or could occur, in actual public schools. Students worked with their classmates to determine potential solutions to the problems and then discussed their responses as a whole class under the direction of their professor, a master practitioner. Both professors were active school administrators, with more than 25 years combined educational leadership experience in public schools. It should be noted that the scenario presentation and discussions took place during the class sessions, only. These were not presented for homework or in online forums.

Of the 44 students in these three sections, 37 volunteered to participate at some point in the data collection sequence, but not all students in the pretest session attended the posttest session months later and vice versa. As a result, only 20 students’ data were used for the matched pairs analysis. All 37 participants were certified professional educators in public schools in Connecticut. The participants’ professional roles varied and included classroom teachers, instructional coaches, related service personnel, unified arts teachers, as well as other non- administrative educational roles. Characteristics of participants in the overall and matched pairs groups can be found in Table 1.

Table 1 Participant Characteristics

Procedure.  Participants’ data were compared between a fall of 2016 baseline data collection period and a spring of 2017 posttest data collection period. During the fall data collection period, participants were randomly assigned one of two versions of a Google Forms survey. After items about participant characteristics, the survey consisted of 11 items designed to elicit quantitative and qualitative data about participants’ perceptions of their problem-solving abilities, as well as their ability to address real-world problems faced by educational leaders. The participants were asked to rate their perception of their situational awareness, flexibility, and problem solving ability on a 10-point (1-10) Likert scale, following operational definitions of the terms (Marzano, Waters, & McNulty, 2005; Winter, 1982). They were asked, for each construct, to write open-ended responses to justify their numerical rating. They were then asked to write what they perceived they still needed to improve their problem-solving skills. The final four items included two real-world, unstructured, problem-based scenarios for which participants were asked to create plans of action. They were also asked to rate their problem-solving confidence with respect to their proposed action plans for each scenario on a 4-point (0-3) Likert scale.

During the spring data collection period, participants accessed the opposite version of the Google Forms survey from the one they completed in the fall. All items were identical on the two survey versions, except the scenarios, which were different on each survey version. The use of two versions was to ensure that any differences in perceived or actual difficulty among the four scenarios provided would not alter results based upon the timing of participant access (Leithwood & Steinbach, 1995). In order to link participants’ fall and spring data in a confidential manner, participants created a unique, six-digit alphanumeric code.

A focus group interview followed each spring data collection session. The interviews were recorded to allow for accurate transcription. The list of standard interview questions can be found in Table 2. This interview protocol was designed to elicit qualitative data with respect to aspiring educational leaders’ perceptions about their developing problem-solving abilities.

Table 2 Focus Group Interview Questions ___________________________________________________________________________________________

Please describe the development of your problem-solving skills as an aspiring educational leader over the course of this school year. In what ways have you improved your skills? Be as specific as you can.

What has been helpful to you (i.e. coursework, readings, experiences, etc.) in this development of your problem-solving skills? Why?

What do you believe you still need for the development in your problem-solving skills as an aspiring educational leader?

Discuss your perception of your ability to problem solve as an aspiring educational leader. How has this changed from the beginning of this school year? Why?

Please add anything else you perceive is relevant to this conversation about the development of your problem-solving skills as an aspiring educational leader.

___________________________________________________________________________________________

Data Analysis.

Quantitative data .  Data were obtained from participants’ responses to Likert-scale items relating to their confidence levels with respect to aspects of problem solving, as well as from the rating of participants’ responses to the given scenarios  against a rubric. The educational leadership problem-solving rubric chosen (Leithwood & Steinbach, 1995) was used with permission, and it reflects the authors’ work with explicitly teaching practicing educational leaders components of problem solving. The adapted rubric can be found in Figure 1. Through the use of this rubric, each individual response by a participant to a presented scenario was assigned a score from 0-15. It should be noted that affect data (representing the final 3 possible points on the 18-point rubric) were obtained via participants’ self-reporting their confidence with respect to their proposed plans of action. To align with the rubric, participants self-assessed their confidence through this item with a 0-3 scale.

0 = No Use of the Subskill 1 = There is Some Indication of Use of the Subskill 2 = The Subskill is Present to Some Degree 3 = The Subskill is Present to a Marked Degree; This is a Fine Example of this Subskill

Figure 1.  Problem-solving model for unstructured problems. Adapted from “Expert Problem Solving: Evidence from School and District Leaders,” by K. Leithwood and R. Steinbach, pp. 284-285. Copyright 1995 by the State University of New York Press.

I compared Likert-scale items and rubric scores via descriptive statistics and rubric scores also via a paired sample  t -test and Cohen’s  d , all using the software program IBM SPSS. I did not compare the Likert-scale items about situational awareness, flexibility, and problem solving ability with  t -tests or Cohen’s  d , since these items did not represent a validated instrument. They were only single items based upon participants’ ratings compared to literature-based definitions. However, the value of the comparison of means from fall to spring was triangulated with qualitative results to provide meaning. For example, to say that participants’ self-assessment ratings for perceived problem-solving abilities increased, I examined both the mean difference for items from fall to spring and what participants shared throughout the qualitative survey items and focus group interviews.

Prior to scoring participants’ responses to the scenarios using the rubric, and in an effort to maximize the content validity of the rubric scores, I calibrated my use of the rubric with two experts from the field. Two celebrated principals, representing more than 45 combined years of experience in school-level administration, collaboratively and comparatively scored participant responses. Prior to scoring, the team worked collaboratively to construct appropriate and comprehensive exemplar responses to the four problem-solving scenarios. Then the team blindly scored fall pretest scenario responses using the Leithwood and Steinbach (1995) rubric, and upon comparing scores, the interrater reliability correlation coefficient was .941, indicating a high degree of agreement throughout the team.

Qualitative data.  These data were obtained from open-ended items on the survey, including participants’ responses to the given scenarios, as well as the focus group interview transcripts. I analyzed qualitative data consistent with the grounded theory principles of Strauss and Corbin (1998) and the constant comparative methods of Glaser (1965), including a period of open coding of results, leading to axial coding to determine the codes’ dimensions and relationships between categories and their subcategories, and selective coding to arrive at themes. Throughout the entire data analysis process, I repeatedly returned to raw data to determine the applicability of emergent codes to previously analyzed data. Some categorical codes based upon the review of literature were included in the initial coding process. These codes were derived from the existing theoretical problem-solving models of Bolman and Deal (2008) and Leithwood and Steinbach (1995). These codes included  modeling ,  relationships , and  best for kids . Open codes that emerged from the participants’ responses included  experience ,  personality traits ,  current job/role , and  team . Axial coding revealed, for example, that current jobs or roles cited, intuitively, provided both sufficient building-wide perspective and situational memory (i.e. for special education teachers and school counselors) and insufficient experiences (i.e. for classroom teachers) to solve the given problems with confidence. From such understandings of the codes, categories, and their dimensions, themes were developed.

Quantitative Results.   First, participants’ overall, aggregate responses (not matched pairs) were compared from the fall to spring, descriptively. These findings are outlined in Table  3. As is seen in the table, each item saw a modest increase over the course of the year. Participant perceptions of their problem-solving abilities across the three constructs presented (situational awareness, flexibility, and problem solving) did increase over the course of the year, as did the average group score for the problem-solving scenarios. However, due to participant differences in the two data collection periods, these aggregate averages do not represent a matched-pair dataset.

Table 3 Fall to Spring Comparison of Likert-Scale and Rubric-Scored Items

a  These problem-solving dimensions from literature were rated by participants on a scale from 1- 10. b  Participants received a rubric score for each scenario between 0-18. Participants’ two scenario scores for each data collection period (fall, spring) were averaged to arrive at the scores represented here.

In order to determine the statistical significance of the increase in participants’ problem- solving rubric scores, a paired-samples  t -test was applied to the fall ( M  = 9.15;  SD  = 2.1) and spring ( M  = 9.25;  SD  = 2.3) averages. Recall that 20 participants had valid surveys for both the fall and spring. The  t -test ( t  = -.153;  df  = 19;  p  = .880) revealed no statistically significant change from fall to spring, despite the minor increase (0.10). I applied Cohen’s  d  to calculate the effect size. The small sample size ( n  = 20) for the paired-sample  t -test may have contributed to the lack of statistical significance. However, standard deviations were also relatively small, so the question of effect size was of particular importance. Cohen’s  d  was 0.05, which is also very small, indicating that little change—really no improvement, from a statistical standpoint—in participants’ ability to create viable action plans to solve real-world problems occurred throughout the year. However, the participants’ perceptions of their problem-solving abilities did increase, as evidenced by the increases in the paired-samples perception means shown in Table 3, though these data were only examined descriptively (from a quantitative perspective) due to the fact that these questions were individual items that are not part of a validated instrument.

Qualitative Results.   Participant responses to open-ended items on the questionnaire, responses to the scenarios, and oral responses to focus group interview questions served as sources of qualitative data. Since the responses to the scenarios were focused on participant competence with problem solving, as measured by the aforementioned rubric (Leithwood &  Steinbach, 1995), these data were examined separately from data collected from the other two sources.

Responses to scenarios.  As noted, participants’ rubric ratings for the scenarios did not display a statistically significant increase from fall to spring. As such, this outline will not focus upon changes in responses from fall to spring. Rather, I examined the responses, overall, through the lens of the Leithwood and Steinbach (1995) problem-solving framework indicators against which they were rated. Participants typically had outlined reasonable, appropriate, and logical solution processes. For example, in a potential bullying case scenario, two different participants offered, “I would speak to the other [students] individually if they have said or done anything mean to other student [ sic ] and be clear that it is not tolerable and will result in major consequences” and “I would initiate an investigation into the situation beginning with [an] interview with the four girls.” These responses reflect actions that the consulted experts anticipated from participants and deemed as logical and needed interventions. However, these two participants omitted other needed steps, such as addressing the bullied student’s mental health needs, based upon her mother’s report of suicidal ideations. Accordingly, participants earned points for reasonable and logical responses very consistently, yet, few full-credit responses were observed.

Problem interpretation scores were much more varied. For this indicator, some participants were able to identify many, if not all, the major issues in the scenarios that needed attention. For example, for a scenario where two teachers were not interacting professionally toward each other, many participants correctly identified that this particular scenario could include elements of sexual harassment, professionalism, teaching competence, and personality conflict. However, many other participants missed at least two of these key elements of the problem, leaving their solution processes incomplete. The categories of (a) goals and (b) principles and values also displayed a similarly wide distribution of response ratings.

One category, constraints, presented consistent difficulty for the participants. Ratings were routinely 0 and 1. Participants could not consistently report what barriers or obstacles would need addressing prior to success with their proposed solutions. To be clear, it was not a matter of participants listing invalid or unrealistic barriers or obstacles; rather, the participants were typically omitting constraints altogether from their responses. For example, for a scenario involving staff members arriving late and unprepared to data team meetings, many participants did not identify that a school culture of not valuing data-driven decision making or lack of norms for data team work could be constraints that the principal could likely face prior to reaching a successful resolution.

Responses to open-ended items.  When asked for rationale regarding their ratings for situational awareness, flexibility, and problem solving, participants provided open-ended responses. These responses revealed patterns worth considering, and, again, this discussion will consider, in aggregate, responses made in both the pre- and post- data collection periods, again due to the similarities in responses between the two data collection periods. The most frequently observed code (112 incidences) was  experience . Closely related were the codes  current job/role  (50 incidences). Together, these codes typically represented a theme that participants were linking their confidence with respect to problem solving with their exposure (or lack thereof) in their professional work. For example, a participant reported, “As a school counselor, I have a lot of contact with many stakeholders in the school -admin [ sic ], parents, teachers, staff, etc. I feel that I have a pretty good handle on the systemic issues.” This example is one of many where individuals working in counseling, instructional coaching, special education, and other support roles expressed their advanced levels of perspective based upon their regular contact with many stakeholders, including administrators. Thus, they felt they had more prior knowledge and situational memory about problems in their schools.

However, this category of codes also included those, mostly classroom or unified arts teachers, who expressed that their relative lack of experiences outside their own classrooms limited their perspective for larger-scale problem solving. One teacher succinctly summarized this sentiment, “I have limited experience in being part of situations outside of my classroom.” Another focused on the general problem solving skill in her classroom not necessarily translating to confidence with problem solving at the school level: “I feel that I have a high situational awareness as a teacher in the classroom, but as I move through these leadership programs I find that I struggle to take the perspective of a leader.” These experiences were presented in opposition to their book learning or university training. There were a number of instances (65 combined) of references to the value of readings, class discussions, group work, scenarios presented, research, and coursework in the spring survey. When asked what the participants need more, again, experience was referenced often. One participant summarized this concept, “I think that I, personally, need more experience in the day-to-day . . . setting.” Another specifically separated experiences from scenario work, “[T]here is [ sic ] some things you can not [ sic ] learn from merely discussing a ‘what if” scenario. A seasoned administrator learns problem solving skills on the job.”

Another frequently cited code was  personality traits  (63 incidences), which involved participants linking elements of their own personalities to their perceived abilities to process problems, almost exclusively from an assets perspective. Examples of traits identified by participants as potentially helpful in problem solving included: open-mindedness, affinity for working with others, not being judgmental, approachability, listening skills, and flexibility. One teacher exemplified this general approach by indicating, “I feel that I am a good listener in regards to inviting opinions. I enjoy learning through cooperation and am always willing to adapt my teaching to fit needs of the learners.” However, rare statements of personality traits interfering with problem solving included, “I find it hard to trust others [ sic ] abilities” and “my personal thoughts and biases.”

Another important category of the participant responses involved connections with others. First, there were many references to  relationships  (27 incidences), mostly from the perspective that building positive relationships leads to greater problem-solving ability, as the aspiring leader knows stakeholders better and can rely on them due to the history of positive interactions. One participant framed this idea from a deficit perspective, “Not knowing all the outlying relationships among staff members makes situational awareness difficult.” Another identified that established positive relationships are already helpful to an aspiring leader, “I have strong rapport with fellow staff members and administrators in my building.” In a related way, many instances of the code  team  were identified (29). These references overwhelmingly identified that solving problems within a team context is helpful. One participant stated, “I often team with people to discuss possible solutions,” while another elaborated,

I recognize that sometimes problems may arise for which I am not the most qualified or may not have the best answer. I realize that I may need to rely on others or seek out help/opinions to ensure that I make the appropriate decision.

Overall, participants recognized that problem-solving for leaders does not typically occur in a vacuum.

Responses to focus group interview questions.  As with the open-ended responses, patterns were evident in the interview responses, and many of these findings were supportive of the aforementioned themes. First, participants frequently referenced the power of group work to help build their understanding about problems and possible solutions. One participant stated, “hearing other people talk and realizing other concerns that you may not have thought of . . . even as a teacher sometimes, you look at it this way, and someone else says to see it this way.” Another added, “seeing it from a variety of persons [ sic ] point of views. How one person was looking at it, and how another person was looking at it was really helpful.” Also, the participants noted the quality of the discussion was a direct result of “professors who have had real-life experience” as practicing educational leaders, so they could add more realistic feedback and insight to the discussions.

Perhaps most notable in the participant responses during the focus groups was the emphasis on the value of real-world scenarios for the students. These were referenced, without prompting, in all three focus groups by many participants. Answers to the question about what has been most helpful in the development of their problem-solving skills included, “I think the real-world application we are doing,” “I think being presented with all the scenarios,” and “[the professor] brought a lot of real situations.”

With respect to what participants believed they still needed to become better and more confident problem solvers, two patterns emerged. First, students recognized that they have much more to learn, especially with respect to policy and law. It is noteworthy that, with few exceptions, these students had not taken the policy or law courses in the program, and they had not yet completed their administrative internships. Some students actually reported rating themselves as less capable problem solvers in the spring because they now understood more clearly what they lacked in knowledge. One student exemplified this sentiment, “I might have graded myself higher in the fall than I did now . . . [I now can] self identify areas I could improve in that I was not as aware of.” Less confidence in the spring was a minority opinion, however. In a more typical response, another participant stated, “I feel much more prepared for that than I did at the beginning of the year.”

Overall, the most frequently discussed future need identified was experience, either through the administrative internship or work as a formal school administrator. Several students summarized this idea, “That real-world experience to have to deal with it without being able to talk to 8 other people before having to deal with it . . . until you are the person . . . you don’t know” and “They tell you all they want. You don’t know it until you are in it.” Overall, most participants perceived themselves to have grown as problem solvers, but they overwhelmingly recognized that they needed more learning and experience to become confident and effective problem solvers.

This study continues a research pathway about the development of problem-solving skills for administrators by focusing on their preparation. The participants did not see a significant increase in their problem-solving skills over the year-long course in educational leadership.

Whereas, this finding is not consistent with the findings of others who focused on the development of problem-solving skills for school leaders (Leithwood & Steinbach, 1995; Shapira-Lishchinsky, 2015), nor is it consistent with PBL research about the benefits of that approach for aspiring educational leaders (Copland, 2000; Hallinger & Bridges, 2017), it is important to note that the participants in this study were at a different point in their careers. First, they were aspirants, as opposed to practicing leaders. Also, the studied intervention (scenarios) was not the same or nearly as comprehensive as the prescriptive PBL approach. Further, unlike the participants in either the practicing leader or PBL studies, because these individuals had not yet had their internship experiences, they had no practical work as educational leaders. This theme of lacking practical experience was observed in both open-ended responses and focus group interviews, with participants pointing to their upcoming internship experiences, or even their eventual work as administrators, as a key missing piece of their preparation.

Despite the participants’ lack of real gains across the year of preparation in their problem- solving scores, the participants did, generally, report an increase in their confidence in problem solving, which they attributed to a number of factors. The first was the theme of real-world context. This finding was consistent with others who have advocated for teaching problem solving through real-world scenarios (Duke, 2014; Leithwood & Steinbach, 1992, 1995; Myran & Sutherland, 2016; Shapira-Lishchinsky, 2015). This study further adds to this conversation, not only a corroboration of the importance of this method (at least in aspiring leaders’ minds), but also that participants specifically recognized their professors’ experiences as school administrators as important for providing examples, context, and credibility to the work in the classroom.

In addition to the scenario approach, the participants also recognized the importance of learning from one another. In addition to the experiences of their practitioner-professors, many participants espoused the value of hearing the diverse perspectives of other students. The use of peer discussion was also an element of instruction in the referenced studies (Leithwood & Steinbach, 1995; Shapira-Lishchinsky, 2015), corroborating the power of aspiring leaders learning from one another and supporting existing literature about the social nature of problem solving (Berger & Luckmann, 1966; Leithwood & Steinbach, 1992; Vygotsky, 1978).

Finally, the ultimate theme identified through this study is the need for real-world experience in the field as an administrator or intern. It is simply not enough to learn about problem solving or learn the background knowledge needed to solve problems, even when the problems presented are real-world in nature. Scenarios are not enough for aspiring leaders to perceive their problem-solving abilities to be adequate or for their actual problem-solving abilities to improve. They need to be, as some of the participants reasoned, in positions of actual responsibility, where the weight of their decisions will have tangible impacts on stakeholders, including students.

The study of participants’ responses to the scenarios connected to the Four Frames model of Bolman and Deal (2008). The element for which participants received the consistently highest scores was identifying solution processes. This area might most logically be connected to the structural and human resource frames, as solutions typically involve working to meet individuals’ needs, as is necessary in the human resource frame, and attending to protocols and procedures, which is the essence of the structural frame. As identified above, the political and symbolic frames have been cited by the authors as the most underdeveloped by educational leaders, and this assertion is corroborated by the finding in this study that participants struggled the most with identifying constraints, which can sometimes arise from an understanding of the competing personal interests in an organization (political frame) and the underlying meaning behind aspects of an organization (symbolic frame), such as unspoken rules and traditions. The lack of success identifying constraints is also consistent with participants’ statements that they needed actual experiences in leadership roles, during which they would likely encounter, firsthand, the types of constraints they were unable to articulate for the given scenarios. Simply, they had not yet “lived” these types of obstacles.

The study includes several notable limitations. First, the study’s size is limited, particularly with only 20 participants’ data available for the matched pairs analysis. Further, this study was conducted at one university, within one particular certification program, and over three sections of one course, which represented about one-half of the time students spend in the program. It is likely that more gains in problem-solving ability and confidence would have been observed if this study was continued through the internship year. Also, the study did not include a control group. The lack of an experimental design limits the power of conclusions about causality. However, this limitation is mitigated by two factors. First, the results did not indicate a statistically significant improvement, so there is not a need to attribute a gain score to a particular variable (i.e. use of scenarios), anyway, and, second, the qualitative results did reveal the perceived value for participants in the use of scenarios, without any prompting of the researcher. Finally, the participant pool was not particularly diverse, though this fact is not particularly unusual for the selected university, in general, representing a contemporary challenge the university’s state is facing to educate its increasingly diverse student population, with a teaching and administrative workforce that is predominantly White.

The findings in this study invite further research. In addressing some of the limitations identified here, expanding this study to include aspiring administrators across other institutions representing different areas of the United States and other developed countries, would provide a more generalizable set of results. Further, studying the development of problem-solving skills during the administrative internship experience would also add to the work outlined here by considering the practical experience of participants.

In short, this study illustrates for those who prepare educational leaders the value of using scenarios in increasing aspiring leaders’ confidence and knowledge. However, intuitively, scenarios alone are not enough to engender significant change in their actual problem-solving abilities. Whereas, real-world context is important to the development of aspiring educational leaders’ problem-solving skills, the best context is likely to be the real work of administration.

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Author Biography

Dr. Jeremy Visone is an Assistant Professor of Educational Leadership, Policy, & Instructional Technology. Until 2016, he worked as an administrator at both the elementary and secondary levels, most recently at Anna Reynolds Elementary School, a National Blue Ribbon School in 2016. Dr. Visone can be reached at  [email protected] .

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• Published: 11 January 2023

The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature

• Enwei Xu   ORCID: orcid.org/0000-0001-6424-8169 1 ,
• Wei Wang 1 &
• Qingxia Wang 1  

Humanities and Social Sciences Communications volume  10 , Article number:  16 ( 2023 ) Cite this article

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Collaborative problem-solving has been widely embraced in the classroom instruction of critical thinking, which is regarded as the core of curriculum reform based on key competencies in the field of education as well as a key competence for learners in the 21st century. However, the effectiveness of collaborative problem-solving in promoting students’ critical thinking remains uncertain. This current research presents the major findings of a meta-analysis of 36 pieces of the literature revealed in worldwide educational periodicals during the 21st century to identify the effectiveness of collaborative problem-solving in promoting students’ critical thinking and to determine, based on evidence, whether and to what extent collaborative problem solving can result in a rise or decrease in critical thinking. The findings show that (1) collaborative problem solving is an effective teaching approach to foster students’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]); (2) in respect to the dimensions of critical thinking, collaborative problem solving can significantly and successfully enhance students’ attitudinal tendencies (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI[0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI[0.58, 0.82]); and (3) the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have an impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. On the basis of these results, recommendations are made for further study and instruction to better support students’ critical thinking in the context of collaborative problem-solving.

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Introduction.

Although critical thinking has a long history in research, the concept of critical thinking, which is regarded as an essential competence for learners in the 21st century, has recently attracted more attention from researchers and teaching practitioners (National Research Council, 2012 ). Critical thinking should be the core of curriculum reform based on key competencies in the field of education (Peng and Deng, 2017 ) because students with critical thinking can not only understand the meaning of knowledge but also effectively solve practical problems in real life even after knowledge is forgotten (Kek and Huijser, 2011 ). The definition of critical thinking is not universal (Ennis, 1989 ; Castle, 2009 ; Niu et al., 2013 ). In general, the definition of critical thinking is a self-aware and self-regulated thought process (Facione, 1990 ; Niu et al., 2013 ). It refers to the cognitive skills needed to interpret, analyze, synthesize, reason, and evaluate information as well as the attitudinal tendency to apply these abilities (Halpern, 2001 ). The view that critical thinking can be taught and learned through curriculum teaching has been widely supported by many researchers (e.g., Kuncel, 2011 ; Leng and Lu, 2020 ), leading to educators’ efforts to foster it among students. In the field of teaching practice, there are three types of courses for teaching critical thinking (Ennis, 1989 ). The first is an independent curriculum in which critical thinking is taught and cultivated without involving the knowledge of specific disciplines; the second is an integrated curriculum in which critical thinking is integrated into the teaching of other disciplines as a clear teaching goal; and the third is a mixed curriculum in which critical thinking is taught in parallel to the teaching of other disciplines for mixed teaching training. Furthermore, numerous measuring tools have been developed by researchers and educators to measure critical thinking in the context of teaching practice. These include standardized measurement tools, such as WGCTA, CCTST, CCTT, and CCTDI, which have been verified by repeated experiments and are considered effective and reliable by international scholars (Facione and Facione, 1992 ). In short, descriptions of critical thinking, including its two dimensions of attitudinal tendency and cognitive skills, different types of teaching courses, and standardized measurement tools provide a complex normative framework for understanding, teaching, and evaluating critical thinking.

Cultivating critical thinking in curriculum teaching can start with a problem, and one of the most popular critical thinking instructional approaches is problem-based learning (Liu et al., 2020 ). Duch et al. ( 2001 ) noted that problem-based learning in group collaboration is progressive active learning, which can improve students’ critical thinking and problem-solving skills. Collaborative problem-solving is the organic integration of collaborative learning and problem-based learning, which takes learners as the center of the learning process and uses problems with poor structure in real-world situations as the starting point for the learning process (Liang et al., 2017 ). Students learn the knowledge needed to solve problems in a collaborative group, reach a consensus on problems in the field, and form solutions through social cooperation methods, such as dialogue, interpretation, questioning, debate, negotiation, and reflection, thus promoting the development of learners’ domain knowledge and critical thinking (Cindy, 2004 ; Liang et al., 2017 ).

Collaborative problem-solving has been widely used in the teaching practice of critical thinking, and several studies have attempted to conduct a systematic review and meta-analysis of the empirical literature on critical thinking from various perspectives. However, little attention has been paid to the impact of collaborative problem-solving on critical thinking. Therefore, the best approach for developing and enhancing critical thinking throughout collaborative problem-solving is to examine how to implement critical thinking instruction; however, this issue is still unexplored, which means that many teachers are incapable of better instructing critical thinking (Leng and Lu, 2020 ; Niu et al., 2013 ). For example, Huber ( 2016 ) provided the meta-analysis findings of 71 publications on gaining critical thinking over various time frames in college with the aim of determining whether critical thinking was truly teachable. These authors found that learners significantly improve their critical thinking while in college and that critical thinking differs with factors such as teaching strategies, intervention duration, subject area, and teaching type. The usefulness of collaborative problem-solving in fostering students’ critical thinking, however, was not determined by this study, nor did it reveal whether there existed significant variations among the different elements. A meta-analysis of 31 pieces of educational literature was conducted by Liu et al. ( 2020 ) to assess the impact of problem-solving on college students’ critical thinking. These authors found that problem-solving could promote the development of critical thinking among college students and proposed establishing a reasonable group structure for problem-solving in a follow-up study to improve students’ critical thinking. Additionally, previous empirical studies have reached inconclusive and even contradictory conclusions about whether and to what extent collaborative problem-solving increases or decreases critical thinking levels. As an illustration, Yang et al. ( 2008 ) carried out an experiment on the integrated curriculum teaching of college students based on a web bulletin board with the goal of fostering participants’ critical thinking in the context of collaborative problem-solving. These authors’ research revealed that through sharing, debating, examining, and reflecting on various experiences and ideas, collaborative problem-solving can considerably enhance students’ critical thinking in real-life problem situations. In contrast, collaborative problem-solving had a positive impact on learners’ interaction and could improve learning interest and motivation but could not significantly improve students’ critical thinking when compared to traditional classroom teaching, according to research by Naber and Wyatt ( 2014 ) and Sendag and Odabasi ( 2009 ) on undergraduate and high school students, respectively.

The above studies show that there is inconsistency regarding the effectiveness of collaborative problem-solving in promoting students’ critical thinking. Therefore, it is essential to conduct a thorough and trustworthy review to detect and decide whether and to what degree collaborative problem-solving can result in a rise or decrease in critical thinking. Meta-analysis is a quantitative analysis approach that is utilized to examine quantitative data from various separate studies that are all focused on the same research topic. This approach characterizes the effectiveness of its impact by averaging the effect sizes of numerous qualitative studies in an effort to reduce the uncertainty brought on by independent research and produce more conclusive findings (Lipsey and Wilson, 2001 ).

This paper used a meta-analytic approach and carried out a meta-analysis to examine the effectiveness of collaborative problem-solving in promoting students’ critical thinking in order to make a contribution to both research and practice. The following research questions were addressed by this meta-analysis:

What is the overall effect size of collaborative problem-solving in promoting students’ critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills)?

How are the disparities between the study conclusions impacted by various moderating variables if the impacts of various experimental designs in the included studies are heterogeneous?

This research followed the strict procedures (e.g., database searching, identification, screening, eligibility, merging, duplicate removal, and analysis of included studies) of Cooper’s ( 2010 ) proposed meta-analysis approach for examining quantitative data from various separate studies that are all focused on the same research topic. The relevant empirical research that appeared in worldwide educational periodicals within the 21st century was subjected to this meta-analysis using Rev-Man 5.4. The consistency of the data extracted separately by two researchers was tested using Cohen’s kappa coefficient, and a publication bias test and a heterogeneity test were run on the sample data to ascertain the quality of this meta-analysis.

Data sources and search strategies

There were three stages to the data collection process for this meta-analysis, as shown in Fig. 1 , which shows the number of articles included and eliminated during the selection process based on the statement and study eligibility criteria.

This flowchart shows the number of records identified, included and excluded in the article.

First, the databases used to systematically search for relevant articles were the journal papers of the Web of Science Core Collection and the Chinese Core source journal, as well as the Chinese Social Science Citation Index (CSSCI) source journal papers included in CNKI. These databases were selected because they are credible platforms that are sources of scholarly and peer-reviewed information with advanced search tools and contain literature relevant to the subject of our topic from reliable researchers and experts. The search string with the Boolean operator used in the Web of Science was “TS = (((“critical thinking” or “ct” and “pretest” or “posttest”) or (“critical thinking” or “ct” and “control group” or “quasi experiment” or “experiment”)) and (“collaboration” or “collaborative learning” or “CSCL”) and (“problem solving” or “problem-based learning” or “PBL”))”. The research area was “Education Educational Research”, and the search period was “January 1, 2000, to December 30, 2021”. A total of 412 papers were obtained. The search string with the Boolean operator used in the CNKI was “SU = (‘critical thinking’*‘collaboration’ + ‘critical thinking’*‘collaborative learning’ + ‘critical thinking’*‘CSCL’ + ‘critical thinking’*‘problem solving’ + ‘critical thinking’*‘problem-based learning’ + ‘critical thinking’*‘PBL’ + ‘critical thinking’*‘problem oriented’) AND FT = (‘experiment’ + ‘quasi experiment’ + ‘pretest’ + ‘posttest’ + ‘empirical study’)” (translated into Chinese when searching). A total of 56 studies were found throughout the search period of “January 2000 to December 2021”. From the databases, all duplicates and retractions were eliminated before exporting the references into Endnote, a program for managing bibliographic references. In all, 466 studies were found.

Second, the studies that matched the inclusion and exclusion criteria for the meta-analysis were chosen by two researchers after they had reviewed the abstracts and titles of the gathered articles, yielding a total of 126 studies.

Third, two researchers thoroughly reviewed each included article’s whole text in accordance with the inclusion and exclusion criteria. Meanwhile, a snowball search was performed using the references and citations of the included articles to ensure complete coverage of the articles. Ultimately, 36 articles were kept.

Two researchers worked together to carry out this entire process, and a consensus rate of almost 94.7% was reached after discussion and negotiation to clarify any emerging differences.

Eligibility criteria

Since not all the retrieved studies matched the criteria for this meta-analysis, eligibility criteria for both inclusion and exclusion were developed as follows:

The publication language of the included studies was limited to English and Chinese, and the full text could be obtained. Articles that did not meet the publication language and articles not published between 2000 and 2021 were excluded.

The research design of the included studies must be empirical and quantitative studies that can assess the effect of collaborative problem-solving on the development of critical thinking. Articles that could not identify the causal mechanisms by which collaborative problem-solving affects critical thinking, such as review articles and theoretical articles, were excluded.

The research method of the included studies must feature a randomized control experiment or a quasi-experiment, or a natural experiment, which have a higher degree of internal validity with strong experimental designs and can all plausibly provide evidence that critical thinking and collaborative problem-solving are causally related. Articles with non-experimental research methods, such as purely correlational or observational studies, were excluded.

The participants of the included studies were only students in school, including K-12 students and college students. Articles in which the participants were non-school students, such as social workers or adult learners, were excluded.

The research results of the included studies must mention definite signs that may be utilized to gauge critical thinking’s impact (e.g., sample size, mean value, or standard deviation). Articles that lacked specific measurement indicators for critical thinking and could not calculate the effect size were excluded.

Data coding design

In order to perform a meta-analysis, it is necessary to collect the most important information from the articles, codify that information’s properties, and convert descriptive data into quantitative data. Therefore, this study designed a data coding template (see Table 1 ). Ultimately, 16 coding fields were retained.

The designed data-coding template consisted of three pieces of information. Basic information about the papers was included in the descriptive information: the publishing year, author, serial number, and title of the paper.

The variable information for the experimental design had three variables: the independent variable (instruction method), the dependent variable (critical thinking), and the moderating variable (learning stage, teaching type, intervention duration, learning scaffold, group size, measuring tool, and subject area). Depending on the topic of this study, the intervention strategy, as the independent variable, was coded into collaborative and non-collaborative problem-solving. The dependent variable, critical thinking, was coded as a cognitive skill and an attitudinal tendency. And seven moderating variables were created by grouping and combining the experimental design variables discovered within the 36 studies (see Table 1 ), where learning stages were encoded as higher education, high school, middle school, and primary school or lower; teaching types were encoded as mixed courses, integrated courses, and independent courses; intervention durations were encoded as 0–1 weeks, 1–4 weeks, 4–12 weeks, and more than 12 weeks; group sizes were encoded as 2–3 persons, 4–6 persons, 7–10 persons, and more than 10 persons; learning scaffolds were encoded as teacher-supported learning scaffold, technique-supported learning scaffold, and resource-supported learning scaffold; measuring tools were encoded as standardized measurement tools (e.g., WGCTA, CCTT, CCTST, and CCTDI) and self-adapting measurement tools (e.g., modified or made by researchers); and subject areas were encoded according to the specific subjects used in the 36 included studies.

The data information contained three metrics for measuring critical thinking: sample size, average value, and standard deviation. It is vital to remember that studies with various experimental designs frequently adopt various formulas to determine the effect size. And this paper used Morris’ proposed standardized mean difference (SMD) calculation formula ( 2008 , p. 369; see Supplementary Table S3 ).

Procedure for extracting and coding data

According to the data coding template (see Table 1 ), the 36 papers’ information was retrieved by two researchers, who then entered them into Excel (see Supplementary Table S1 ). The results of each study were extracted separately in the data extraction procedure if an article contained numerous studies on critical thinking, or if a study assessed different critical thinking dimensions. For instance, Tiwari et al. ( 2010 ) used four time points, which were viewed as numerous different studies, to examine the outcomes of critical thinking, and Chen ( 2013 ) included the two outcome variables of attitudinal tendency and cognitive skills, which were regarded as two studies. After discussion and negotiation during data extraction, the two researchers’ consistency test coefficients were roughly 93.27%. Supplementary Table S2 details the key characteristics of the 36 included articles with 79 effect quantities, including descriptive information (e.g., the publishing year, author, serial number, and title of the paper), variable information (e.g., independent variables, dependent variables, and moderating variables), and data information (e.g., mean values, standard deviations, and sample size). Following that, testing for publication bias and heterogeneity was done on the sample data using the Rev-Man 5.4 software, and then the test results were used to conduct a meta-analysis.

Publication bias test

When the sample of studies included in a meta-analysis does not accurately reflect the general status of research on the relevant subject, publication bias is said to be exhibited in this research. The reliability and accuracy of the meta-analysis may be impacted by publication bias. Due to this, the meta-analysis needs to check the sample data for publication bias (Stewart et al., 2006 ). A popular method to check for publication bias is the funnel plot; and it is unlikely that there will be publishing bias when the data are equally dispersed on either side of the average effect size and targeted within the higher region. The data are equally dispersed within the higher portion of the efficient zone, consistent with the funnel plot connected with this analysis (see Fig. 2 ), indicating that publication bias is unlikely in this situation.

This funnel plot shows the result of publication bias of 79 effect quantities across 36 studies.

Heterogeneity test

To select the appropriate effect models for the meta-analysis, one might use the results of a heterogeneity test on the data effect sizes. In a meta-analysis, it is common practice to gauge the degree of data heterogeneity using the I 2 value, and I 2  ≥ 50% is typically understood to denote medium-high heterogeneity, which calls for the adoption of a random effect model; if not, a fixed effect model ought to be applied (Lipsey and Wilson, 2001 ). The findings of the heterogeneity test in this paper (see Table 2 ) revealed that I 2 was 86% and displayed significant heterogeneity ( P  < 0.01). To ensure accuracy and reliability, the overall effect size ought to be calculated utilizing the random effect model.

The analysis of the overall effect size

This meta-analysis utilized a random effect model to examine 79 effect quantities from 36 studies after eliminating heterogeneity. In accordance with Cohen’s criterion (Cohen, 1992 ), it is abundantly clear from the analysis results, which are shown in the forest plot of the overall effect (see Fig. 3 ), that the cumulative impact size of cooperative problem-solving is 0.82, which is statistically significant ( z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]), and can encourage learners to practice critical thinking.

This forest plot shows the analysis result of the overall effect size across 36 studies.

In addition, this study examined two distinct dimensions of critical thinking to better understand the precise contributions that collaborative problem-solving makes to the growth of critical thinking. The findings (see Table 3 ) indicate that collaborative problem-solving improves cognitive skills (ES = 0.70) and attitudinal tendency (ES = 1.17), with significant intergroup differences (chi 2  = 7.95, P  < 0.01). Although collaborative problem-solving improves both dimensions of critical thinking, it is essential to point out that the improvements in students’ attitudinal tendency are much more pronounced and have a significant comprehensive effect (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]), whereas gains in learners’ cognitive skill are slightly improved and are just above average. (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

The analysis of moderator effect size

The whole forest plot’s 79 effect quantities underwent a two-tailed test, which revealed significant heterogeneity ( I 2  = 86%, z  = 12.78, P  < 0.01), indicating differences between various effect sizes that may have been influenced by moderating factors other than sampling error. Therefore, exploring possible moderating factors that might produce considerable heterogeneity was done using subgroup analysis, such as the learning stage, learning scaffold, teaching type, group size, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, in order to further explore the key factors that influence critical thinking. The findings (see Table 4 ) indicate that various moderating factors have advantageous effects on critical thinking. In this situation, the subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), learning scaffold (chi 2  = 9.03, P  < 0.01), and teaching type (chi 2  = 7.20, P  < 0.05) are all significant moderators that can be applied to support the cultivation of critical thinking. However, since the learning stage and the measuring tools did not significantly differ among intergroup (chi 2  = 3.15, P  = 0.21 > 0.05, and chi 2  = 0.08, P  = 0.78 > 0.05), we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving. These are the precise outcomes, as follows:

Various learning stages influenced critical thinking positively, without significant intergroup differences (chi 2  = 3.15, P  = 0.21 > 0.05). High school was first on the list of effect sizes (ES = 1.36, P  < 0.01), then higher education (ES = 0.78, P  < 0.01), and middle school (ES = 0.73, P  < 0.01). These results show that, despite the learning stage’s beneficial influence on cultivating learners’ critical thinking, we are unable to explain why it is essential for cultivating critical thinking in the context of collaborative problem-solving.

Different teaching types had varying degrees of positive impact on critical thinking, with significant intergroup differences (chi 2  = 7.20, P  < 0.05). The effect size was ranked as follows: mixed courses (ES = 1.34, P  < 0.01), integrated courses (ES = 0.81, P  < 0.01), and independent courses (ES = 0.27, P  < 0.01). These results indicate that the most effective approach to cultivate critical thinking utilizing collaborative problem solving is through the teaching type of mixed courses.

Various intervention durations significantly improved critical thinking, and there were significant intergroup differences (chi 2  = 12.18, P  < 0.01). The effect sizes related to this variable showed a tendency to increase with longer intervention durations. The improvement in critical thinking reached a significant level (ES = 0.85, P  < 0.01) after more than 12 weeks of training. These findings indicate that the intervention duration and critical thinking’s impact are positively correlated, with a longer intervention duration having a greater effect.

Different learning scaffolds influenced critical thinking positively, with significant intergroup differences (chi 2  = 9.03, P  < 0.01). The resource-supported learning scaffold (ES = 0.69, P  < 0.01) acquired a medium-to-higher level of impact, the technique-supported learning scaffold (ES = 0.63, P  < 0.01) also attained a medium-to-higher level of impact, and the teacher-supported learning scaffold (ES = 0.92, P  < 0.01) displayed a high level of significant impact. These results show that the learning scaffold with teacher support has the greatest impact on cultivating critical thinking.

Various group sizes influenced critical thinking positively, and the intergroup differences were statistically significant (chi 2  = 8.77, P  < 0.05). Critical thinking showed a general declining trend with increasing group size. The overall effect size of 2–3 people in this situation was the biggest (ES = 0.99, P  < 0.01), and when the group size was greater than 7 people, the improvement in critical thinking was at the lower-middle level (ES < 0.5, P  < 0.01). These results show that the impact on critical thinking is positively connected with group size, and as group size grows, so does the overall impact.

Various measuring tools influenced critical thinking positively, with significant intergroup differences (chi 2  = 0.08, P  = 0.78 > 0.05). In this situation, the self-adapting measurement tools obtained an upper-medium level of effect (ES = 0.78), whereas the complete effect size of the standardized measurement tools was the largest, achieving a significant level of effect (ES = 0.84, P  < 0.01). These results show that, despite the beneficial influence of the measuring tool on cultivating critical thinking, we are unable to explain why it is crucial in fostering the growth of critical thinking by utilizing the approach of collaborative problem-solving.

Different subject areas had a greater impact on critical thinking, and the intergroup differences were statistically significant (chi 2  = 13.36, P  < 0.05). Mathematics had the greatest overall impact, achieving a significant level of effect (ES = 1.68, P  < 0.01), followed by science (ES = 1.25, P  < 0.01) and medical science (ES = 0.87, P  < 0.01), both of which also achieved a significant level of effect. Programming technology was the least effective (ES = 0.39, P  < 0.01), only having a medium-low degree of effect compared to education (ES = 0.72, P  < 0.01) and other fields (such as language, art, and social sciences) (ES = 0.58, P  < 0.01). These results suggest that scientific fields (e.g., mathematics, science) may be the most effective subject areas for cultivating critical thinking utilizing the approach of collaborative problem-solving.

The effectiveness of collaborative problem solving with regard to teaching critical thinking

According to this meta-analysis, using collaborative problem-solving as an intervention strategy in critical thinking teaching has a considerable amount of impact on cultivating learners’ critical thinking as a whole and has a favorable promotional effect on the two dimensions of critical thinking. According to certain studies, collaborative problem solving, the most frequently used critical thinking teaching strategy in curriculum instruction can considerably enhance students’ critical thinking (e.g., Liang et al., 2017 ; Liu et al., 2020 ; Cindy, 2004 ). This meta-analysis provides convergent data support for the above research views. Thus, the findings of this meta-analysis not only effectively address the first research query regarding the overall effect of cultivating critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills) utilizing the approach of collaborative problem-solving, but also enhance our confidence in cultivating critical thinking by using collaborative problem-solving intervention approach in the context of classroom teaching.

Furthermore, the associated improvements in attitudinal tendency are much stronger, but the corresponding improvements in cognitive skill are only marginally better. According to certain studies, cognitive skill differs from the attitudinal tendency in classroom instruction; the cultivation and development of the former as a key ability is a process of gradual accumulation, while the latter as an attitude is affected by the context of the teaching situation (e.g., a novel and exciting teaching approach, challenging and rewarding tasks) (Halpern, 2001 ; Wei and Hong, 2022 ). Collaborative problem-solving as a teaching approach is exciting and interesting, as well as rewarding and challenging; because it takes the learners as the focus and examines problems with poor structure in real situations, and it can inspire students to fully realize their potential for problem-solving, which will significantly improve their attitudinal tendency toward solving problems (Liu et al., 2020 ). Similar to how collaborative problem-solving influences attitudinal tendency, attitudinal tendency impacts cognitive skill when attempting to solve a problem (Liu et al., 2020 ; Zhang et al., 2022 ), and stronger attitudinal tendencies are associated with improved learning achievement and cognitive ability in students (Sison, 2008 ; Zhang et al., 2022 ). It can be seen that the two specific dimensions of critical thinking as well as critical thinking as a whole are affected by collaborative problem-solving, and this study illuminates the nuanced links between cognitive skills and attitudinal tendencies with regard to these two dimensions of critical thinking. To fully develop students’ capacity for critical thinking, future empirical research should pay closer attention to cognitive skills.

The moderating effects of collaborative problem solving with regard to teaching critical thinking

In order to further explore the key factors that influence critical thinking, exploring possible moderating effects that might produce considerable heterogeneity was done using subgroup analysis. The findings show that the moderating factors, such as the teaching type, learning stage, group size, learning scaffold, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, could all support the cultivation of collaborative problem-solving in critical thinking. Among them, the effect size differences between the learning stage and measuring tool are not significant, which does not explain why these two factors are crucial in supporting the cultivation of critical thinking utilizing the approach of collaborative problem-solving.

In terms of the learning stage, various learning stages influenced critical thinking positively without significant intergroup differences, indicating that we are unable to explain why it is crucial in fostering the growth of critical thinking.

Although high education accounts for 70.89% of all empirical studies performed by researchers, high school may be the appropriate learning stage to foster students’ critical thinking by utilizing the approach of collaborative problem-solving since it has the largest overall effect size. This phenomenon may be related to student’s cognitive development, which needs to be further studied in follow-up research.

With regard to teaching type, mixed course teaching may be the best teaching method to cultivate students’ critical thinking. Relevant studies have shown that in the actual teaching process if students are trained in thinking methods alone, the methods they learn are isolated and divorced from subject knowledge, which is not conducive to their transfer of thinking methods; therefore, if students’ thinking is trained only in subject teaching without systematic method training, it is challenging to apply to real-world circumstances (Ruggiero, 2012 ; Hu and Liu, 2015 ). Teaching critical thinking as mixed course teaching in parallel to other subject teachings can achieve the best effect on learners’ critical thinking, and explicit critical thinking instruction is more effective than less explicit critical thinking instruction (Bensley and Spero, 2014 ).

In terms of the intervention duration, with longer intervention times, the overall effect size shows an upward tendency. Thus, the intervention duration and critical thinking’s impact are positively correlated. Critical thinking, as a key competency for students in the 21st century, is difficult to get a meaningful improvement in a brief intervention duration. Instead, it could be developed over a lengthy period of time through consistent teaching and the progressive accumulation of knowledge (Halpern, 2001 ; Hu and Liu, 2015 ). Therefore, future empirical studies ought to take these restrictions into account throughout a longer period of critical thinking instruction.

With regard to group size, a group size of 2–3 persons has the highest effect size, and the comprehensive effect size decreases with increasing group size in general. This outcome is in line with some research findings; as an example, a group composed of two to four members is most appropriate for collaborative learning (Schellens and Valcke, 2006 ). However, the meta-analysis results also indicate that once the group size exceeds 7 people, small groups cannot produce better interaction and performance than large groups. This may be because the learning scaffolds of technique support, resource support, and teacher support improve the frequency and effectiveness of interaction among group members, and a collaborative group with more members may increase the diversity of views, which is helpful to cultivate critical thinking utilizing the approach of collaborative problem-solving.

With regard to the learning scaffold, the three different kinds of learning scaffolds can all enhance critical thinking. Among them, the teacher-supported learning scaffold has the largest overall effect size, demonstrating the interdependence of effective learning scaffolds and collaborative problem-solving. This outcome is in line with some research findings; as an example, a successful strategy is to encourage learners to collaborate, come up with solutions, and develop critical thinking skills by using learning scaffolds (Reiser, 2004 ; Xu et al., 2022 ); learning scaffolds can lower task complexity and unpleasant feelings while also enticing students to engage in learning activities (Wood et al., 2006 ); learning scaffolds are designed to assist students in using learning approaches more successfully to adapt the collaborative problem-solving process, and the teacher-supported learning scaffolds have the greatest influence on critical thinking in this process because they are more targeted, informative, and timely (Xu et al., 2022 ).

With respect to the measuring tool, despite the fact that standardized measurement tools (such as the WGCTA, CCTT, and CCTST) have been acknowledged as trustworthy and effective by worldwide experts, only 54.43% of the research included in this meta-analysis adopted them for assessment, and the results indicated no intergroup differences. These results suggest that not all teaching circumstances are appropriate for measuring critical thinking using standardized measurement tools. “The measuring tools for measuring thinking ability have limits in assessing learners in educational situations and should be adapted appropriately to accurately assess the changes in learners’ critical thinking.”, according to Simpson and Courtney ( 2002 , p. 91). As a result, in order to more fully and precisely gauge how learners’ critical thinking has evolved, we must properly modify standardized measuring tools based on collaborative problem-solving learning contexts.

With regard to the subject area, the comprehensive effect size of science departments (e.g., mathematics, science, medical science) is larger than that of language arts and social sciences. Some recent international education reforms have noted that critical thinking is a basic part of scientific literacy. Students with scientific literacy can prove the rationality of their judgment according to accurate evidence and reasonable standards when they face challenges or poorly structured problems (Kyndt et al., 2013 ), which makes critical thinking crucial for developing scientific understanding and applying this understanding to practical problem solving for problems related to science, technology, and society (Yore et al., 2007 ).

Suggestions for critical thinking teaching

Other than those stated in the discussion above, the following suggestions are offered for critical thinking instruction utilizing the approach of collaborative problem-solving.

First, teachers should put a special emphasis on the two core elements, which are collaboration and problem-solving, to design real problems based on collaborative situations. This meta-analysis provides evidence to support the view that collaborative problem-solving has a strong synergistic effect on promoting students’ critical thinking. Asking questions about real situations and allowing learners to take part in critical discussions on real problems during class instruction are key ways to teach critical thinking rather than simply reading speculative articles without practice (Mulnix, 2012 ). Furthermore, the improvement of students’ critical thinking is realized through cognitive conflict with other learners in the problem situation (Yang et al., 2008 ). Consequently, it is essential for teachers to put a special emphasis on the two core elements, which are collaboration and problem-solving, and design real problems and encourage students to discuss, negotiate, and argue based on collaborative problem-solving situations.

Second, teachers should design and implement mixed courses to cultivate learners’ critical thinking, utilizing the approach of collaborative problem-solving. Critical thinking can be taught through curriculum instruction (Kuncel, 2011 ; Leng and Lu, 2020 ), with the goal of cultivating learners’ critical thinking for flexible transfer and application in real problem-solving situations. This meta-analysis shows that mixed course teaching has a highly substantial impact on the cultivation and promotion of learners’ critical thinking. Therefore, teachers should design and implement mixed course teaching with real collaborative problem-solving situations in combination with the knowledge content of specific disciplines in conventional teaching, teach methods and strategies of critical thinking based on poorly structured problems to help students master critical thinking, and provide practical activities in which students can interact with each other to develop knowledge construction and critical thinking utilizing the approach of collaborative problem-solving.

Third, teachers should be more trained in critical thinking, particularly preservice teachers, and they also should be conscious of the ways in which teachers’ support for learning scaffolds can promote critical thinking. The learning scaffold supported by teachers had the greatest impact on learners’ critical thinking, in addition to being more directive, targeted, and timely (Wood et al., 2006 ). Critical thinking can only be effectively taught when teachers recognize the significance of critical thinking for students’ growth and use the proper approaches while designing instructional activities (Forawi, 2016 ). Therefore, with the intention of enabling teachers to create learning scaffolds to cultivate learners’ critical thinking utilizing the approach of collaborative problem solving, it is essential to concentrate on the teacher-supported learning scaffolds and enhance the instruction for teaching critical thinking to teachers, especially preservice teachers.

Implications and limitations

There are certain limitations in this meta-analysis, but future research can correct them. First, the search languages were restricted to English and Chinese, so it is possible that pertinent studies that were written in other languages were overlooked, resulting in an inadequate number of articles for review. Second, these data provided by the included studies are partially missing, such as whether teachers were trained in the theory and practice of critical thinking, the average age and gender of learners, and the differences in critical thinking among learners of various ages and genders. Third, as is typical for review articles, more studies were released while this meta-analysis was being done; therefore, it had a time limit. With the development of relevant research, future studies focusing on these issues are highly relevant and needed.

Conclusions

The subject of the magnitude of collaborative problem-solving’s impact on fostering students’ critical thinking, which received scant attention from other studies, was successfully addressed by this study. The question of the effectiveness of collaborative problem-solving in promoting students’ critical thinking was addressed in this study, which addressed a topic that had gotten little attention in earlier research. The following conclusions can be made:

Regarding the results obtained, collaborative problem solving is an effective teaching approach to foster learners’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]). With respect to the dimensions of critical thinking, collaborative problem-solving can significantly and effectively improve students’ attitudinal tendency, and the comprehensive effect is significant (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

As demonstrated by both the results and the discussion, there are varying degrees of beneficial effects on students’ critical thinking from all seven moderating factors, which were found across 36 studies. In this context, the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have a positive impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. Since the learning stage (chi 2  = 3.15, P  = 0.21 > 0.05) and measuring tools (chi 2  = 0.08, P  = 0.78 > 0.05) did not demonstrate any significant intergroup differences, we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving.

Data availability

All data generated or analyzed during this study are included within the article and its supplementary information files, and the supplementary information files are available in the Dataverse repository: https://doi.org/10.7910/DVN/IPFJO6 .

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Acknowledgements

This research was supported by the graduate scientific research and innovation project of Xinjiang Uygur Autonomous Region named “Research on in-depth learning of high school information technology courses for the cultivation of computing thinking” (No. XJ2022G190) and the independent innovation fund project for doctoral students of the College of Educational Science of Xinjiang Normal University named “Research on project-based teaching of high school information technology courses from the perspective of discipline core literacy” (No. XJNUJKYA2003).

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Xu, E., Wang, W. & Wang, Q. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanit Soc Sci Commun 10 , 16 (2023). https://doi.org/10.1057/s41599-023-01508-1

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Tips and Techniques

Expert vs. novice problem solvers, communicate.

• Have students  identify specific problems, difficulties, or confusions . Don’t waste time working through problems that students already understand.
• If students are unable to articulate their concerns, determine where they are having trouble by  asking them to identify the specific concepts or principles associated with the problem.
• In a one-on-one tutoring session, ask the student to  work his/her problem out loud . This slows down the thinking process, making it more accurate and allowing you to access understanding.
• When working with larger groups you can ask students to provide a written “two-column solution.” Have students write up their solution to a problem by putting all their calculations in one column and all of their reasoning (in complete sentences) in the other column. This helps them to think critically about their own problem solving and helps you to more easily identify where they may be having problems. Two-Column Solution (Math) Two-Column Solution (Physics)

Encourage Independence

• Model the problem solving process rather than just giving students the answer. As you work through the problem, consider how a novice might struggle with the concepts and make your thinking clear
• Have students work through problems on their own. Ask directing questions or give helpful suggestions, but  provide only minimal assistance and only when needed to overcome obstacles.
• Don’t fear  group work ! Students can frequently help each other, and talking about a problem helps them think more critically about the steps needed to solve the problem. Additionally, group work helps students realize that problems often have multiple solution strategies, some that might be more effective than others

Be sensitive

• Frequently, when working problems, students are unsure of themselves. This lack of confidence may hamper their learning. It is important to recognize this when students come to us for help, and to give each student some feeling of mastery. Do this by providing  positive reinforcement to let students know when they have mastered a new concept or skill.

Encourage Thoroughness and Patience

• Try to communicate that  the process is more important than the answer so that the student learns that it is OK to not have an instant solution. This is learned through your acceptance of his/her pace of doing things, through your refusal to let anxiety pressure you into giving the right answer, and through your example of problem solving through a step-by step process.

Experts (teachers) in a particular field are often so fluent in solving problems from that field that they can find it difficult to articulate the problem solving principles and strategies they use to novices (students) in their field because these principles and strategies are second nature to the expert. To teach students problem solving skills,  a teacher should be aware of principles and strategies of good problem solving in his or her discipline .

The mathematician George Polya captured the problem solving principles and strategies he used in his discipline in the book  How to Solve It: A New Aspect of Mathematical Method (Princeton University Press, 1957). The book includes  a summary of Polya’s problem solving heuristic as well as advice on the teaching of problem solving.

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Principals and Problem-Solving

• Posted November 13, 2014
• By Bari Walsh

If you’re a school principal, how does context matter when you’re facing a difficult problem? Where do you find your source of support, and do the people you turn to actually help you to reach effective solutions? Ebony Bridwell-Mitchell explores these questions in a paper published in Organization Science with co-author Theresa Lant, finding that the type of people principals surround themselves with, and to whom they turn for counsel, has a lot to do with how they think about their role and their challenges in the first place. This “cognitive context,” she found, helps determine their social context and is predictive of how they’ll go about solving problems or advancing their agenda.

Bridwell-Mitchell studies educational leadership, management, and organizations, often exploring the tension between structure and agency — “how we make choices within constraints,” as she describes it. “A lot of education policy is focused on what we can do to get people to make better choices — how we can spur them to be more gritty, or how we can incentivize them. But all the choices people make — even if they’re properly incentivized, even if they’re extra gritty — are constrained in some way by context. It turns out that when you look at differences across individuals, what best explains the variation is context. People in one context tend to think and do things a certain way, and very differently than people in another context.”

How Context Matters

In the recent paper, she set out to explore just how context matters when school principals are faced with decisions. She wanted to understand not only how principals’ social networks mattered, but also how cognitive context mattered — how they thought about or framed their problems.

She investigated two different ways in which principals might frame a pressing problem: as political, having to do with influence or power, or as strategic, having to do with performance and resources. She wanted to see whether that framing had an effect on the kinds of people they chose to go to for help.

She found that when people frame their problems politically, they are more likely to turn to advisors they think are trustworthy and have influence. But when they frame problems as being strategic, they are more likely to turn to people they think are accessible and have resources.

The Takeaway

What does all this mean in terms of helping principals solve problems? “If people have persistent patterns in how they see problems, then they have a tendency to choose certain kinds of people, irrespective of whether that’s what the problem actually is or those are the people they actually need,” says Bridwell-Mitchell. “You can imagine that people might be thinking about the problem in the wrong way and choosing the wrong people and not ending up with the solutions they need.”

“It really gives us an incentive to invest in what people often call shared decision making or shared leadership,” she continues. “What this is saying is, you need people to help you think carefully about these problems, so you can make sure you’re conceptualizing them in ways that will get you to the right people for help.”

Bridwell-Mitchell is doing a follow-up study to assess which cognitive contexts and social contexts may be more effective at solving which types of problems. She’s asking groups of principals to work through the issues involved in two randomly assigned scenarios, one about bullying and one about increasing achievement in middle-performing students, and then to come up with a solution. A set of experts — other principals and field experts — will assess and rate the solutions. The goal is to shed light on which factors were more helpful in arriving at effective solutions — cognitive framing, social context, or a combination of the two.

The bottom line is that context matters — perhaps more than any other factor — in effective leadership, says Bridwell-Mitchell. “If we’re not thinking about how much context matters, and how to change context, we’re losing most of the leverage that we have to actually get people to behave differently.”

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Decision-Making and Problem Solving

• First Online: 19 August 2017

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• Christos Saitis 3 &
• Anna Saiti 4  

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Decision-making is a fundamental activity that significantly influences the efficiency of an organization. That is because this activity is at the heart of management in every typical organization. Therefore, one of the key prerequisites for an educational leader is to have effective decision-making skills.

During an average working day, a school head makes different kinds of decisions, regardless of their importance. Indeed, within the wider framework of a school unit’s activities, the resolution of problems and the decision-making are two fundamental elements that assess, to a significant degree, the effectiveness of the school’s performance. Consequently, it is absolutely necessary for all educational leaders to understand the process of decision-making and of resolving problems, since the sheer existence of schools (and indeed all typical organizations) depends on their decision-making processes. This chapter:

Analyses the meaning, types and the procedures for effective decision-making

Outlines suggestions (e.g. careful assessment of the credibility of information) on how to avoid wrong decisions

Emphasizes human weakness in the decision-making process (e.g. when a manager puts too much emphasis on the initial information and does not assess whether or not it is creditable)

Examines the meaning of the term “problem” and also summarizes the process and methods of problem solving in the field of education

Presents case studies related to the school reality

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Saitis, C., Saiti, A. (2018). Decision-Making and Problem Solving. In: Initiation of Educators into Educational Management Secrets. Springer, Cham. https://doi.org/10.1007/978-3-319-47277-5_3

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Introduction

Practitioners in various domains are often faced with ill-structured problems. For example, teachers devise lesson plans that consider learners’ prior knowledge, curriculum guidelines, and classroom management strategies. Similarly, engineers must develop products that meet safety standards, yet achieve project guidelines that meet client needs. Given the types of problems that practitioners face in everyday decision-making, educators have increasingly begun to adopt inquiry-based learning, which better exposes learners to the types of issues faced within a domain (Hung et al., 2019; Koehler & Vilarinho-Pereira, 2021). This instructional approach includes multiple changes to the educational experience when compared to the teacher-centric classroom approach (Reigeluth & Carr-Chellman, 2009). As opposed to a didactic strategy to instruction, students take ownership of their learning and generate questions among their peers, while teachers serve as facilitators (Lazonder & Harmsen, 2016; Loyens & Rikers, 2011; Savery, 2009). The central focus of these strategies also includes ill-structured cases that are similar to the types of problems practitioners face. The complexity of these problems often consists of interconnected variables (latent, salient) and multiple perspectives, so there is rarely a single predetermined solution that satisfies all options (Ifenthaler, 2014). Additionally, these problems are challenging because they include multiple criteria for evaluation (Jonassen, 2011b; Ju & Choi, 2017), which makes it challenging to definitively determine when a ‘right’ answer has been achieved.

There are a number of skillsets needed for problem-solving instructional strategies, such as the inquiry process (Glazewski & Hmelo-Silver, 2018), collaboration (Koehler & Vilarinho-Pereira, 2021), and argumentation (Noroozi et al., 2017). Another important element of problem-solving includes decision-making; that is, the process by which individuals make choices as they resolve the ill-structured case. Understanding decision-making is important because individuals engage in a myriad of choices throughout the problem representation and solution generation phases of problem-solving (Ge et al., 2016). Moreover, learners must engage in multiple and interconnected decisions as they select evidence and determine causal chains during various stages of problem-solving (Shin & Jeong, 2021). The decision-making process is also closely linked with failure and the iterative choices needed to overcome errors in the problem-solving cycles (Schank et al., 1999; Sinha & Kapur, 2021). As such, decision-making is key for learners’ agency as they engage in self-directed learning and take ownership of ill-structured cases.

Despite its importance, the field of learning design only minimally addresses theories and models specifically associated with decision-making. The decision-making processes required for inquiry-based learning necessitates a more in-depth analysis because it is foundational to problem-solving as individuals weigh evidence, make strategic choices amidst an array of variables, and causal reasoning. In addition, an advanced understanding of this skill set would allow educators to develop systems that leverage specific decision-making strategies within design. Based on this gap, we survey broad decision-making paradigms (normative, descriptive, and prescriptive), along with case-based decision-making theory (Gilboa & Schmeidler, 1995; Kolodner, 1991). For each category, we then proffer an example that instantiates the theory. Finally, the article concludes with implications for practice.

Literature Review

Inquiry-based learning is an instructional strategy that affords learners with agency as they solve ill-structured problems. Although variations exist (problem-based learning, project-based learning, case-based instruction), the strategy often situates a contextual case to the learners that is representative of the domain (Lazonder & Harmsen, 2016; Loyens & Rikers, 2011). When compared with teacher-centric approaches where the instructor acts as the ‘sage on the stage’ (Reigeluth & Carr-Chellman, 2009), students in inquiry-based learning engage in a variety of learning actions in the problem representation and solution generation stage. The former necessitates learners define the problem, identify variables, and determine the underlying causal mechanisms of the issue (Delahunty et al., 2020; Ertmer & Koehler, 2018). Solution generation requires learners propose a way to resolve the issue, along with supporting evidence (Ge et al., 2016). This latter stage also includes how learners test out a solution and iterate based on the degree to which their approach meets its goals. As learners engage in these tasks, they must remedy knowledge gaps and work with their peers to reconcile different perspectives. Beyond just retention of facts, learners also engage in information seeking (Belland et al., 2020), question generation (Olney et al., 2012), causal reasoning (Giabbanelli & Tawfik, 2020; Shin & Jeong, 2021), argumentation (Ju & Choi, 2017; Noroozi & Hatami, 2019), and other higher-order thinking skills.

Another important aspect of inquiry-based learning also includes decision-making, which describes the choices learners select as they understand the problem and move towards its resolution. To that end, various theories and models that explicate the nuances of problem-solving have implicitly referenced decision-making. When describing the solution generation stage, Jonassen (1997) asserts that learners’ “resulting mental model of the problem will support the learner's decision and justify the chosen solution” (p. 81). Ge et al. (2016) proposed a conceptual model of self-regulated learning in ill-structured problem-solving in which “students not only must make informed decisions and select the most viable against alternative solutions, but also must support their decisions with defensible and cogent arguments” (p. 4). In terms of encountered failure during problem-solving, Kapur (2008) explains how students must “decide on the criteria for decision making or general parameters for solutions” (p. 391) during criteria development. Indeed, these foundation theories and models of problem-solving highlight the importance of decision-making in various aspects of inquiry-based learning.

Despite its importance, very little understanding is known within the learning design field about the specific decision-making processes inherent within problem-solving. Instead, there is a large body of literature dedicated to strategic approaches to self-directed learning (Xie et al., 2019), collaboration (Radkowitsch et al., 2020), and others. However, specific attention is needed towards decision-making to understand how learners seek out information, weigh evidence, and make choices as they engage in problem-solving. A review of theories argues for three distinct overarching theoretical paradigms of decision-making (Schwartz & Bergus, 2008): normative, descriptive, and prescriptive. There is also a related body of literature around case-based decision-making theory (Gilboa & Schmeidler, 1995), which describes how prior experiences are used to inform choices for new problems. Below we define the theory and related literature, along with a design example that instantiates the decision-making approach.

Outline of Decision-Making Theories and Constructs

Normative Decision-Making

Normative decision-making theoretical foundations.

Normative decision-making describes how learners make choices based on the following: (a) perceived subjective utility and (b) probability (Gati & Kulcsár, 2021). The former focuses on the values of each outcome, especially in terms of how the individual assesses expected benefits and costs associated with one’s goals and preferences. Alternatively, probability describes the degree to which individuals perceive that a selected action will lead to a specific outcome. Hence, a key assumption - and potential criticism - of normative decision-making is that individuals are logically consistent as they make choices under the constraints of rationality, which has been called into question.

Another important element of normative decision-making includes ‘compensatory models’; that is, how the benefits of an alternative outweigh the disadvantages. The most common compensatory model described in the literature is multi-attribute utility theory (MAUT), which is used to account for decision-making amidst multiple criteria (Jansen, 2011). MAUT thus aligns well with ill-structured problem-solving because it assumes that choices are made amongst a variety of competing alternatives. In a conservation example, one might select a green energy alternative to reduce carbon emissions, but it may be disruptive to the existing energy sources (e.g., fossil fuels) and raise costs in the short term. In the context of medicine, a surgery might ultimately resolve an issue, but it poses a risk for post-procedure infections and other complications. As individuals consider each alternative, MAUT is a way of “measuring the decision-maker’s values separately for a set of influential attributes and by weighting these by the relative importance of these attributes as perceived by the decision-maker” (Jansen, 2011, p. 101). MAUT component of normative decision-making specifically argues individuals progress in the following five steps (Von Winterfeldt & Edwards, 1993): 

• Individuals explicate the various alternatives and salient attributes associated with each choice.
• Each alternative is evaluated separately based on each attribute in terms of the following: complete (all essential aspects are addressed), operational (attributes can be meaningfully used), decomposable (deconstructing aspects of evaluation as to simplify evaluation process), non-redundant (remove duplicates of aspects), and minimal (keep a number of attributes focused and central to the problem).
• Individuals assign relative weights to each attribute
• Individuals sum the aggregate weight to evaluate each alternative.
• Individuals make a final choice.

Rather than pursue a less than optimal selection, MAUT argues that “they [individuals] strive to choose the most beneficial alternative and obtain all information relevant to the decision, and they are capable of considering all possible outcomes of the choice, estimating the value of each alternative and aggregating these values into a composite variable” (Gati et al., 2019, p. 123). Another characteristic is how individuals select the factors and assess the degree to which they can be compensated. Some individuals (e.g., expert, novice) may weigh a specific factor differently, even if the other aspects align with their desired outcomes. Given that individuals are not always rational and consistent in decision-making, some argue that the normative decision-making model is not truly representative of how individuals actually engage in everyday problem-solving (Gati et al., 2019; Jansen, 2011; Schwartz & Bergus, 2008). 

Normative decision-making theoretical application

Normative decision-making approaches applied to learning design make choices and probabilities salient to the learner, such as in the case of learner dashboards (Valle et al., 2021) or heuristics. Arguably, the most common application of decision-making in learning technologies for inquiry-based learning includes simulations, which situate individuals within an authentic context and posit a series of choices, and allow them to model choices (Liu et al., 2021). Systems that especially exhibit normative decision-making often consist of the following: (a) encourages learners to consider what is currently known about the phenomena vs. what knowledge the decision-makers lack, (b) makes probability associated with a choice clear, and (c) observes the outcomes of the decision.

One example of normative decision-making applied to design includes The Wildlife Module/Wildfire Explorer project developed by Concord Consortium. In this environment, learners are tasked with lowering wildfire risk in terms of fires and other natural hazards (see Figure 1). The decision-making is especially focused on choices around terrain and weather conditions, which add to or limit the amount of risk that is posed to each town. As learners make decisions, the interface allows individuals to manipulate variables and thus observe how certain choices will result in higher benefits relative to others. For instance, reducing the amount of brush in the area will better prevent wildfire when compared with cutting fire lines. In another instance, they explore how dry terrain and 30 mile per hour (MPH) winds would increase the potential wildfire risk of an area. The learning environment thus instantiates aspects of normative decision-making as learners select the parameters and discern its effects on the wildfire within the region.

Wildlife Module/Wildfire Explorer as Applying Normative Decision-Making

Descriptive Decision-Making

Descriptive decision-making theoretical foundations.

Whereas the normative decision-making approaches assume individuals make rational decisions that maximize choices, descriptive decision-making illustrates the gap between optimal decision-making and how people actually make choices (Gati et al., 2019). Although it is sometimes criticized for the lack of clarity, there are some elements of descriptive decision-making that have emerged. One key component includes satisficing, which posits that individuals attempt to make decisions based on how choices are maximized and meet specific goals. As outlined in the seminal work by Simon (1972), individuals aspire to engage in complex rational selections; however, humans have limited cognitive resources available to process the information available during decision-making. Because choices for ill-structured problems often have competing alternatives, individuals settle for decisions that meet some kind of determined threshold for acceptance in light of a given set of defined criteria. The theory further argues individuals will likely choose the first option that satisfices the desire; so while the final selection may be satisficing, it may not necessarily be the best and most rational decision (Gati et al., 2019). This is especially true in ill-structured problems that include multiple perspectives and constraints that make an ideal solution difficult. Rather, individuals instead strive for a viable choice that can be justified in light of multiple criteria and constraints.

Descriptive decision-making theoretical application

One example includes the EstemEquity project (Gish-Lieberman et al., 2021), which is a learning environment designed to address attrition rates for women of color in STEM through mentorship strategies aimed at building self-efficacy. Because the dynamics of mentorship can be difficult, the system relies heavily on decision-making and reflection upon choice outcomes (see Figure 2). The first steps of a scenario outline a common mentor/mentee challenge, such as a mentee frustrated because she feels as though the mentor is not listening to her underlying problem as she navigates higher education in pursuit of her STEM career. The learning environment then poses two choices that would resolve the issue. Although no single solution will fully remedy the ill-structured mentorship challenge, they must make value judgments about the criteria for success and the degree to which their decision meets the requirements. Based on the goals, the learning environment provides feedback as to how the choice satisfices given their determined threshold of optimal mentor and mentee relationships.

EstemEquity as Applying Descriptive Decision-Making

Prescriptive Decision-Making

Prescriptive decision-making theoretical foundations.

The aforementioned approaches highlight how individuals engage in sense-making as they make a selection among latent and salient variables. To better support ideal decision-making, the prescriptive approach is concerned with providing overt aids to make the best decisions (Divekar et al., 2012). Moreover, prescriptive decision-making “bridges the gap between descriptive observations of the way people make choices and normative guidelines for how they should make choices” (Keller, 1989, p. 260). Prescriptive decision-making thus provides explicit guidelines for making better decisions while taking into consideration human limitations. For example, physicians may use a heuristic that outlines a specific medication based on symptoms and patient characteristics (e.g., height, weight, age). Similarly, a mental health counselor may select a certain intervention approach when a client presents certain behavioral characteristics. In doing so, prescriptive decision-making outlines a series of “if-then” scenarios and details the ideal choice; that is, the pragmatic benefit of the decision to be made given a set of certain circumstances (Gati et al., 2019).

There are multiple challenges and benefits to the prescriptive approach to decision-making. In terms of the former, some question the degree to which a single set of heuristics can be applied across multiple ill-structured problems with varying degrees of nuance. That said, the prescriptive approach has gained traction in the ‘big data’ era, which compiles a considerable amount of information to make it actionable for the individual. An emerging subset of the field includes prescriptive analytics, especially in the business domain (Lepenioti et al., 2020). Beyond just presenting information, prescriptive analytics distinguishes itself because it provides the optimal solution based on input and data-mining strategies from various sources (Poornima & Pushpalatha, 2020). As theorists and practitioners look to align analytics with prescriptive decision-making, Frazzetto et al., (2019) argues: 

If the past has been understood (descriptive analytics; ‘DA’), and predictions about the future are available (predictive analytics; ‘PDA’), then it is possible to actively suggest (prescribe) a best option for adapting and shaping the plans according to the predicted future (p. 5).

Prescriptive decision-making theoretical application

Prescriptive decision-making approaches arguably are most used in adaptive tutoring systems, which outline a series of “if-then” steps based on learners’ interactions. ElectronixTutor is an adaptive system that helps learners understand electrical engineering principles within a higher educational context (see Figure 3). Rather than allowing the learner to navigate as desired or make ad-hoc selections, the recommender system leverages user input from completed lessons to prescribe the optimal lesson choice that best furthers their electrical engineering knowledge. For example, after successful completion on the “Series and Parallel Circuit” (the “if”), the system prescribes that the learner advance to the next “Amplifier” lessons (the “then”) because the system has determined that as the next stage of the learning trajectory. When a learner inputs the correct decision, they are prompted with the optimal selection the system deems as best advances their learning. Alternatively, a wrong selection constrains the choices for the learner and reduces the complexity of the process to a few select decisions. In doing so, the adaptive system implements artificial intelligence to prescribe the optimal path the learner should take based on the previous input from the learner (Hampton & Graesser, 2019).

Autotutor as Applying Prescriptive Decision-Making

Case-Based Decision-Making Theory

Case-based decision-making theoretical foundations.

The literature suggests case-based decision-making theory (CBDMT) is another problem-solving approach individuals employ within domain practice (Gilboa & Schmeidler, 1995). The premise behind CBDMT is that individuals recall previous experiences which are similar to the extant issue and select the solution that yielded a successful resolution (Huang & Pape, 2020; Pape & Kurtz, 2013). These cases are often referred to as ‘repeated choice problems’ whereby individuals see available actions as similar between the new problem and prior experiences (Ossadnik et al., 2013). According to the theory, memory is a set of cases that consists of the following constructs: problem, a potential act chosen in the problem, and ensuing consequence. Specifically, “the memory contains the information required by the decision-maker to evaluate an act, which is specific to the problem” (Ossadnik et al., 2013, p. 213). A key element in a case-based approach to decision-making includes the problem features, the assigned weights of said features, and observed consequences as a reference point for the new problem (Bleichrodt et al., 2017).

The CBDMT approach is similar to the normative approach to decision-making in that it describes how learners make a summative approach to decision-making; however, it differs in that it explicates how one leverages prior experience to calculate these values. Moreover, the value of a case for decision-making is evaluated through a comparison of related acts of other known issues when the new problem is assessed by the individual. Specifically, Gilboa and Schmeidler (1995) propose: “Each act is evaluated by the sum of the utility levels that resulted from using this act in past cases, each weighted by the similarity of that past case to the problem at hand” (p. 605). In this instance, utility refers to the benefits of the decision being made and the forecasting of outcomes (Grosskopf et al., 2015; Lovallo et al., 2012). The individual compares the new case to a previous case and then selects the decision with the highest utility outcome. As one gains expertise, CBDMT proffers one can “combine variations in memory with variations in sets of choice alternatives, leading to generalized versions” (Bleichrodt et al., 2017, p. 127) 

Case-based decision-making theoretical application

Because novices lack prior experiences, one might argue it may be difficult to apply CBDMT in learning design. However, the most often applied approach is by leveraging narratives as a form of vicarious experience (Jonassen, 2011a). In one example by Rong et al. (2020), veterinary students are asked to solve ill-structured problems about how to treat animals that go through various procedures. As part of the main problem to solve, learners must take into consideration the animal’s medical history, height, weight, and a variety of other characteristics. To engender learners’ problem-solving, the case profiles multiple decision points, and later asks the learners to make their own choice and justify its selection. Decision-making is supported through expert cases, which serve as vicarious memory and encourage the learners to transfer the lessons learned towards the main problem to solve (Figure 4). In doing so, the exemplars serve as key decision-making aids as novices navigate the complexity of the ill-structured problem.

Video Exemplars as Applying Case-Based Decision-Making Theory

Discussion and Implications for Design

Theorists of education have often discussed ways to foster various elements of ill-structured problem-solving, including problem representation (Ge et al., 2016), information-seeking (Glazewski & Hmelo-Silver, 2018), question generation (Olney et al., 2012), and others. While this has undoubtedly advanced the field of learning design, we argue decision-making is an equally foundational aspect of problem-solving that requires further attention. Despite its importance, there is very little discourse as to the nuances of decision-making within learning design and how each perspective impacts the problem-solving process. A further explication of these approaches would allow educators and designers to better support learners as they engage in inquiry-based learning and similar instructional strategies that engender complex problemsolving. To address this gap, this article introduces and discusses the application of the following decision-making paradigms: normative, descriptive, prescriptive, and CBDMT.

The above theoretical paradigms have implications for how these theories align with other design approaches of learning systems. In many instances, scaffolds are designed to support specific aspects of problem solving. Some systems are designed to support the collaborative process that occurs during inquiry-based learning (Noroozi et al., 2017), while other scaffolds outline the argumentation process (Malogianni et al., 2021). Alternatively, learning environments may embed prior narratives to model how practitioners solve problems (Tawfik et al., 2020). While each of these theories supports a critical aspect of problem solving, there are opportunities to further refine these learning systems by more directly supporting the decision-making process. For example, one way to align these design strategies and normative decision-making theories would be to outline the different choices and probabilities of expected outcomes. A learning system might embed supports that outline alternative perspectives or reflection questions, but could also include scaffolds that explicate optimal solution paths as it applies a prescriptive decision-making approach. In doing so, designers can simultaneously support various aspects of ill-structured problem solving.

There are also implications as it relates to the expert-novice continuum, which is often cited as a critical component of problem-solving (Jonassen, 2011a; Kim & Hannafin, 2008). Indeed, a body of rich literature has described differences as experts and novices identify variables within ill-structured problems (Jacobson, 2001; Wolff et al., 2021) and define the problem-space within contexts (Ertmer & Koehler, 2018; Hmelo-Silver, 2013). Whereas many post-hoc artifacts have documented outcomes that describe how novices grow during inquiry-based learning (e.g., concept map, argumentation scores), less is known about in situ decision-making processes and germane design strategies novice learners engage in when they are given problem-solving cases. For example, it may be that novices might benefit more from a prescriptive decision-making design strategy given the inherent complexity and challenges of cognitive load presented within an inquiry-based learning module. Alternatively, one might argue simulation learning environments designed for normative decision-making would make the variables more explicit, and thus better aid learners in their choice selection when presented with a case. The simulation approach often employed for normative decision-making might also allow for iterative decision-making, which may be especially advantageous for novices that are newly exposed to the domain. A further understanding of these decision-making approaches allows educators and designers to better support learners and develop systems that emphasize this higher-order learning skillset.

As learners engage in information-seeking during problem-solving, it follows that a choice is made based on the synthetization of multiple different sources (Glazewski & Hmelo-Silver, 2018). Future explorations around information seeking and decision-making would yield important insights for problem solving in multiple respects. For instance, the normative decision-making approach argues individuals assign values to various attributes and use this assessment to make a selection. As learners engage in inquiry-based learning, designers can use understanding of normative approaches to determine how individuals search for information to satisfice an opinion, use this to assess the probability of an action, and the resulting choice. From a descriptive decision-making approach, learners weigh various information sources as they seek out an answer that satisfices. Finally, a case-based decision-making theory approach may find learners search for information and related weights for the following: problem (q ∈ Q), a potential act chosen in the problem (a ∈ A), and ensuing consequence (r ∈ R). Although the design of inquiry-based learning environments often overlooks the intersection of information-seeking approaches and decision-making, a better understanding of the role of theory would aid designers as they construct learning environments that support this aspect of problem solving.

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The ongoing COVID-19 pandemic has demanded large-scale collaboration within all organizations, including higher education, and taking teaching and learning seriously, in this moment, means leveraging partnerships to address the wicked (large, complex) problems cited by Bass (2020). These problems are not ours alone to solve; rather, we make the case for a “wicked consciousness,” an amalgam of perspectives, in educational development. Guided by intellectual humility, our success as educational developers ought to be measured by the quality of our collaborations as well as our ability to learn with others, form equitable partnerships, and lead others by our example.

transdisciplinary, interdisciplinary, collaboration, partnership, wicked consciousness

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Introduction

That teaching and learning had its own claim to scholarship was a novel idea 30 years ago ( Boyer, 1990 ). Many, including Randy Bass, subsequently called for instructors to apply a data-driven mindset to student learning ( Bass, 1999 ). Since then, educational development has matured into its own scholarly field ( McDonald & Stockley, 2008 ), and educational developers have become key players in the organizational development of higher education institutions ( Beach et al., 2016 ; Kelley et al., 2017 ). As eight educational developers working toward organizational change at a diverse group of institutions, we combined our experience and perspectives to answer the following question:

“What does it mean to take teaching and learning seriously in this moment, in the current ecosystem of higher education?”

In November 2019, this question inspired us to explore opportunities for leveraging the educational developer role to bridge boundaries and build an institutional culture focused on “learning from and with students” ( Bass et al., 2019 ). We concur with the assertion of the New Learning Compact framework ( Bass et al., 2019 ) that higher education is under-performing in whom it serves and how well it serves all learners and that for higher education to fulfill its promise, teaching and learning must be constantly re-centered at the core of institutional missions. With this in mind, we argue that taking teaching and learning seriously in this moment requires educational developers to pursue strong partnerships at multiple levels in and beyond our institutions to create meaningful change: to learn from and with one another.

Pandemic-Induced Connections

In the spring of 2020 when the global coronavirus (COVID-19) pandemic transformed society, including higher education, many centers for teaching and learning (CTLs) found themselves suddenly playing a more central role and working with a host of new collaborators as they helped facilitate the rapid emergency-induced shift to remote instruction ( Korsnack & Ortquist-Ahrens, 2021 ). The coronavirus pandemic emphasized the need for institutions to focus on learning as a collaborative effort involving all stakeholders, not simply the thing that happens in the classroom. While the New Learning Compact ( Bass et al., 2019 ) includes full-time and adjunct “faculty” in their definitions, we propose further expanding the concept of “instructor” to include anyone who helps students develop skills and dispositions for lifelong learning.

Acknowledging that institutions may continue to operate in siloed structures such as departments and units, educational developers must build bridges across these boundaries with the purpose of building a culture in which coaches, success/academic advisors, career development centers, and administrators all recognize student learning as central to their unit’s ability to support the institutional mission. Furthermore, they must do so in ways that provide equity of access and equity of experience in higher education ( Winkelmes, 2015 ). Thus, from a learning perspective, student success can be defined as “academic achievement, engagement in educationally purposeful activities, satisfaction, acquisition of desired knowledge, skills and competencies, persistence, attainment of educational objectives, and [successful] post-college performance” ( Kuh et al., 2006 , p. 7).

This learning-focused definition of student success provides a different answer to the perennial question of higher education’s “return on investment” in an era when content knowledge is just a touch screen away. An instructor’s ability to encourage and guide students in making sense of information, integrating it across contexts, and using it creatively will be of even greater importance in the post-pandemic higher education landscape due to increased online coursework—a modality that is known to have lower retention and completion rates ( Bawa, 2016 ). If “it takes a village to raise a child,” then we believe it takes a community to effectively nurture the passion for learning in our students through demonstrating and modeling this value both within and beyond the classroom. Various units across our institutions interface with students on a daily basis, working alongside them as they experience authentic problems. We acknowledge this complicates our work in supporting teaching and learning by adding the requirement of effective relationship-building across institutional units.

Supporting Skill Transfer

A trend that exemplifies the increasing complexity of our mission is the call to support instructors in aligning course outcomes with employerdriven needs assessment data (e.g., National Association of Colleges and Employers [NACE] survey data). Consistently, employers expect and require college graduates to demonstrate “adaptability, communication skills, creativity, critical thinking and reasoning, ethical decision-making, leadership, problem identification, problem-solving, and teamwork” ( Taylor & Haras, 2020 , p. 2). Education, especially a liberal arts education, does not often claim to train students for the workforce. But the needs and requests of employers do indeed align with the habits of mind that universities aim to develop in their students, as highlighted in the American Council on Education’s recent Beyond Classroom Borders: Linking Learning and Work Through Career-Relevant Instruction report ( Taylor & Haras, 2020 ).

Leveraging connections across the institution from an educational developer perspective will help students connect the dots between classroom learning and workplace skills. Units ranging from the registrar’s office to information technology to health services play key roles in establishing safe, functional, and inclusive teaching spaces and learning experiences, but their contributions toward institutional learning goals could go further. For example, a registrar’s office representative on a general education committee will better understand institutional learning goals and thus better explain to students why they must take certain classes. Similarly, an information technology help desk that connects services such as file conversions or video editing to digital learning goals and/or workplace skills will help students understand the broader usefulness of specific course assignments. In addition, partnerships between social work or nursing faculty with health services staff would allow faculty to link classroom learning with health practices students experience in their interactions with student health services. During the pandemic, almost every institutional unit has had to learn new online collaboration tools, increasing the opportunities for data capture and even learning analytics in non-academic units. The more tools, data, and people we engage, the more complex our work becomes. We thus challenge the educational developer to engage with this complexity, to focus on “connecting” our higher education ecosystem, and to become an organizational change agent ( Grupp, 2014 ).

Building a Culture of Collaboration

This call for collaboration and convergence in higher education, “to work together to rejuvenate an antiquated system for our accelerating times” ( Davidson, 2017 ), is not ours alone. To lead a culture of collaboration, we need to identify and develop latent relationships in our work. This means educational developers need to be mindful of the inclusion of all voices, including both instructors and students. It also requires that we find common ground across units with distinct but often complementary missions such as academic skills support services, learning management system (LMS) services, instructional technology, and more. Building on the idea of a CTL as a hub ( Wright et al., 2018 ), strengthening our connections to the institutional community requires continuous addition of new spokes while maintaining the essential preexisting spokes. Both the quantity of the spokes and their quality (how supportive they are) should be continually assessed in providing an effective structure for our work. The building of relationships and the continued maintenance of preexisting partnerships are essential to the role of educational developers.

Partnerships within educational development are myriad and fall into multiple categories. Some partnerships are more common, such as with educational technology, disabilities services, libraries, and diversity, equity, and inclusion (DEI) units at our institutions. Other partnerships seem logical but may remain unrealized, such as academic advising, student affairs, and other student services. Finally, many potential partnerships at our institutions involve expanding the definition of teaching and learning to embrace extracurricular and co-curricular elements of students’ education: athletic departments, student organizations, and external stakeholders, such as employers, who assist with internships. What all of these partnerships have in common is that they widen the lens through which we view learning: who is involved and where it happens. By defining learning more holistically, we have an opportunity to redefine teaching too.

Again, the mission to take teaching and learning seriously is more expansive than a CTL can achieve on its own. We must productively collaborate with others within and beyond our institutions to realize these goals (and more). As the New Learning Compact ( Bass et al., 2019 ) provides a method to assess and prioritize institutional needs, we extend this work by making the case that broadening a definition of learning and student success requires educational developers to expand the scope of their work, develop the mindset necessary to do that, and identify new metrics to assess the effectiveness. More specifically, we argue for the development of a “wicked consciousness” to recast collaborators in and beyond the institution as partners, as defined by the Students as Partners model, and to develop new ways to assess the efficacy and impact of this work.

The Need for Collaboration

Calls for collaboration in educational development are not new. Chism (2004) argued that this meant leveraging and engaging the assistance of other stakeholders on campus. However, others have pointed out that potential partnerships are often unrealized (e.g., see Behling & Linder, 2017 ). The value of collaboration has never been so clear as during higher education’s response to the COVID-19 pandemic, when failure to collaborate effectively resulted in barriers to student success. Indeed, “collaboration and exchange across difference spurs participants to rethink their assumptions” ( Bass et al., 2019 ). While the pandemic has likely resulted in some reactive or forced collaborations, a true test of what we have learned from these challenging times will be to proactively pursue a post-pandemic culture of collaboration within and outside of our institutions.

In pursuit of collaboration, Bass calls for us to broaden our scope and lead “all sectors of faculty as well as staff” to engage in educational outcomes ( Bass et al., 2019 ). We agree with Bass (2020) , who asserts that inter- and/or trans-disciplinarity “is not just about disciplines or academic expertise, but also about functional role, identity, and perspective” (p. 21). We concur with a definition of discipline that moves beyond the silos of academic disciplines to include our colleagues from across the institution: for example, seeing student affairs as a discipline ( Patterson, 2019 ) with its own holistic, student-centered worldview. As our field continues to grow, including even more perspectives can widen our lens on student experience, expanding our definition of student learning to include co-curricular and extracurricular learning: “Everyone—diverse faculty, staff, advisors, students— should be regarded as learners, inquirers, researchers, and agents of change” ( Bass, 2020 , p. 21).

Wicked Problems

We believe this culture of collaboration must extend beyond the walls of our own institutions. There is much to gain from learning with and from outside partners such as potential employers, community organizations, and government. Why take this approach? We need all of these perspectives to address what Bass calls “wicked problems,” an idea from design:

A wicked problem is a social or cultural problem that is difficult or impossible to solve for as many as four reasons: incomplete or contradictory knowledge, the number of people and opinions involved, the large economic burden, and the interconnected nature of these problems with other problems . ( Kolko, 2012 )

The problems we see within higher education, such as low graduation or retention rates, inequalities of access, and questions about the endurance of student learning, become wicked when we grow our perspective to consider not only the role of actors within the institution but also the broader systemic, historical, and cultural influences at play. We need to consider how institutional and community stakeholders contribute to or hinder these efforts.

What happens if we fail to take this broad view? We may fail to understand the true scope of the problem, and our solutions may be inadequate, if not wholly inappropriate. As one example, Bass (2020) pushes against siloed views of learning and student success. He highlights the inherent incompleteness of approaching either in isolation, pointing out that “if one understands the problem of student success as a tame problem … it is likely we will focus only [emphasis added] on strategies intended to have direct impact on student learning, persistence, and completion” ( Bass, 2020 , p. 13). Using educational equity as another example, Bass notes that more collaboration ensures equity is not relegated to any one office but integrated within faculty’s work ( Bass, 2020 ). Both examples demonstrate that solving tame problems can contribute to addressing wicked problems, but we must keep context in mind. In our increasingly corporate cultures of assessment and accountability, there is pressure to yield to a kind of short-termism, in which the need to generate returns (graduation and retention rates, greater diversity and selectivity of admissions) limits thinking to solutions that are tangible but perhaps short-sighted and therefore “tame.”

The scope of wicked problems asks for a perspective that places our strategic aims but also our expertise in a broader context. If, as Bass (2020) argues, “In a wicked problem frame, the optimization of educational practice is not the end game” (p. 28), then we need to reconsider how we define our expertise and how we ground our professional identities. If we place our identities in the context of addressing wicked problems through higher education, then we may need a new way to represent our work. Just as Barr and Tagg (1995) advanced academic development from a teaching model to a learning one, educational developers may find themselves moving even further from a transmission model to a constructivist or even emergent model ( Bass, 2020 ). Our effectiveness cannot lie solely in our knowledge of pedagogical best practices, just as instructors need more than their content knowledge. Whether it’s the model of coach ( Cruz & Rosemond, 2017 ) or the more general idea of “connector,” our work asks more of us than selling our vision to others. We need to promote transdisciplinary collaborations that are co-equal, more power-neutral, and, at least in part, exploratory.

Bridging Boundaries

In the pursuit of more transdisciplinary collaboration, it’s important to note that building bridges requires acknowledging the boundaries we cross in collaboration. Inequities and power dynamics exist within higher education: academic bullying occurs across roles (see Prevost & Hunt, 2018 ), faculty realignment (such as contingent faculty being reassigned to new academic professional tracks), and the continuing proliferation of an “adjunct underclass” ( Childress, 2019 ) all highlight the need to develop trust across differences of position and power. One way to approach this is through introspection and self-assessment. We may need to ask ourselves questions such as the following: Do our advisory boards (if we have them) have representatives from all ranks of instructors, including adjunct instructors, and do we seek new perspectives with the addition of student or faculty affairs professionals and others? How can we involve students as partners, as well as those outside of our institutions in our teaching and learning mission? Finally, how do we maintain our already tenuous identities (see Rudenga & Gravett, 2019 , 2020 ) in this expansive vision of educational development?

While we want to advocate for a teaching and learning perspective in the work of our collaborators, we may also need to integrate a student affairs lens, a faculty affairs lens, a campus life lens, an employer or community partner lens, and others into our scope of teaching and learning. We need to be willing to ask the question of what constitutes learning in this context and get an answer that we don’t anticipate, with a willingness to expand or adapt our definitions of learning. A culture of collaboration means that we are open to growth too. We build bridges not just to teach or spread our teaching and learning mission but also to allow ourselves to be changed by those with different views/perspectives within our institutions. The effort to build bridges asks us to assess honestly the extent of our contact with faculty/instructors, staff, and students: across career stages and across demographics.

These multiple perspectives, from both within and outside of our institutions, form the possibility of what Bass refers to as a convergence approach ( Bass, 2020 ). Wicked problems ask us to discover and consider a broad range of evidence and experiences. As Bass (2020) notes, “By understanding the problem of learning as a wicked problem . . . the co-evolution of the field’s problems and the tools it has to address them should radically expand our approaches toward improving education rather than narrow them” (p. 11). But how do we create a system that is likely to unearth what we don’t know, known unknowns (such as the perspectives of others) and unknown unknowns (questions we haven’t thought to ask, evidence we haven’t thought to consider)? We argue for a wicked consciousness, a persistent amalgamation of perspectives achieved only through a culture of collaboration. Kolko (2012) concurs: “Due to the system [ sic ] qualities of these large problems, knowledge of science, economics, statistics, technology, medicine, politics, and more are necessary for effective change. This demands interdisciplinary collaboration, and most importantly, perseverance.”

Framing all of this discussion, the COVID-19 pandemic and related social, global, and cultural challenges have been a stress test for the existing collaborative infrastructure of our institutions. In many cases, the crisis forced us to team up across units in response to immediate problems in need of solving. While it remains to be seen whether or not we go back to business as usual (functioning more independently and less collaboratively) post crisis, the pandemic has made real how pressing it is for everyone to see how all stakeholders support—or become a barrier to—a wider definition of student success. The immensity and interrelatedness of wicked problems call upon a more collaborative and inclusive response, guided by complementary (even competing) perspectives working in concert. Approaching a wicked consciousness requires a shift from collaboration to partnership: the orientation to learn from and with one another is what animates this effort.

From Collaboration to Partnership

Transdisciplinary collaboration asks for a willingness to recognize our collaborators, be they instructors, staff, students, or external stakeholders, in co-equal partnership. We may take this idea for granted in our relationships with colleagues while we may continue to see others through a lens of institutional hierarchy. The Students as Partners movement, embraced by numerous institutions across the United States and beyond, is an approach that invites students into collaborations with instructors, administrators, and educational developers. It is characterized by “a relationship in which all involved—students, academics, professional services staff, senior managers, students’ unions, and so on—are actively engaged in and stand to gain from the process of learning and working together” ( Healey et al., 2014 , p. 12). This perspective asks us to see students as equals, to honor their lived experience as learners, and we are challenged to “learn from and with” students ( Bass et al., 2019 ). We believe that this can serve as a model for revisiting collaborations throughout our networks, intentionally recasting them in the spirit of partnership.

Learning From and With Students

The idea of learning from students is embedded into educational development. While our most common collaborators are the instructors themselves, we often bring in the student perspective as a way to further discussions about teaching and learning (e.g., student feedback). In this sense, students are already indirect collaborators. Learning with students asks us to go further, asking what we can accomplish together. What differentiates this from collaboration is an evolving question (see Table 1 for comparison). The research on student partnerships is a growing body of scholarship focused on co-inquiry. This literature asks questions about power structures in higher education, and some believe it has the potential to disrupt traditional teaching and learning relationships ( Cook-Sather et al., 2014 ).

A Partnership Model for Educational Developers

As the rhetoric of disrupting teaching and learning through partnership can seem like heady stuff, the extent to which we can learn from and with students may be met with some skepticism, some of it reasonable and understandable. There is evidence that our students may not always be accurate judges of their learning ( Carpenter et al., 2020 ; Deslauriers et al., 2019 ), and we may hesitate to yield so much ownership to students. Limits of students’ perspectives may cause us to cast doubt on the value of seeking their input. Learning with students also requires a high degree of agency on the part of the students involved ( Weimer, 2002 ). They may have little experience with directing their educational experiences, and their shift into the position of partner may require us to solicit dialogue to empower a great sense of agency. We need to establish trustworthiness to complement our expertise ( Little & Green, 2021 ).

Skepticism notwithstanding, we also need to be critical of the state of existing partnerships, with a call to do more than adopt the appearance of partnership. A review of Students as Partners literature ( Mercer-Mapstone et al., 2017 ) found that much of the scholarship was instructor led and instructor authored; students were listed as the lead author on very few of the reviewed articles. In response, Morris (2019) calls for a shift from “students as co-enquirers” to “students as joint authors.” In turn, teachers and students “become jointly responsible for a process in which all grow” ( Freire, 1993 , pp. 79–80). We can learn from these areas of growth when cultivating our partnerships. This idea has roots in the literature of collaborative learning (e.g., Peters & Armstrong, 1998 ) but also ties back to the co-equal, constructivist approach to educational development that builds on Barr and Tagg (1995) . In addition to informing our approaches, partners can guide them. We need to be willing to yield a hand from the wheel—to share the responsibility of steering change—giving partners a chance to codetermine the direction of our co-inquiry.

Learning From and With Others

If we adopt this stance of learning from and learning with in other collaborations, one could imagine educational development partnerships across the university. Student affairs as partners, faculty affairs as partners, and so on, as well as a renewed commitment to instructors as partners (rather than clients). Whether or not these become formalized initiatives, this mindset can inform our work with colleagues: empowering the expertise of others in collaboration. Just as a Students as Partners initiative benefits from the true inclusion of student voices, our work will benefit from the inclusion of voices from those outside of our ranks who could provide new insights, connections, and the critical mass to effect change as partners.

The co-inquiry of partnership asks us to acknowledge the limits to our perspectives. It requires cultivating trust across power and asks us “to go beyond listening to the student voice” ( Healey et al., 2016 ). Matthews (2017) makes the case that Students as Partners is an open-ended strategy that asks us to let go of the need for specific outcomes. Intellectual humility ( Whitcomb et al., 2017 ) is required to invite differences into our partnerships, into our scholarship, and into our definitions of teaching and learning. We have made strides in this direction through the growth of discipline-based educational research (DBER) and disciplinary approaches in the scholarship of teaching and learning (SoTL). In seeking partnerships, we ought not to smooth over differences but to embrace them. In bringing different perspectives to light, partners have the chance to understand one another’s intentions ( Abbot & Cook-Sather, 2020 ).

Partnership in Action

Maintaining effective relationships with staff, instructors, and students represents an opportunity to learn from one another and to collaborate within our institutions. In addition, educational developers are well positioned to encourage and promote constructive discussions about teaching and learning beyond the academy’s boundaries. The understanding of teaching and learning theory and data could be better leveraged to help students transfer skills and knowledge developed in college classrooms to life beyond institutional walls. This is especially critical in the current ecosystem of higher education. Even before the recent COVID-19 pandemic, state appropriations for higher education were decreasing following a peak in 2001 ( Tandberg & Laderman, 2018 ), and although most analyses confirmed the value of a college degree, some economists and policy-makers have questioned higher education’s “return on investment” ( Abel & Deitz, 2014 ; Carnevale et al., 2019 ).

In an era when higher education has been called upon to identify and implement “high impact practices” ( Kuh, 2008 ) such as undergraduate research, civic and global engagement, and experiential learning, instructors have sought support and resources from CTLs. As a result, educational developers have been actively involved in creating opportunities for collaboration, connection, and problem-solving ( Beach et al., 2016 ; Grupp & Little, 2019 ). The role of educational developers as leading from the middle to move campus-wide initiatives forward can be further explored in areas such as career discernment and development. For example, CTLs may offer workshops that connect teaching approaches to appreciative advising, growth mindset, and theories of psycho-social development. Through collaboration and problem-solving, educational developers may better serve the institution by working with campus colleagues to integrate external stakeholders into their work, such as prospective employers, internship site coordinators, and local community leaders.

Prospective Employers as Partners

Educational developers traditionally focus on improving teaching and learning based on their knowledge of SoTL literature and their institutional knowledge of practices that are effective. However, in order to facilitate connections with potential employers and local businesses, it might be beneficial for educational developers to facilitate translations between SoTL and occupational skills language. For example, educational developers could introduce instructors to resources on occupational skills (e.g., Occupational Information Network, https://www.onetonline.org , sponsored by the U.S. Department of Labor/Employment and Training Administration). If instructors and students can translate between course and program learning goals and the occupational skills of interest to employers, students will be more effective at communicating their relevant skills to potential employers. Communication between these groups can improve cross-unit goal alignment by improving explanations from multiple stakeholders about how courses and programs relate to occupational skills. These connections are valuable for institutions that serve adult learners who are often already in the workforce, and they are important for students in institutions with a liberal arts focus as well ( Gallagher, 2018 , 2020 ).

Internship Site Coordinators as Partners

Educational developers and vibrant CTL communities also maximize the impact of out-of-classroom experiences by fostering an atmosphere in which student experiences are connected back to course and program learning goals via a student-focused course design. For example, a neurobiology class that draws on out-of-classroom student experiences in day cares, hospitals, nursing homes, and cafés to enrich classroom discussions fulfills the promise of learning from and with students while also revealing to students the connections between classroom learning and areas of possible professional practice. Repetition of this experience over multiple years reinforces this reflective metacognitive practice as a way to fully develop a growth mindset approach to workplace experiences.

Community Leaders as Partners

One learning framework that can be effectively used by educational developers and instructors in partnership with students and career development, study abroad, or community relations offices is Kolb’s 1984 model of Experiential Learning ( Kolb, 1984 ; Kolb & Kolb, 2005 ). This model’s emphasis on interactive learning and information processing ( McCarthy, 2016 ) complements more current research demonstrating the importance of metacognition ( Ohtani & Hisasaka, 2018 ). Recent educational practice has acknowledged the benefits of internships, study abroad, and service learning, but these experiences are not always fully connected to course, program, or institution-level learning goals. For example, while study abroad inevitably involves active learning experiences, a recent study ( Strange & Gibson, 2017 ) reports that students found “that the most influential parts of their programs were the field trips, self-reflection, community interaction, and writing aspects” and concludes that these components relate closely to Experiential Learning Theory components. While further research is needed to determine how best to optimize the learning opportunities inherent in experiential settings (study abroad, internships, field work), there is a role for educational developers in these conversations. Helping instructors and partners (study abroad offices, community partners) find ways to integrate student learning gains with the rest of their education is key in supporting students’ postgraduate pursuits.

An explicit focus on student learning with respect to personal and professional goals can also be beneficial for students who participate in shadowing or internship experiences. These types of career preparation or volunteer experiences rarely link planned activities to desired learning outcomes. In the best-case scenario, students can create their own experiential learning “syllabus” that must be approved by both the instructor advisor and the experience supervisor. Journaling ensures that students engage in reflective observation and abstract conceptualization as their experience progresses. This linkage between classroom practice and, most commonly, career-related outof-classroom experiences joins the student, instructor advisor, staff member, and internship or workplace supervisor in a shared endeavor to maximize the student’s learning from the experience. This network of relationships may work most effectively when guided or curated by an educational developer.

Re-Assessing Our Work

We have said that it takes a community to effectively nurture the passion for learning in our students. Moving this effort forward (by modeling the value of learning within and beyond the classroom) will require more collaborators, more voices, and more partners, so that it becomes a community effort rather than a fragmented response (see Table 1 ). We also need tools to get started. Bass et al.’s (2019) New Learning Compact offers strategies for effecting change across all levels of our institutions. Though its scope and purpose are much larger, we are excited by its potential to be used as a “how-to” or aspirational inventory for educational developers working to be more “active, imaginative, and capable ‘principal investigators’ of the asymmetry between the classroom and the world” ( Bass, 2020 , p. 19). Finally, in addition to partners and tools, we need a way of assessing this community effort.

Investigating Primary AND Secondary Impacts

CTLs build programming and engagement opportunities designed to have positive effects on the instructors and broader academic community (i.e., students, staff, and administrators). We call the outcomes of these programs, services, and events the primary impacts of the center. Many instruments exist to assess this work, notably Hines’s (2017) Field-Tested Model, the Defining What Matters framework ( Collins-Brown, Brown et al., 2018 ), and the Center for Teaching and Learning Matrix ( Collins-Brown, Haras et al., 2018 ). However, the secondary effects (or emergent outcomes) of educational development can take many forms, including those that come about as a result of spontaneous or serendipitous interactions—those that are not planned and therefore not assessed, or not assessable, by traditional measures. These unplanned interactions can lead to deeper connections as they happen organically, rely on common interests or needs, and are maintained because they have mutual impact. But how do we measure them?

In the absence of specific data that can tell us exactly how effective we are in our role as “connector,” we must find other means to assess and improve in this aspect of our role. The New Learning Compact offers strategies and principles to guide this work. And we see patterns in the New Learning Compact framework that echo Bass’s earlier call to action in “The Scholarship of Teaching: What’s the Problem?” Specifically, he calls for teachers to forgo a mindset wherein problems are seen as a need for remediation (i.e., an outcomes-oriented approach) and instead approach them as investigators (i.e., a processoriented approach): “Changing the status of the problem in teaching from terminal remediation to ongoing investigation is precisely what the movement for a scholarship of teaching is all about” ( Bass, 1999 ).

In efforts to reassess our work, we may need to advocate even more for the value of qualitative data in our work. While we can solicit instructor feedback and undertake needs assessments, we might also be more careful observers of casual, or backstage, conversations that can have surprising power ( Roxå & Mårtensson, 2009 ). These interactions build our campus network; expand our understanding of individual instructors and their needs and interests; and give us more specialized knowledge of groups, disciplines, resources, and the history of the institution and its people. These ripples , often unseen, have the power to shape a culture of teaching and learning at our institutions. Participants in a course design academy share their experience with colleagues, a workshop on retrieval leads to discussions about how to help students study more effectively, and CTLs take a lead role in shaping the implementation of peer review of teaching on their campuses. We might understand the relationship of primary and secondary impacts as a cycle: we seek to impact, we are impacted by others, and we hope to have greater impact.

While our effectiveness in the role of educational developer is typically assessed through measurable impacts (attendance, diversity of attendees, instructor learning, and instructor change of attitudes), our secondary impacts are those that come about as a result of our role as “connectors.” We believe these impacts are correlated with the quantity and quality of our network, relationships, and connections across the institution and with external stakeholders; however, what comes out of these connections is where the real value of the educational developer is revealed. Measuring such impacts is a worthy goal. The challenge is not unlike the challenges of measuring student learning—we can assess whether a student has achieved specific learning outcomes, but it’s not always possible to claim that the teaching was the reason for this success. Indeed, ascribing a cause-and-effect relationship is challenging when it comes to the secondary impacts of educational development. This shift is a prerequisite to a much larger shift: the shift from evaluating our work within the context of our institution to evaluating our work within the context of addressing larger, more wicked problems.

Bass (2020) challenges us to see learning as a wicked problem: “the long-term problem of reimagining and enacting education so that it plays a meaningful role in creating a more just society and fostering a sustainable human future” (p. 10). Throughout this article, we have argued for a culture of collaboration, more specifically a culture of partnership. We have declared that our impact will depend on our ability to listen effectively to others and to bring others into the core of a university’s mission: teaching and learning. We believe that reconsidering our work in terms of partnership asks us to reassess our work in terms of our relationships. Returning to the Hub Model of CTLs ( Wright et al., 2018 ), we have claimed the importance of both developing new spokes and maintaining the thickness of preexisting spokes as a way to strengthen our connection to the institutional community. We aim to assess our work through both the quantity and quality of relationships. Yet while we may be able to demonstrate relationships built, how do we know if we’re making progress on our wicked problems?

Pandemic Reflections

In late fall 2019, we first imagined “taking teaching and learning seriously, in this moment” in terms of the growth and maintenance of the partnerships, both formal and informal, we were developing with campus instructors, staff, and students. As we moved forward from spring 2020 into the continuing uncertainties of subsequent semesters, the concept of collaboration narrowed to essential questions of assisting instructors and other campus stakeholders to deliver their services with greater flexibility. Goals became constrained to that which was needed to preserve the institution’s core mission—education. Anecdotally, our response to the pandemic was driven by collaborative and creative problem-solving rather than by ready-made, evidence-based solutions, as this moment was unlike any other experience we could draw on. In a larger context, a tension developed between immediate problem-solving and initiating collaborations, bridge-building, and developing partnerships in sustainable ways. There was no time to consider future sustainability; the conditions required immediate actions. As Bass reflected on the porous barriers between higher education and the outside world, COVID-19, an accompanying economic crisis, and protests against systemic racism have required that educational developers and CTLs deal with these immediate and real challenges of teaching and learning in an uncertain, unprecedented environment.

As we negotiated the summer of 2020 with its demands and challenges of preparing for an ambiguous fall semester, we experienced a tension between negotiating the immediate needs and taking teaching and learning, writ large, seriously. We focused on internet access, how to navigate social distancing in classrooms, and how to hold up our educational ideals in a time when so much felt compromised. Simultaneously, the recognition of disproportionate impacts of the pandemic on persons of color in the United States combined with continuing police killings of black men and women spurred a summer of protests that placed a persistent reality for many into the public consciousness. Many of us struggled with not only our workload but also our sense of purpose. Yet “solutions to wicked problems can be only good or bad, not true or false. There is no idealized end state to arrive at, and so approaches to wicked problems should be tractable ways to improve a situation rather than solve it” ( Kolko, 2012 ). Thus, the shift toward wicked consciousness asks only to begin: to inform ourselves, to ask better questions, all in the service of beginning again.

Looking Forward With Wicked Consciousness

Broadening the current scope of educational development and moving forward into the future are not incompatible. The idea of taking teaching and learning seriously needs to be serious minded as well as open minded. Bass (2020) makes this clear: “In a wicked problem stance, some learning design research (pedagogical and curricular) should be carried out solely for the purpose of discovering the ‘adjacent possible’ [citing Stuart Kauffman]” (p. 23). As we embrace the idea that the pivot from the spring of 2020 has become a protracted change in the face of unprecedented circumstances, our answer to Bass’s question of “What’s the problem now?” is evolving. Thus, our response to the idea of taking teaching and learning seriously can evolve while we don’t lose sight of existing ideas, research, and potential solutions. Efforts to study the efficacy of online teaching and learning predate our current circumstances, and ideas about engagement online can find a place alongside our present, ongoing concerns about mental health, burnout, and managing the changes we’ve asked of instructors and students alike (and educational developers too).

Finally, taking teaching and learning seriously in this moment may mean that our educational development takes on more of a moral dimension. It’s a choice to value an approach that focuses our efforts on wicked problems. Evidence can inform our decisions, but it can’t make our decisions for us. The wicked problems of climate change, racial injustice, and all forms of cultural instability have become even more pressing in the wake of the COVID-19 pandemic and ask us to help students integrate their learning into a wider context. We know from decades of research on learning transfer that learners may not make active connections between what they learn in our institutions and the outside world unless they are prompted to do so in multiple contexts ( Barnett & Ceci, 2002 ). In our roles, we can create opportunities for instructors to consider how to connect their teaching (and student learning) to solve the big problems in society, and we must.

The question of what it means to take teaching and learning seriously in this moment is indeed a wicked one. Yet we have probed questions that can guide our response. What does it mean to take ourselves and our partners seriously as teachers? As learners? Is it to expand the idea of discipline to include partners within and outside of our institutions, to expand the label of instructor to include all who are responsible for the teaching mission of our institutions, and to embrace the label of learner as something we have in common with our students? When we consider the possibility of wicked consciousness, we believe it can come only from deconstructing the barriers and silos we’ve put between departments, between teachers and learners, between external and academic stakeholders, and between academia and the outside world. Bass would argue that these boundaries are porous, if illusory, and in this moment, it is critical that educational developers create meaningful and lasting partnerships that not only solve problems “but also restlessly and authentically open up the questions of learning and higher education as if our human future depended on it” ( Bass, 2020 , p. 28). That, to us, would be the very definition of taking teaching and learning seriously in this moment.

Biographies

Adam H. Smith , PhD, is an Assistant Research Professor at the Schreyer Institute for Teaching Excellence at the Pennsylvania State University. Adam received his PhD in Fine Arts from Texas Tech University, where he also began work in faculty development. He currently co-facilitates POD Scholarly Reads through the Scholarship Committee and serves as incoming Co-Chair of the Mindfulness and Contemplative Pedagogy special interest group.

Laurie L. Grupp , PhD, is the Dean of the School of Education and Human Development at Fairfield University. Her research interests include bilingual special education, change leadership, educational development, and reflective practice. In her educational development and leadership roles, she has engaged in efforts to promote diversity, equity, and inclusion while supporting faculty in all aspects of their professional growth.

Lindsay Doukopoulos , PhD, is Associate Director of the Biggio Center at Auburn University. A creative writer by training, her research now focuses on the use of instructional technologies to motivate and assess learning, scaling up faculty development for active learning buildings, gamification, and the use of storytelling to build equity and empower innovative teaching. She is former chair and ongoing member of the Digital Resources and Innovations committee for the POD Network.

John C. Foo , PhD, is the Assistant Director of Faculty Programs and Services for Science and Engineering at Columbia University’s Center for Teaching and Learning. He develops and facilitates programming and services for faculty instructors to enhance STEM education and make it more equitable for and accessible to Columbia’s diverse student population. John received his PhD in Biomedical Engineering from Cornell University.

Barbara J. Rodriguez , PhD, is a Regional Director of Academic Programs for the Association of College and University Educators (ACUE) and adjunct English faculty at Central Carolina Community College. She serves as a mentor for the Higher Learning Commission’s Assessment Academy, a coach for the Pathways Collaborative, and a peer reviewer for the Community College Journal of Research and Practice .

Janel Seeley , PhD, is director of the Ellbogen Center for Teaching and Learning at the University of Wyoming. She received her doctorate in Educational Psychology from the University of Tennessee. Her interests include collaborative communication and the scholarship of teaching and learning (SoTL). She is past chair of the SoTL special interest group committee for the POD Network and a co-leader for the International Society for the Scholarship of Teaching and Learning’s (ISSOTL) International Collaborative Writing Group public SoTL initiative.

Linda M. Boland , PhD, is the Associate Provost for Faculty, Director of the Teaching and Scholarship Hub, and Professor of Biology at the University of Richmond in Virginia. Her areas of interest in faculty development include early and mid-career faculty development, inclusive teaching, sustaining scholarship, leadership development, and building effective partnerships to promote institutional effectiveness.

Laurel L. Hester , PhD, was Assistant Provost and Associate Professor of Biology at Keuka College, where she managed a broad portfolio that included assessment, institutional effectiveness, accreditation, and compliance. Pandemic-induced career re-evaluation led her to become an Investigative Scientist for the National Science Foundation Office of Inspector General. She notes that the views expressed in this article do not necessarily represent the views of the National Science Foundation or the United States.

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Facilitating Complex Thinking

Problem-Solving

Somewhat less open-ended than creative thinking is  problem-solving , the analysis and solution of tasks or situations that are complex or ambiguous and that pose difficulties or obstacles of some kind (Mayer & Wittrock, 2006). Problem-solving is needed, for example, when a physician analyzes a chest X-ray: a photograph of the chest is far from clear and requires skill, experience, and resourcefulness to decide which foggy-looking blobs to ignore, and which to interpret as real physical structures (and therefore real medical concerns). Problem-solving is also needed when a grocery store manager has to decide how to improve the sales of a product: should she put it on sale at a lower price, or increase publicity for it, or both? Will these actions actually increase sales enough to pay for their costs?

PROBLEM-SOLVING IN THE CLASSROOM

Problem-solving happens in classrooms when teachers present tasks or challenges that are deliberately complex and for which finding a solution is not straightforward or obvious. The responses of students to such problems, as well as the strategies for assisting them, show the key features of problem-solving. Consider this example and students’ responses to it. We have numbered and named the paragraphs to make it easier to comment about them individually:

Scene #1: A problem to be solved

A teacher gave these instructions: “Can you connect all of the dots below using only  four  straight lines?” She drew the following display on the chalkboard:

The problem itself and the procedure for solving it seemed very clear: simply experiment with different arrangements of four lines. But two volunteers tried doing it at the board, but were unsuccessful. Several others worked at it at their seats, but also without success.

Scene #2: Coaxing students to re-frame the problem

When no one seemed to be getting it, the teacher asked, “Think about how you’ve set up the problem in your mind—about what you believe the problem is about. For instance, have you made any assumptions about how long the lines ought to be? Don’t stay stuck on one approach if it’s not working!”

Scene #3: Alicia abandons a fixed response

After the teacher said this, Alicia indeed continued to think about how she saw the problem. “The lines need to be no longer than the distance across the square,” she said to herself. So she tried several more solutions, but none of them worked either.

The teacher walked by Alicia’s desk and saw what Alicia was doing. She repeated her earlier comment: “Have you assumed anything about how long the lines ought to be?”

Alicia stared at the teacher blankly, but then smiled and said, “Hmm! You didn’t actually  say  that the lines could be no longer than the matrix! Why not make them longer?” So she experimented again using oversized lines and soon discovered a solution:

Scene #4: Willem’s and Rachel’s alternative strategies

Meanwhile, Willem worked on the problem. As it happened, Willem loved puzzles of all kinds and had ample experience with them. He had not, however, seen this particular problem. “It  must  be a trick,” he said to himself because he knew from experience that problems posed in this way often were not what they first appeared to be. He mused to himself: “Think outside the box, they always tell you. . .” And  that  was just the hint he needed: he drew lines outside the box by making them longer than the matrix and soon came up with this solution:

When Rachel went to work, she took one look at the problem and knew the answer immediately: she had seen this problem before, though she could not remember where. She had also seen other drawing-related puzzles and knew that their solution always depended on making the lines longer, shorter, or differently angled than first expected. After staring at the dots briefly, she drew a solution faster than Alicia or even Willem. Her solution looked exactly like Willem’s.

This story illustrates two common features of problem-solving: the effect of degree of structure or constraint on problem-solving, and the effect of mental obstacles to solving problems. The next sections discuss each of these features and then look at common techniques for solving problems.

The Effect of Constraints: Well-Structured Versus Ill-Structured Problems

Problems vary in how much information they provide for solving a problem, as well as in how many rules or procedures are needed for a solution. A  well-structured problem  provides much of the information needed and can in principle be solved using relatively few clearly understood rules. Classic examples are the word problems often taught in math lessons or classes: everything you need to know is contained within the stated problem and the solution procedures are relatively clear and precise. An  ill-structured problem  has the converse qualities: the information is not necessarily within the problem, solution procedures are potentially quite numerous, and multiple solutions are likely (Voss, 2006). Extreme examples are problems like “How can the world achieve lasting peace?” or “How can teachers ensure that students learn?”

By these definitions, the nine-dot problem is relatively well-structured—though not completely. Most of the information needed for a solution is provided in Scene #1: there are nine dots shown and instructions given to draw four lines. But not  all  necessary information was given: students needed to consider lines that were longer than implied in the original statement of the problem. Students had to “think outside the box,” as Willem said—in this case, literally.

When a problem is well-structured, so are its solution procedures likely to be as well. A well-defined procedure for solving a particular kind of problem is often called an  algorithm ; examples are the procedures for multiplying or dividing two numbers or the instructions for using a computer (Leiserson, et al., 2001). Algorithms are only effective when a problem is very well-structured and there is no question about whether the algorithm is an appropriate choice for the problem. In that situation, it pretty much guarantees a correct solution. They do not work well, however, with ill-structured problems, where they are ambiguities and questions about how to proceed or even about precisely  what  the problem is about. In those cases, it is more effective to use  heuristics , which are general strategies—“rules of thumb,” so to speak—that do not always work but often do, or that provide at least partial solutions. When beginning research for a term paper, for example, a useful heuristic is to scan the library catalog for titles that look relevant. There is no guarantee that this strategy will yield the books most needed for the paper, but the strategy works enough of the time to make it worth trying.

In the nine-dot problem, most students began in Scene #1 with a simple algorithm that can be stated like this: “Draw one line, then draw another, and another, and another.” Unfortunately, this simple procedure did not produce a solution, so they had to find other strategies for a solution. Three alternatives are described in Scenes #3 (for Alicia) and 4 (for Willem and Rachel). Of these, Willem’s response resembled a heuristic the most: he knew from experience that a good  general  strategy that  often  worked for such problems was to suspect deception or trick in how the problem was originally stated. So he set out to question what the teacher had meant by the word  line  and came up with an acceptable solution as a result.

Common Obstacles to Solving Problems

The example also illustrates two common problems that sometimes happen during problem-solving. One of these is  functional fixedness : a tendency to regard the  functions  of objects and ideas as  fixed  (German & Barrett, 2005). Over time, we get so used to one particular purpose for an object that we overlook other uses. We may think of a dictionary, for example, as necessarily something to verify spellings and definitions, but it also can function as a gift, a doorstop, or a footstool. For students working on the nine-dot matrix described in the last section, the notion of “drawing” a line was also initially fixed; they assumed it to be connecting dots but not extending lines beyond the dots. Functional fixedness sometimes is also called  response set , the tendency for a person to frame or think about each problem in a series in the same way as the previous problem, even when doing so is not appropriate for later problems. In the example of the nine-dot matrix described above, students often tried one solution after another, but each solution was constrained by a set response not  to extend any line beyond the matrix.

Functional fixedness and the response set are obstacles in  problem representation , the way that a person understands and organizes information provided in a problem. If information is misunderstood or used inappropriately, then mistakes are likely—if indeed the problem can be solved at all. With the nine-dot matrix problem, for example, construing the instruction to draw four lines as meaning “draw four lines entirely within the matrix” means that the problem simply could not be solved. For another, consider this problem: “The number of water lilies on a lake doubles each day. Each water lily covers exactly one square foot. If it takes 100 days for the lilies to cover the lake exactly, how many days does it take for the lilies to cover exactly half of the lake?” If you think that the size of the lilies affects the solution to this problem, you have not represented the problem correctly. Information about lily size is  not  relevant to the solution and only serves to distract from the truly crucial information, the fact that the lilies  double  their coverage each day. (The answer, incidentally, is that the lake is half covered in 99 days; can you think why?)

Strategies to Assist Problem-Solving

Just as there are cognitive obstacles to problem-solving, there are also general strategies that help the process be successful, regardless of the specific content of a problem (Thagard, 2005). One helpful strategy is  problem analysis —identifying the parts of the problem and working on each part separately. Analysis is especially useful when a problem is ill-structured. Consider this problem, for example: “Devise a plan to improve bicycle transportation in the city.” Solving this problem is easier if you identify its parts or component subproblems, such as (1) installing bicycle lanes on busy streets, (2) educating cyclists and motorists to ride safely, (3) fixing potholes on streets used by cyclists, and (4) revising traffic laws that interfere with cycling. Each separate subproblem is more manageable than the original, general problem. The solution of each subproblem contributes to the solution of the whole, though of course is not equivalent to a whole solution.

Another helpful strategy is  working backward   from  a final solution to the originally stated problem. This approach is especially helpful when a problem is well-structured but also has elements that are distracting or misleading when approached in a forward, normal direction. The water lily problem described above is a good example: starting with the day when  all  the lake is covered (Day 100), ask what day would it, therefore, be half-covered (by the terms of the problem, it would have to be the day before, or Day 99). Working backward, in this case, encourages reframing the extra information in the problem (i. e. the size of each water lily) as merely distracting, not as crucial to a solution.

A third helpful strategy is  analogical thinking —using knowledge or experiences with similar features or structures to help solve the problem at hand (Bassok, 2003). In devising a plan to improve bicycling in the city, for example, an analogy of cars with bicycles is helpful in thinking of solutions: improving conditions for both vehicles requires many of the same measures (improving the roadways, educating drivers). Even solving simpler, more basic problems is helped by considering analogies. A first-grade student can partially decode unfamiliar printed words by analogy to words he or she has learned already. If the child cannot yet read the word screen , for example, he can note that part of this word looks similar to words he may already know, such as  seen  or  green,  and from this observation derive a clue about how to read the word  screen . Teachers can assist this process, as you might expect, by suggesting reasonable, helpful analogies for students to consider.

Video 5.4.1. Problem Solving explains strategies used for solving problems.

Many systems for problem-solving can be taught to learners (Pressley, 1995). There are problem-solving strategies to improve general problem solving (Burkell, Schneider, & Pressley, 1990; Mayer, 1987; Sternberg, 1988), scientific thinking (Kuhn, 1989), mathematical problem solving (Schoenfeld, 1989), and writing during the elementary years (Harris & Graham, 1992a) and during adolescence (Applebee, 1984; Langer & Applebee, 1987).

A problem-solving system that can be used in a variety of curriculum areas and with a variety of problems is called IDEAL (Bransford & Steen, 1984). IDEAL involves five stages of problem-solving:

• Identify the problem. Learners must know what the problem is before they can solve it. During this stage of problem-solving, learners ask themselves whether they understand what the problem is and whether they have stated it clearly.
• Define terms. During this stage, learners check whether they understand what each word in the problem statement means.
• Explore strategies. At this stage, learners compile relevant information and try out strategies to solve the problem. This can involve drawing diagrams, working backward to solve a mathematical or reading comprehension problem, or breaking complex problems into manageable units.
• Act on the strategy. Once learners have explored a variety of strategies, they select one and now use it.
• Look at the effects. During the final stage of the IDEAL method, learners ask themselves whether they have come up with an acceptable solution.

Video 5.4.2. The Problem Solving Model explains the process involved in solving problems. These steps can be explicitly taught to enhance problem-solving skills.

Candela Citations

• Problem-Solving. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/edpsy/chapter/problemsolving. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Educational Psychology. Authored by : Kelvin Seifert and Rosemary Sutton. Provided by : The Saylor Foundation. Retrieved from : https://courses.lumenlearning.com/educationalpsychology. License : CC BY: Attribution
• Educational Psychology. Authored by : Bohlin. License : CC BY: Attribution
• Problem Solving. Authored by : Carole Yue. Provided by : Khan Academy. Retrieved from : https://youtu.be/J3GGx9wy07w. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• The Problem Solving Model. Provided by : Gregg Learning. Retrieved from : https://youtu.be/CDk_BD1LXiI. License : All Rights Reserved

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ORIGINAL RESEARCH article

This article is part of the research topic.

Invention Education and STEM: Perspectives and Possibilities

Systematic Review of Invention Education Research Landscape: State of the Discipline and Future Directions Provisionally Accepted

• 1 Saline High School, United States
• 2 Eastern Michigan University, United States

The final, formatted version of the article will be published soon.

Invention and innovation education and its associated practices (e.g., problem-finding, problem-defining, learning from failure, iterative problem-solving, innovation-focused curricula, collaboration, and maker spaces) are moving from the periphery to the center of education at an ever-increasing pace. Although the research and literature on invention and innovation education, collectively termed as Invention Education (IvE) in this research, is on the rise, to our knowledge no attempt has been made to systematically review the literature available on the topic. To address this gap, we identify, collect, and systematically review scientific literature on IvE. We conduct Bibliometrix-based and targeted analysis to identify the topics, sources, authors, and articles most cited, as well as prominent countries publishing IvE literature. Another objective of this research is to uncover the intellectual, conceptual, and social structures of IvE. A third objective is to identify the progress made and the challenges being faced in furthering IvE and propose future directions. Our review shows that the field has seen substantial growth, especially in recent years particularly in the USA. Research shows IvE’s importance in nurturing a well-rounded, innovative, and skilled future workforce, emphasizing creativity, critical thinking, collaboration, adaptability, and problem-solving skills. Although with a plethora of curricula and K-20 programs in USA, followed by South Korea, and China, IvE lacks unifying conceptualization, definitions and frameworks. The lack of commonly accepted terms and theoretical bases, and difficulties integrating invention into STEM coursework, are compounded by barriers like resource limitations, curriculum constraints, and the need for teacher training and support. The review underscores the need for IvE to address and dismantle inventor stereotypes and cultivate a diverse and inclusive generation of innovators. It points to the impact of gender and stereotypes on participation in IvE programs and the importance of promoting equity and access to IvE opportunities for all students. The article concludes with a discussion of challenges and future research directions to address them.

Keywords: Invention education, Innovation education, Systematic Literature Review, Bibliometrix, problem-solving

Received: 28 Aug 2023; Accepted: 01 May 2024.

Copyright: © 2024 Dalela and Ahmed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Miss. Suhani L. Dalela, Saline High School, Saline, 48176, Michigan, United States

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“Being a part of SkillsUSA and competing in the skills competition has helped me learn new skills with my hands and work on teamwork, communication and leadership skills,” Delvis said. “You learn how to work with other people that aren’t like you and get your mind thinking about your future career.”

Along with the individual contests, all competitors at the SkillsUSA State Leadership and Skills Conference were required to submit a resume and take a professional development test that focused on workplace, professional and technical skills as well as overall knowledge of SkillsUSA.

“SkillsUSA helps provide real-world context to the content being taught by classroom educators,” said Kent Storm, state director for SkillsUSA Iowa. “Taking the learning beyond the classroom allows students to grow and learn next to industry partners and gain valuable experience."

As one of Iowa’s career and technical student organizations (CTSO) , SkillsUSA champions the skilled trades industry and provides opportunities for students to apply the skills they have developed in classrooms through conferences, competitions, community service events, worksite visits and other activities.

“Participation in a CTSO like SkillsUSA helps students gain hands-on experience and connect classroom curricula to careers,” said Cale Hutchings, education consultant at the Iowa Department of Education. “Through CTSOs, students can become leaders and strengthen their employability skills, which is valuable as they explore potential next steps in their college and career pathways.”

SkillsUSA boasts a roster of over 400,000 members nationwide. In Iowa, over 1,300 students and advisers in career and technical education programs participate in local SkillsUSA chapters.

At Southeast Polk, 21 student members are a part of their SkillsUSA chapter. Led by industrial technology teachers and chapter advisers Ryan Andersen and Brett Rickabaugh, the students have been involved with several community service projects, employer presentations and opportunities to work closely with instructors.

“Any time a student participates in SkillsUSA, it gives us more time with that student to elaborate on what we’ve learned in class,” Andersen said. “They can connect the idea to the planning, design and completion of a project and how that activity fits into a real career. That’s something we can’t replicate without a CTSO.”

Anderson also stated that students who participate in SkillsUSA and activities like the State Leadership and Skills Conference build confidence through their experiences.

“It really helps students to have the confidence to rely on their skills and what they know,” he said. “The skills competition requires them to use problem-solving skills and build off their knowledge to continue to learn and persevere.”

This year’s first-place winners at the SkillsUSA State Leadership and Skills Conference will move onward to compete with 6,000 other students at the national conference in Atlanta this June.

For Simon and Delvis, the skills competition was another step in building necessary skills and acumen for their futures. Simon, with his penchant for cabinet making, already has a full-time job lined up after graduation with a local cabinet shop. Additionally, Delvis would like to pursue something within the computer science field, perhaps in the coding or software engineering areas, and although he is changing fields, he believes SkillsUSA has helped him feel more prepared for the future.

“It has definitely helped me with skill-building and problem-solving,” he said. “What I’ve learned will be beneficial no matter what I decide to do next.”