|
|
|
|
Teachers’ Cognitive Flexibility on Engagement and Their Ability to Engage Students: A Theoretical and Empirical Explorationby Kristy Cooper Stein, Andrew Miness & Tara Kintz - 2018 Background: Student engagement is a cognitively complex domain that is often oversimplified in theory and practice. Reliance on a single model overlooks the sophisticated nature of student engagement and can lead to misconceptions and limited understandings that hinder teachers’ ability to engage all of their students. Assessing varied models simultaneously frames student engagement as a dynamic process contingent upon interactions among many contextual variables. Purpose: We explore the relationship between how high school teachers understand student engagement and their ability to consistently engage students in their classes. We present cognitive flexibility theory and its seven reductive biases to illustrate the complexity of engaging students across contexts and subjects. This theory makes a compelling a priori case that teachers who more consistently and effectively engage students in their classes are likely to be those who possess higher levels of cognitive flexibility in the domain of student engagement. To test this hypothesis empirically, we asked: Do teachers who are more effective at engaging students reveal more cognitive flexibility when discussing student engagement, as compared with teachers who are less effective at engaging students? Research Design: We present a mixed-methods case study conducted over three years at one high school. We utilize annual student survey data to identify teachers with whom students reported relatively more and less classroom engagement. Then, we examine the comments of 18 teachers who participated in annual focus groups about student engagement across those three years to identify differences in how more and less engaging teachers express cognitive flexibility in their understanding of student engagement. Findings: We find that teachers whom students found more engaging tended to illustrate more cognitive flexibility in how they thought and spoke about engagement. By contrast, teachers whom students rated as less engaging tended to see engagement in more simplistic and compartmentalized ways. Within these trends, the data provide evidence that individual teachers fall along the seven theorized continuums regarding the extent to which they demonstrated cognitive flexibility on engagement. Conclusions: By bringing cognitive flexibility theory to the domain of student engagement, we call for a new research agenda focused on understanding the development of teachers’ knowledge of student engagement and, in turn, engaging instruction. In place of receiving a new model, tool, or checklist, teachers need opportunities to grapple with the complexity of engagement, to see and analyze various cases, and to build schema in relation to their classroom practice. Student engagement in the classroomincluding active involvement during class, genuine interest in academic learning, and a willingness to attempt rigorous tasksis an essential component of education. Vital engagement outcomes include learning, persistence in the face of challenges, academic achievement, high school graduation, educational attainment, and overall well-being in life (Bundick, Quaglia, Corso, & Haywood, 2014; Shernoff, 2013). Yet findings repeatedly show low engagement in U.S. high schools, particularly among students of color and low-income students (Boser & Rosenthal, 2012; Lawson & Lawson, 2013). At the same time, teachers and administrators express that they want engaged students. They know their jobs are easier and more rewardingand that students learn morewhen students are behaviorally, emotionally, and cognitively engaged in learning (Cooper Stein, Kintz, & Miness, 2016). Despite the multitude of reasons to engage students, many teachers report that they and their colleagues lack sufficient knowledge of how to engage students (Education Week Research Center, 2014). Given that what teachers know impacts how they teach (Wei, Darling-Hammond, Andree, Richardson, & Orphanos, 2009), we argue that increasing engagement requires insight into how teachers understand and think about student engagement. To this end, we approach student engagement as a knowledge domain for teachers, and we theoretically and empirically explore the relationship between how high school teachers understand engagement and their ability to consistently engage their students during class. We begin by arguing that student engagement is a cognitively complex domain that is often oversimplified in theory and practice. Using four models of student engagement (Appleton, Christenson, Kim, & Reschly, 2006; Bundick et al., 2014; Cooper, 2014; Lawson & Lawson, 2013), we illustrate both the multitude of dynamic factors that constitute and impact engagement and the tendency for researchers to oversimplify engagement when communicating new findings. We further argue that presenting and analyzing several models could be a powerful strategy for illustrating the inherent complexity of engagement. We then integrate student engagement theory with cognitive flexibility theory (Spiro, Coulson, Feltovich, & Anderson, 1988) as a framework for (1) conceptualizing how teachers understand student engagement in relatively simplistic and/or complex ways, (2) considering how those understandings shape teachers abilities to engage students, and (3) theorizing the types of learning experiences that could help teachers to develop an understanding that would support more engaging practice. Building on this theoretical foundation, we then introduce three years of empirical data from annual focus groups with 18 high school teachers discussing student engagement. Using surveys from 4,669 students collected over three years, we group teachers into those whom students report as relatively more and less engaging, and we use comments from two more engaging teachers and two less engaging teachers to illustrate how teachers in these two groups expressed different levels of cognitive flexibility in regards to student engagement with learning in the classroom. Through these analyses, we demonstrate that increasing student engagement will likely require integrating more complexity and multidimensionality into teachers understandings of student engagement as a domain of knowledge and practice. MODELING THE COMPLEXITY OF STUDENT ENGAGEMENT Models are essential in that they approximate reality and provide possible explanations for specific phenomena. In the case of student engagement, researchers have developed distinct models to explain the construct and its originary elements. In isolation, these models describe intricate features and workings of student engagement at the individual, institutional, and societal levels. When examined collectively, though, the varied perspectives on student engagement have the potential to contribute to a more informed, accurate, and holistic understanding of the dynamic interactions at play. For this reason, we argue that reliance on a single model overlooks the sophisticated nature of student engagement and can lead to misconceptions and limited understanding that can drastically and unnecessarily limit teachers ability to engage all of their students. We assert that reflecting on multiple models in unison is a more informative way to understand the complex domain of student engagement. Assessing varied models simultaneously frames student engagement as a multi-layered, dynamic process contingent upon interactions among a multitude of contextual variables. The cognitive activity of analyzing and integrating multiple models could support teachers in constructing their own complex schema and nuanced understandings of engagement, which in turn could enable them to engage students in enhanced ways (Spiro et al., 1988). To illustrate these points, we consider four models of student engagement that come from recent articles in top-tier research journals. These models, selected for their intricateness, clarity, and overall representation of salient themes across the literature, convey how coordinating student engagement cannot be reduced to a single factor. Each model powerfully explores the embedded contexts in which student engagement occurs from a particular perspective, and collectively they capture the dynamic and detailed processes of engagement. Importantly, these models take varying approaches to establishing what Skinner, Furrer, Marchand, and Kindermann (2008) refer to as the indicators (attributes) and facilitators (causes) of engagement, thereby adding further complexity to the construct of engagement. The four models rely upon similar conceptions of what student engagement is, and this consistency facilitates a natural communication between them. The models share a core assumption that, as a meta-construct, student engagement is composed of behavioral, cognitive, and emotional indicators. In the first model, Cooper (2014) frames student engagement as classroom engagement and operationalizes it as an active state of responding to a class through focused behavior, emotion, and cognition (p. 365). In the second model, Bundick and colleagues. (2014) categorize student engagement as an umbrella construct for related concepts of school engagement, academic engagement, and engagement in schoolworkall of which have behavioral, emotional, and cognitive elements. Aligned with both of these, Appleton and colleagues (2006) also root their model in the same three dimensions of engagement, intentionally attending to cognitive and emotional engagement because such traits are less observable than behavioral cues. Building on these conceptualizations, Lawson and Lawsons (2013) model identifies the behavioral, emotional, and cognitive dimensions of student engagement as merely starting points, and uses an expanded definition of student engagement as conceptual glue that connects student agency and its ecological influences to the organizational structures and culture of the school (p. 433). This view reflects all four of the models, in that the researchers conceive of student engagement as nuanced and relational in practice and as the orchestration of many interrelated parts.
While the models share similar engagement indicators, they each attend to different facilitators of engagement, which lend to their complimentary perspectives. Focusing more closely on the classroom, Cooper (2014) emphasizes the importance of connective instruction in eliciting engagement among high school students. Connective instruction, a concept that emerges from the psychological work of Martin and Dowson (2009), embodies the notion that students are more likely to feel emotionally engaged in a classroom when they feel strong connections in three domains: connections with the teacher, connections with the content, and connections with the instructional experience. Cooper (2014) argues that classroom contexts that engage students in these emotionally connective ways are more engaging for adolescents than classrooms that focus merely on academic rigor or lively methods of instruction because of connective instructions link to identity development. The Student Engagement Core Model presented by Bundick and colleagues (2014) extends our understanding at the classroom level by examining the content, student, and teacher conditions most suited to promoting positive engagement outcomes. These authors orient student engagement within classroom dynamics and map engagement as an interactional process achieved through strong studentteacher relationships, high levels of teacher competence, and relevant academic experiences for students. While Bundick and colleagues (2014) frame engagement as related to many factors such as race, ethnicity, socio-economic status, parental support, and structural conditions, they exclude these variables from their model because such facilitators of engagement exist outside of teachers control, and they seek to identify actionable information for educators. The intentional omission of particular facilitators speaks to the varied scope researchers set and to the highly nested nature of student engagement within school, family, and social contexts. Appleton and colleagues (2006) take up the nested, contextual nature of engagement by focusing on facilitators occurring with the family, peer group, and school. They illustrate the multidimensionality of engagement by examining the diverse factors at play in each domain. Within the school context, for example, Appleton and colleagues (2006) identify school climate, instructional programming and learning activities, mental health support, clear and appropriate teacher expectations, goal structure, and teacherstudent relationships as all impacting student engagement. The authors further see the family and peers as impacting engagement through nine other context-specific factors. Across these contexts, Appleton and colleagues (2006) model delineates academic, behavioral, cognitive, and psychological indicators linked to engagement that are experienced uniquely by individuals and that lead to academic, social, and emotional outcomes. Through this complex, contextual approach, Appleton and colleagues (2006) identify broad factors influencing classroom interactions. Building on the more holistic approach of the Appleton model, Lawson and Lawsons (2013) model advances a multilayered view of student engagement. Specifically, Lawson and Lawson (2013) focus on interactions among facilitators that shape and are shaped by engagement in the home and community, in the school, and in academic activities. This model recognizes that students engage simultaneously across contexts and enter into these situations with particular dispositions developed through past experiences with individuals and institutions. By accounting for the nested experiences of students, this model frames student engagement more holistically and challenges the notion that engagement can simply be transmitted from teacher to student. Lawson and Lawson (2013) advocate for considering the attentional, positional, and socio-cultural acts of engagement in that these domains help explain potential influences on individual and group engagement patterns. They thus highlight how for each student, the experience of engaging does not reduce to a single determinant, but rather can be explained within larger social systems. Collectively, these four models illustrate that student engagement operates on multiple levels for individual students. Student engagement connects with many dimensions of students lives and is the product of countless interactions between personal, psychological, environmental, instructional, relational, and contextual factors. Each model thoughtfully speaks to a particular level of action at the classroom or school or societal level, and contributes to our understanding of student engagement by richly describing different indicators and facilitators of the process. Together, these models reflect the complexity of student engagement and the multitude of insights and understandings that could inform teachers efforts to engage students. COGNITIVE FLEXIBILITY As student engagement is a cognitively complex domain, we assert that engaging students requires teachers to possess cognitive flexibility in regards to teaching for engagement. Spiros cognitive flexibility theory, a seminal theory of cognition, defines cognitive flexibility as the selective use of knowledge to adaptively fit the needs of understanding and decision making in a particular situation (Spiro, Coulson, Feltovich, & Anderson, 1988, p. 5; reprinted as Spiro et al., 2004). Developing cognitive flexibility in a complexor ill-structureddomain like student engagement requires what Spiro and colleagues (1988) term advanced knowledge acquisition, which is the means by which practitioners develop mastery of complex concepts and proficiency in utilizing and applying those concepts. These theorists argue that, without advanced knowledge, practitioners illustrate reductive biases, such that they hold oversimplified understandings of ill-structured domains that are rife with misconceptions. Cognitive flexibility theory outlines seven forms of reductive bias and provides remedies for each. As the remedies constitute contrasts to each bias, they characterize advanced knowledge in a complex domain. In Figure 1, we show these seven reductive biases and remedies as representing endpoints along seven continuums of cognitive flexibility. Whereas Spiro and colleagues apply this theory to the domains of training medical students and designing multimedia learning environments (e.g., Spiro et al., 1988; Spiro, Collins, & Ramchandran, 2007a, 2007b), here we contextualize these seven continuums within the domain of engaging students in K12 classrooms as a way to bring cognitive flexibility theory to the field of student engagement. Figure 1. Seven Continuums of Cognitive Flexibility Theory
Fundamentally, a lack of cognitive flexibility entails oversimplification and over-regularization of complex, irregular phenomena (the reductive bias in continuum #1). Practitioners who exhibit this bias mentally collapse by standardizing divergent concepts and interpreting them as uniform, rather than fully grasping and embracing differences, complexities, and interactions among concepts (Spiro et al., 1988). With student engagement, we could imagine a teacher having a simple mental representation of what facilitates engagementsuch as a simple understanding of the proverbial rigor, relevance, and relationshipsthat prevents the teacher from seeing and attending to nuances in what engages various students, even within these dynamics (e.g., variations in the types of teacherstudent relationships that engage different students, how students perceptions of rigor influence their relationships with teachers, etc.). Such simplified understandingwithout attention to variations and interconnections among the concepts of rigor, relevance, and relationshipscould minimize the teachers ability to use this model aptly and effectively to engage all students. The teacher would incorrectly see the three concepts as lacking complexity, working uniformly for all students, and separated from one another. The remedy for this bias is to support teachers in developing an understanding of the full complexity of engagement by identifying limitations of simplified understandings, by identifying exceptions to over-regularized notions, and by exposing the superficial nature of simple interpretations (Spiro et al., 1988). Relatedly, practitioners who lack cognitive flexibility often rely on a single mental representation of a concept (#2), as opposed to having multiple representations that account for variability, inherent contradictions, and the full range of related concepts and processes that cannot possibly be captured by one model or theory. For example, holding multiple mental representations of the facilitators of student engagement could enable teachers to draw on different engagement strategies for different purposes and students, understand shortcomings and limitations in particular approaches to engagement, or diagnose and remedy engagement-related problems. A teacher holding just one mental representation of engagement would be less able to engage in these analytic processes. To remedy this bias, Spiro and colleagues advise exposing practitioners to integrated multiple analogies. For example, teachers examining the potential of one particular model to represent the complexities of student engagement could identify the limitations and misleading elements of that model and examine how other models might be integrated to better represent the phenomenon. To this end, teachers critiquing the Cooper (2014) model described earlier could use the models from Bundick et al. (2014), Appleton et al. (2006), and Lawson and Lawson (2013) to point out shortcomings in Coopers model. By contrasting and integrating models and discussing the analytic process with others, teachers would develop a more complexand likely, more accuratemental representation of engagement that would be cognitively superior to the understanding from just one model. Reductive bias also occurs when practitioners apply their understanding of a complex domain uniformly across cases through generic, generalized principles (#3), and thus fail to acknowledge variability across cases. We can envision teachers trying to engage all students in all classes and during all lessons from one general idea of what causes student engagement. A teacher might believe, for example, that all students find fun in-class activities engaging and so focus on integrating such activities into most lessons. Yet it is unlikely that activities are highly engaging for all students or the most engaging way to deliver all content. And the teacher could uniformly integrate activities without understanding the more abstract elements of why some students find activities engaging (e.g., as opposed to having the entertainment value the teacher assumes is engaging, it might be that activities engage some students because they clearly illustrate concrete connections across academic concepts). To counter such generalized thinking, Spiro and colleagues (1988) advise case analyses and case-based discussions to develop practitioners understanding of variability across individuals and instances. In one application, Spiro and colleagues (2007b) describe multiple analyses of video excerpts. For engagement, teachers could (a) watch four video segments to deeply analyze thought-provoking questions as an engagement strategy, or (b) watch one video segment four times to look first at questioning, then at student-to-student interactions, then at relevance, and finally at teacher wait timeall with the intention of building a complex understanding of patterns and variations across and within cases. Reductive bias also appears in a tendency to generalize across contexts, such that practitioners rely on prepackaged prescriptions that are context-independent (#4). So, a teacher might attempt to engage students in all subjects every year with the same prepackaged prescriptionsperhaps, for example, opening every new unit of study with an instructional video. But, the teachers understanding of why a given approach engages students could be simplistic (e.g., that a video sparks students interest), and the teacher might not understand why the prescription does not engage students in every subject, class, school setting, and generation. So, when a video fails to engage a class, the teacher may presume the group is unmotivated or the video is ineffective, when it may be that instructional videos are outdated in todays multimedia environment. Overreliance on prescriptions can lead to teachers ignoring disengaged students because they cannot figure out why the prescription does not apply in a given context. Spiro and colleagues (1988) pose the remedy of helping practitioners develop knowledge-in-usethat is, knowledge gained in practice by attending to variations across contexts (e.g., examining how contemporary students engage with media content). Developing knowledge-in-use requires exposure to many cases and contexts and deductive learning from those cases and contexts. Similarly, a lack of cognitive flexibility often leads practitioners to rely on rigid, precompiled schema (#5) that serve as recipes for what to do in various situations (Spiro et al., 1988). Yet complex domains require practitioners to possess what Spiro and colleagues refer to as flexible, recombinable knowledge structures (p. 8) that allow practitioners to draw on a variety of insights and strategies that can be applied differently across cases and contexts. The theorists assert that such knowledge structures develop when practitioners actively assemble their own schema, as opposed to having precompiled knowledge structures handed to them. If we think of the example of integrating multiple engagement models, we can expect teachers to develop a more complex understanding if they participate in the integration of models themselves. Simply attending a professional development session in which an expert explains how various models capture the limitations of others, and then presents a broader, integrated model, will not lead to the development and application of flexible schema on engagement. Reductive bias also occurs when practitioners compartmentalize knowledge components (#6), as opposed to seeing concepts and cases as interconnected. With complex domains, Spiro and colleagues (1988) note that abstract concepts are woven together in multiple interconnections that manifest differently across cases and examples. Given these interactions, although cases might be analyzed independently during the learning process, practitioners must be supported in seeing connections across cases. This perspective will enhance their understanding of how learning from past cases can be applied to future cases that are kind of like this earlier one, kind of like that one (p. 9). Through making connections across concepts and cases, practitioners develop a stronger understanding of the complexity of all cases. Finally, Spiro and colleagues (1988) note that reductive bias often occurs as a result of passive transmission of knowledge (#7) from trainers to practitioners, such that personalized knowledge gained through experience and analysis is lost in favor of more simplified, communicable means of explanation. Instead, cognitive flexibility theory favors practitioners actively participating in the learning process and receiving tutorial guidance from expert mentors, as well as adjunct support in the development and management of complexity. This final concept from cognitive flexibility theory mirrors findings from the learning sciences, which assert that individuals learn best by constructing their own knowledge (Piaget, 2000; Sawyer, 2006). If empirical evidence supports our hypothesis that more engaging teachers tend to hold more complex understandings of student engagement, then concerted efforts must be made to help all teachers actively construct these more complex, multidimensional understandings. RESEARCH METHODS Cognitive flexibility theory makes a compelling a priori case that teachers who more consistently and effectively engage students in their classes are likely to be those who possess higher levels of cognitive flexibility in the domain of student engagement. To test this hypothesis empirically, we asked: Do teachers who are more effective at engaging students reveal more cognitive flexibility when discussing student engagement, as compared with teachers who are less effective at engaging students? To answer this question, we draw on two forms of data collected over three years at one high school. First, we utilize annual student survey data to identify teachers with whom students reported relatively more and less classroom engagement. Then, we examine the comments of 18 teachers who participated in annual focus groups about student engagement across those three years to identify differences in how more and less engaging teachers express cognitive flexibility in their understanding of student engagement. In prior analyses of these data, we focused on how more and less engaging teachers described their teaching practice and efforts to engage students. We found that more engaging teachers expressed stronger agency over engagement, which manifested in reflectiveness (continually assessing and improving practice for engagement), adaptivity (modifying instruction midstream in response to disengagement), and support (helping students manage threats to engagement) (Cooper Stein et al., 2016). In this second analysis, we turn our attention from teachers practice to their understandings of what engagement is and what causes student engagement. Using the framework of cognitive flexibility theory, we seek to theorize about the types of learning experiences that could potentially scaffold teachers to a more sophisticated understanding of engagement and thus increase every teachers ability to engage students to high levels. Research Site We collected data at Lincoln High School,1 a large comprehensive high school located on the periphery of a large city in Texas. During the three years of this study, Lincoln served a diverse student body of approximately 2,380 students annually (about 39% White, 29% Latino, 16% Asian, 14% Black; about 27% receiving free and reduced-price lunch). The school was working to increase student engagement through a number of initiatives, including a book study of Schlechtys (2002) Working on the Work, which 33 teachers volunteered to read and discuss. Through this avenue, the principal attempted to shift focus from what teachers were doing to what students were doing. The principal also noted efforts to align professional development to address student engagement through improved technology use, the teaming of teachers, instructional rounds, and a focus on best practices (talking to learn, writing to learn, scaffolding, collaborative group work, etc.), as well as the collection and distribution of annual student engagement survey data, which our team provided. We also provided brief professional development on engagement to the full staff when we delivered annual survey results. Participants Student participants were those who completed engagement surveys. In November 2011, our first year, we had a low student response rate of only 49% (n = 1,261). In subsequent years, November 2012 and 2013, we shortened our survey and the response rates increased to 71% (n = 1,726) and 72% (n = 1,682), respectively. In total, we collected 4,669 student surveys over the three years. The demographics of respondents were fairly similar each year. In all three years, students were 49% male and 51% female. Racial and ethnic distribution was fairly representative of the student body, with an average of 39% White, 22% Latino, 17% Asian, 11% Black, and 8% mixed race.2 Teacher focus group participants included 29 total teachers across the three years, with participation by 21 teachers each year. We asked the principal to recruit teachers who were representative of the staff in regards to gender, experience, content area, responsiveness to school initiatives, and perspectives on teaching and learning. All teachers who were referred by the principal agreed to participate in focus groups. However, some teachers were not able to participate all three years due to a change in their assignment, no longer teaching at the school, or a scheduling conflict. Eighteen teachers participated for at least two years, with 13 participating in focus groups for all three years. In the present study, we focus on comments from the 18 teachers who participated for at least two years. As shown in Table 1, those teachers included 12 women and six men teaching in a variety of subjects, with three to 33 years of experience at the beginning of our study. After collecting our data, we calculated the mean student engagement survey scores for each teacher each year, and we used standardized means to group teachers into those whom students deemed relatively more engaging (consistently above the school mean), relatively less engaging (consistently below the mean), and transitional (sometimes above and sometimes below the mean). Despite subject-area patterns among focus group participants (i.e., three science teachers in the higher group, three math teachers in the lower group), this pattern is not reflected among all teachers in the school. This suggests that the groupings evident in Table 1 are merely a result of our sampling procedures. Table 1. Overview of Teachers Participating in Focus Groups for Two or More Years
Data Collection In November of 2011, 2012, and 2013, teachers administered student surveys during advisory classes to assess students perceptions of their engagement in various classes. In the first year, we asked students to report on all seven of their classes. Given the low response rate that year, in 2012 and 2013, we randomly assigned students three class periods on which to report. Students completed the same 19-item survey for each class on which they reported. This produced a total of 4,651 reports on classes in 2011, 4,659 reports in 2012, and 4,600 reports in 2013. For each class report, we used six survey items modeled on items used by the National Center for School Engagement (2006) to assess students self-reported engagement in that class: How often do you do all of your work in this class? How happy are you when you are in this class? How excited are you about what you are learning in this class? How much do you feel like this class is worth your time? If you dont understand something in this class, how often do you try to figure it out? When you are doing assignments in this class, how often do you concentrate? In the present study, as in prior work (Cooper, 2014), internal reliability for the classroom engagement scale was quite high (a = 0.89). Upon completion of the survey, these items were averaged to create an engagement “score” for each student in each class on which they reported. In addition to reporting their engagement in each class, students also reported their perceptions of teachers practice in 13 areas known to impact engagement (e.g., how often the teacher challenged them, how often they worked in groups, how much they perceived the teacher cared about them). These items were not used to measure engagement, but rather to provide teachers with student perception information that could inform their practice. In February each year, we provided 1.53 hours of professional development to share school-wide and department-level survey results with Lincolns staff of 165 teachers. As three former educators and now researchers (one professor and two graduate students) with expertise in student engagement, we presented strategies that teachers could employ to increase student engagement as well as the aggregate engagement data for the school. Teachers reviewed their department-level data in departmental teams and identified potential areas for further development. At the end of the sessions, teachers received confidential, sealed envelopes containing aggregate survey results for students reporting on their classes. Teachers were advised of the percentages of respondents who reported that each item was quite often or always true (the top two responses out of five Likert-style options). Following the professional development session each year, two members of our research team convened three focus groups of 58 teachers each for 4045 minutes per group. We attempted to compose the focus groups so that each group represented the sample as a whole. We used semi-structured protocols addressing teachers practice and thinking in regards to indicators and facilitators of student engagement. A number of questions linked clearly to concepts within cognitive flexibility theory. For example, teachers understanding of variability across cases and contexts was assessed through the questions, Does your ability to engage students differ across the classes you teach? If so, how? Why do you think this is? and To what extent do you believe a given students engagement changes from class to class? What explains these differences? As another example, we asked teachers how their thinking on student engagement was changing over time and what they saw as contributing to those changes. This was a means of assessing whether and how their understanding of engagement grew through experiences of knowledge-in-use and flexible schema assembly. Each year, the protocol followed the same structure with three main sections: (a) student engagement, (b) student engagement research, and (c) change in instructional practice. From year to year, some questions remained the same, others changed slightly to assess how participants views may have changed over time, and a few questions were different. For example, over the three years we asked: Is there anything you might do differently as a result of what youve heard today? (Year 1); Did you do anything differently over the past year as a result of the survey results you received last year? (Year 2); and As you look at the change in your personal results from last year to this year, what are your reactions, and what do you think explains any difference or lack of differences? (Year 3). The facilitator provided the opportunity for all participants to respond to a given question. When there were no more responses, the facilitator would move on to another question. At times, the facilitator would prompt an individual who had not responded as much, to be sure to include the perspectives of all individuals in the focus group. Verbal affirmations such as yeah and other indicators of agreement or disagreement were noted in the transcription of each focus group. All focus group sessions were recorded and transcribed. Data Analysis For the present analysis, we created individual-level focus group transcripts for the 18 teachers with at least two years of focus group data by creating one Word document containing all of their comments across their two or three years of participation. We then had a third party create pseudonyms for each teacher to blind ourselves from what we knew about each teacher to the extent possible, particularly in regards to their survey scores. Individually, each member of our research team then read through all 18 transcripts to assign codes. We used the seven forms of reductive bias and the seven remedies described in cognitive flexibility theory (see Figure 1) to create our codebook. Our process included an initial analysis and discussion of the codes of six teachers to calibrate our understandings of the codes and to establish at least an 80% coding reliability among the three members of our research team. Analyses and discussions centered on distinguishing among the seven continuums of cognitive flexibility theory by examining how individuals demonstrated cognitive flexibility and sophisticated understanding in their comments, and whether they were relatively high or low on those continuums. We also identified the domains of influenceor facets of students lives and schooling experiencesto which each teacher attributed student engagement (e.g., characteristics of students, of families, of classroom instruction, of teachers, etc.). Within each domain, we then teased out the different factors to which each teacher attributed engagement (e.g., within the domain of instruction, engagement factors might include hands-on activities or differentiated assessment). Ultimately, we compared our codes and came to agreement about how each teacher fared in regards to the seven continuums. We then un-blinded the transcripts and used teachers standardized mean student engagement survey scores to identify each teacher as being more engaging, less engaging, or transitional. Our goal in creating these groups was to examine teachers relative abilities to engage students, as compared with one another. For this exploratory study, we began with this straightforward approach to identifying teachers as relatively more or less engaging, and then we sought to complicate that notion by delving into the intricate differences in teachers underlying understandings of student engagement as a domain of knowledge. In this way, we attempted to move from a simple and concrete conception of a teachers ability to engage students to one that is more abstract, nuanced, and complex over the course of our analysis. Using standardized mean scores for teachers in the same school enabled us to keep the context and research conditions stable, thereby allowing us to better isolate actual differences in teachers personal understanding and knowledge of engagement. In creating the three groups, we excluded one teacher who taught special education through immersion and so did not have individual-level survey scores. Classifying the 17 other teachers into the three groups, we then looked at patterns in how teachers in the three groups understood student engagement and examined their responses according to the seven continuums of cognitive flexibility theory. Limitations We acknowledge a number of potential limitations to this study. First, although our professional development was brief and limited in scope, there were a variety of efforts to increase student engagement at the school, so it is possible that activities within the school may have influenced teachers conceptualizations of engagement. However, our primary focus was on differences in how teachers demonstrated their understanding of the indicators and facilitators of engagement, regardless of what influenced their thinking. Second, the use of a single school site could be a limitation given the lack of a comparison group and the potential for factors at the single site to have contributed to how participating teachers thought about engagement. Nonetheless, our primary intention was to utilize the mean split to identify differences within this group of teachers in how they thought about engagement, and to distinguish potential ways in which cognitive flexibility theory may apply to such differences in teachers whom students rated as more and less engaging. Third, focus groups have limitations as a means of data collection. We used focus groups to provide an opportunity for teachers to participate in the natural evolution of a free-flowing conversation that could include topics we may have not anticipated, but it is possible that some responses were influenced by prior comments. Given the nature of a focus group as a conversation with colleagues, we also acknowledge that an individuals full understanding of a concept might not be revealed in this setting. Regardless, we did find a number of interesting patterns in our focus group data. FINDINGS We found that more engaging teachers more consistently provided evidence of cognitive flexibility, such that they grappled with complexity in regards to student engagement, entertained multiple representations of engagement, and appeared to construct their own schema around the factors that shaped student engagement in their classes. By contrast, less engaging teachers appeared to possess less cognitive flexibility, as they held more consistently to a handful of notions about engagement, and their comments showed less evidence of a willingness, tendency, or ability to see variability across cases or interconnections among elements of engagement. Comments from transitional teachers fell somewhere in the middle, although those who became more engaging over time reflected levels of cognitive flexibility more similar to those of the more engaging teachers. Of course, we did not find that more and less engaging teachers were at opposite ends of the continuum for each of the seven forms of reductive bias. Naturally, the differences were subtler and more nuanced, and we infer that different teachers are lying at different points along each of the seven continuums, with more engaging teachers tending to be further along the continuums in the direction of the remedies to reductive biases. To illustrate these variations, we present two pairs of teachers, with each pair containing one relatively more engaging teacher and one relatively less engaging teacher who are at similar points in their lives and careers. The contrasts within the first pair effectively illustrate the reductive biases and remedies related to singular representation, oversimplification, and prepackaged prescriptions. The second pair more adequately demonstrates the biases and remedies related to generalized principles, rigid schema assembly, and compartmentalization of concepts. Both pairings illuminate the critical contrast between passive and active knowledge acquisition in the domain of student engagement. Tina and Kayla3 The first pair includes Tina and Kayla, both young women who appeared to be in their late 20s or early 30s and who were in their fifth year of teaching during the first year of our study. Tina taught Integrated Physics and Chemistry, Aquatics, and Advanced Placement (AP) Environmental Studies. Across the three years of our study, average composite engagement scores for Tinas students were 0.35, 0.14, and 0.31 standard deviations above the school mean, making Tina a relatively more engaging teacher. Representing the perspective of a less engaging teacher, Kayla taught U.S. history to 11th grade students and coached volleyball and soccer. Her average student engagement scores over three years were 0.53, 0.27, and 1.03 standard deviation units, all well below the school mean. Here, we contrast these two teachers to illustrate how they differed in the extent to which they held singular or multiple representations of student engagement, the levels of simplification or complexity they attributed to engagement, the extent to which they relied on prepackaged prescriptions or knowledge-in-use, and whether they demonstrated passive or active acquisition of knowledge on engagement. The first point of contrastsingular versus multiple representationscomes through in the explanations these two young teachers provided when they discussed the domains of students lives that influenced engagement, and the more specific factors that facilitated engagement within those domains. In Table 2, we present a summary of these domains and factors for each teacher, along with some of the language they used to describe their understandings. The first contrast immediately evident in the table is that Kayla attributed student engagement to influences in two domains of students lives, while Tina considered five domains. Additionally, Kayla cited more factors across those domains, eight as compared to five. Given these differences, we infer that Tinas mental representationor schemafor student engagement likely consisted of more representations than Kaylas. Tina seemed to see student engagement as having layered dimensionsoperating in the family, in the student, in the students interactions with the teacher, in the nature of the instruction, and in the school structures that group students in large or small classes. Kaylas focus on just the student and the instruction suggests that she saw fewer dimensions to engagement than Tina. Just the same, Tinas understanding did not appear to be so sophisticated as to include interconnections among various factors (such as a relationship between a students openness to being engaged and their sense of personal connection with the teacher). However, she was certainly conscious of more layers of influence shaping student engagement, and so likely held more mental representations of the construct, as compared with Kayla.
In digging into the language the two teachers used, there is evidence of the second point of contrastoversimplification versus complexity. Tinas language revealed that she was grappling to understand the complexity of engagement, whereas Kayla saw engagement in more simplistic terms. For instance, in Tinas first comment listed in Table 2, she asserted that she found it very challenging to challenge simultaneously a range of high-performing to low-performing students all in one class, and she reported wanting to improve in that area. For instruction, Tina noted that hands-on activities could be engaging because students were actively doing something, but she acknowledged variations in the extent to which various lab activities were really great for engagement. In these comments, Tina illustrated her understanding that attempting to engage all students during all lessons was complex due to variations across students and academic activities. By contrast, Kayla seemed content with simple explanations for how to engage studentssuch as through hands-on History Alive lessons, group work and group tests, and pacing that gave students a chance to get engaged. Similarly, Kaylas first comment in the student domain was a globalized statement about static differences between AP and non-AP students, with non-AP students being less likely to engage or have high-level conversations. In her next comment, she described the prior years entire junior class as unwilling to do really, really basic stuff like complete a worksheet. Kayla did not attend to variations or complexities, but only referred to students collectively as they or most of my classes and asserted that certain groups uniformly had tendencies to engage or preferences for what they found engaging. Her phrasing suggested that she saw the class, not individual students, as the primary unit of variation, whereas Tina referred to variation at both the class and individual levels. In these ways, Kaylas conceptualization of engagement revealed more of the types of oversimplification and over-regularization found in reductive bias, as compared with Tina. In contrasting this pairing, we also highlight differences in two additional facets of cognitive flexibilityprepackaged prescriptions versus knowledge-in-use and passive versus active acquisition of knowledge. As we have seen in Table 2, Kayla clearly held some prepackaged prescriptions for engagement, such as believing that hands-on History Alive lessons and group work would engage most, if not all, students. Elsewhere in her comments, Kayla also made it clear that she was trying to determine who to listen to when seeking guidance on what she should do in her classroom. In the first year of the study, she stated: As a faculty, weve been told, Step away from the lecture style. It doesnt engage students. OK, when you get to college, thats all it is, is lecture style. Ive gotten four emails from four different students who are now in college saying thank you so much for teaching me how to take notes because thats all we do in college. Who do you listen to? Two years later, in the third year, Kayla was still struggling with this same conflict: Weve been told we want to do more group work, we want to do more this and this, and thats great and all. But if my kids leave my classroom and they dont know how to take notes, Im doing them a disservice. Thats what Im worried about. Are you taking away necessary skillsets to help you succeed later in life? So engagement, yes I would like that to be a good focus. However, skillsets I feel like is a little more important and applicable to your life. Kayla was clearly conflicted about whether to abide by her administrators edicts not to lecture or follow her own belief that lecturing was important for preparing students for college. Her phrasing opposed developing students skillsets to engaging students, and she appeared to believe that only one of those goals could be the right one. We did not see evidence that Kayla understood the inherent complexities in student engagement, such that multiple realities could be true and that the ideas she described were not necessarily competing. For example, it was unclear whether Kayla recognized that there are ways to engage students in lectures, that she could help students develop note-taking skills outside of lectures, and that engagement is a conduit for student learning regardless of the form of instruction. Further, she did not reference the possibility that different students might have benefitted from lecture more than others, different classes might have responded more to a lecture format, and different content might have been better delivered through different instructional formats. While we did not ask Kayla to comment on these possibilities explicitly, her comments suggested that she relied on prepackaged prescriptions for engagement that were generalizable, context independent, and situated in the knowledge of experts other than her. By contrast, Tina indicated that she was constantly and actively working to develop knowledge-in-use by looking for differences (e.g., in students ability levels, in lab activities, in her energy level, etc.) that accounted for the variations in engagement that she observed. In her second-year focus group, she explained, Youre always kinda guessing, going, Well, I think [the lesson] didnt work, but what do the students think about that? How do they really feel about that? Are they getting it, or are they just kinda staring at me? This comment suggested that Tina looked for evidence from students to make assessments about the engagement and learning potential of her instruction. It also revealed that Tina had an inquisitive, growth mindset (Dweck, 2006) about her teaching, which emerged in other comments as well. For example, Tina noted in the third year of the study, her seventh year as a teacher, Maybe Im still figuring stuff out as a teacher, trying to figure out who I am and what Im doing. She also said to the researcher leading the focus group, Id like to email you and see if I can get [survey results for] the two different classes in a breakdown, because ones an upper level course and ones a lower level, and Id like to see how I approach the two differently. This continual effort to try to understand engagement and how to maximize it illustrated the complexity Tina attributed to the domain, her tendency to reflect and inquire in ways that developed knowledge-in-use, and the active role she played in developing her own knowledge on engagement. In all of these ways, we found Tina to possess more cognitive flexibility in regards to engagement than Kayla. Ken and Sean Our second pairing illustrates the differing viewpoints of Ken and Sean, both second-career male teachers who appeared to be between the ages of 50 and 60. In the first year of our study, Ken was in his 12th year of teaching, and Sean was in his 17th. Ken taught Chemistry and AP Chemistry and had average composite engagement scores close to the school mean in the first year (0.02 standard deviation units) and above the mean in the second two years (0.28 and 0.13). We classified Ken as a transitional teacher because he technically went from relatively less engaging to more engaging over time. However, the insights revealed in his comments demonstrated a high level of cognitive flexibility, and so we present him here as demonstrating the perspective of a more engaging teacher. His counterpart, Sean, was a math teacher who taught Pre-AP Algebra, Pre-Calculus, and Pre-AP Pre-Calculus. Seans engagement scores across the three years were 0.51, 0.55, and 0.32, all well below the school mean. Thus, Sean represents the perspective of a relatively less engaging teacher. In Table 3, we present the domains and factors to which Ken and Sean attributed engagement, along with their comments on each factor. The table shows that Ken noted eight factors across three domains of influence and Sean noted seven factors across five domains. Unlike in the prior pairing, here the less engaging teacher, Sean, referenced more domains of influence than the more engaging teacher. At first glance, this might suggest that Sean held a more complex mental representation of engagement. However, in comparing these teachers comments within the domains they cite, it is apparent that Ken had a much more sophisticated understanding of the indicators and facilitators of engagement than Sean, even if he cited fewer domains of influence. We use this pairing to illustrate how these two teachers differed in (1) whether they drew on generalized principles or noted variability across cases; (2) the degree to which they perceived concepts of engagement to be compartmentalized or interconnected; (3) the extent to which they were rigid or flexible in their schema assembly; and (4) whether they were more passive or active in acquiring knowledge on engagement.
The first two differencesgeneralized principles versus variability of cases and compartmentalization versus interconnectednessare evident in how Ken and Sean talked about the factors they saw as impacting engagement. As their first factor in the student domain in Table 3, both Ken and Sean described how they saw the academic level of students and classes as impacting their ability to engage students. While both acknowledged some variability, Kens attention to variation was more nuanced and showed interconnections across domains of influence, which Sean did not identify. Making a stark generalization, Sean described students in pre-AP classes as the ones that are putting out the most effort because they aspired to become doctors or engineers, and he credited the switch to having all pre-AP classes as the sole reason his survey scores increased. Ken similarly compared classes with pre-AP students to classes with students who were deemed academically at risk, but his comments revealed more mindful attention to how variations in students academic needs had implications for engagement. Ken observed that, because students in lower-level classes needed more time and support to understand academic concepts, there was a danger of having less personal studentteacher interactions and thus a less personal classroom climate. It was this difference in climate to which Ken ultimately attributed varying levels of engagement. Further, Ken noted variations within groups, particularly among pre-AP students, and he corrected himself when he realized he was starting to generalize. Kens comment also illustrates his attention to connections across domains of influencein this case, how academic ability shaped studentteacher relationships, which shaped the classroom climate. This insight demonstrated a more nuanced, sophisticated, and interconnected understanding of engagement, as compared with that held by Sean. These same two points of contrast also come through in how Sean and Ken described instructional factors related to engagement. Sean told the researchers conducting the focus groups that Theres not a teacher in this building that does not already do the engaging practices we advocated in our presentation and assessed in our survey. He went on to say: The majority of what was presented is easily implemented because the teachers are already doing the hard part. Its the implementation, maybe some additional praise in the classroom, things along that line. Despite the fact that his student engagement scores were considerably below the school mean, Sean vastly oversimplified what it would take to increase engagement throughout the school. In this and other comments, Sean suggested that he and other teachers possessed the necessary knowledge for engaging all students; they just needed to determine which strategies to use on a given day or for a given lesson. He suggested that redoing lessons and tests or rewriting lesson plans would resolve any disparities in engagement. In such statements, Sean asserted that engaging students was relatively simple and could be achieved through a series of generalized practices that teachers already knew. He saw only minimal need for attending to variability across cases, and he made no references to connections across factors that impacted engagement. By contrast, Kens comments on instruction demonstrated understanding of overlapping, interconnected issues and the need for differentiation to accommodate variability across cases. He linked differentiated curriculum and assessment with the needs for student autonomy, self-directed learning, and studentteacher relationships. He also referenced the need to acknowledge students points of view on the content they were learning, and emphasized that until students saw content as relevant there would be no significant improvements in engagement. In his comments, Ken revealed an understanding that engagement was complex, that it would be tougher to increase than people expected, and that it was a puzzle with multiple pieces. He also noted that, even with 12 years of teaching experience, he was still working to develop his practice around engagement. In his comment on giving students attention, his final comment in Table 3, Ken noted that he would like to do more to engage students in the middle of the academic hierarchy so they are not just nameless, faceless. In this and other statements, Ken clearly refuted the notion that engagement could be elicited through simple, generalized principles, and he asserted that engagement required teacher action on multiple, complex fronts. The third and fourth key differences across these two teachersrigid versus flexible schema assembly and passive versus active acquisition of knowledgecame through in many comments, and in the fact that only Ken referred to acquiring new knowledge on engagement. Seans comments described abovethat he and other teachers were already doing what they needed to do to engage students and perhaps needed only some additional praise in the classroom, things along that lineillustrated his sense that he already possessed the knowledge for engagement. As Sean was never specific about the types of engaging teaching practices he used, we cannot determine if they were strategies he developed and flexibly combined and recombined to fit the needs of various students, topics, or lessons, or if they were strategies he received as precompiled recipes for engagement. But we can conclude that Sean did not see his practice as continuing to evolve at that time, despite having relatively low scores on the student engagement survey. Ken, on the other hand, described reading about 10 books right now that were expanding his instructional skillset and informing his thinking about teaching for engagement. As a way to integrate his new learning into his evolving schema, he also identified connections between (1) what he was learning about differentiating curriculum and assessment from those books and (2) engagement practices and concepts. This active acquisition and integration of new knowledge demonstrated how Ken was building his own understanding of engaging practice in ways that would allow him to be more flexible in his application of his knowledge. DISCUSSION Through theoretical and empirical lenses, we have attempted to illustrate that teachers understanding of the indicators and facilitators of student engagement has implications for their ability to engage students. Theoretically, cognitive flexibility theory provides seven reductive biases and remedies that can help us understand how teachers conceptualizations of student engagement can appear to fall short of the complexity needed for teachers to enact advanced knowledge in flexible ways that engage students across contexts and subject areas. Empirically, our research findings suggest that teachers whom students found more engaging tended to illustrate more cognitive flexibility in how they thought about and spoke about engagement. By contrast, teachers whom students rated as less engaging tended to see engagement in more simplistic, singular, and compartmentalized ways. Within these trends, the data provide compelling evidence that individual teachers fall along the seven theorized continuums in terms of the extent to which they demonstrated cognitive flexibility regarding engagement. By applying cognitive flexibility theory to student engagement through these lenses, we feel convinced that efforts to increase student engagement must include active professional learning experiences for teachers that provide them with a deep and sophisticated understanding of student engagement so that they can consistently and expertly apply that knowledge in practice. In turn, we argue that a goal of such professional development efforts would be to progressively move people along the continuums from less to more cognitively complex thinking about engagement. To this end, we believe our findings raise the question: How did more engaging teachers develop deeper, more complex understanding of student engagement? We present several possibilities to explain how these differences may have emerged naturally, as a way to theorize how all teachers might acquire a more complex understanding of engagement. One potential explanation is that some teachers gained cognitive flexibility regarding student engagement through experience if they were prone to certain habits of mind, such as being reflective. In prior analyses of this data, we found that more engaging teachers were more likely to be reflective about their teaching practice and adapt their teaching midstream if they sensed that students were disengaged (Cooper Stein et al., 2016). From the perspective of cognitive flexibility theory, we might assert that these tendencies to respond to evidence about ones teaching may promote deductive learning from experience, which can lead to the development of knowledge-in-use. Teachers who are not prone to reflective ways of thinking seemed to be less likely to develop cognitive flexibility through experience. It is possible that these less reflective teachers developed a tendency toward oversimplification in response to the complex challenges of trying to engage students. Their reductive worldview (Feltovich, Coulson, & Spiro, 2001) could be a response to repeated difficulties in attempting to engage students and an effort to develop generalized principles to guide their practice instead. Cognitive flexibility theory also suggests that more engaging teachers developed a complex understanding through exposure to different representations of engagement in various settings. Such experiences may have promoted their understanding of variability across cases and contexts, and supported their development of complex, flexible schema. These teachers may have had opportunities to observe other teachers engaging students in ways they had not previously encountered; they may have heard about experiences in which teachers were effective in different ways; and they may have worked with students with a wide range of backgrounds, needs, dispositions, and orientations. As these teachers experimented with new approaches, they may have been able to make connections among multiple alternative understandings. They may have been exposed to more experiences that cultivated their flexible and complex understanding of student engagement. Or perhaps they had both: exposure to different representations of engagement in various settings and the habits of mind to learn from that experience. Another possibility is that cognitive flexibility was cultivated through professional development and structured opportunities for coaching and support. Some training or professional development programs may have fostered the more engaging teachers schema assembly through meaningful learning experiences involving application and synthesis of new knowledge, rather than the memorization and retrieval of rigid or existing schemas. More engaging teachers may also have had opportunities for participatory learning in which they were involved in active collaboration and constructive experiences that required them to draw on multiple representations to acquire new knowledge. These teachers may have been exposed to different case studies of engagement to expand their awareness of multidimensionality and variability of representations. Furthermore, other individuals may have directly supported these more engaging teachers in managing complexity by their own example or through mentoring, guidance, and ongoing support. That less engaging teachers did not have a similarly complex understanding suggests that teachers who do not gain cognitive flexibility through experience require scaffolding to develop a more complex understanding of how to engage students. By bringing cognitive flexibility theory to the domain of student engagement, we are setting a new research agenda focused on understanding how to support the development of teachers cognitive flexibility regarding student engagement and, in turn, engaging instruction. The understanding that teachers are at different places in their development of cognitive flexibility is critical for the design of future professional development to enhance student engagement. The differences among teachers cognitive flexibility and their abilities to engage students in the classroom indicate the need to find ways to reconceive how we think about the purpose and structure of professional development. In place of receiving a new model, tool, or checklist, teachers need opportunities to grapple with the complexity of engagement, to see and analyze various cases, and to build schema in relation to their classroom practice. At the same time, it is important to address the contextual elements of teaching today. Rather than placing another expectation on teachers, our intention in introducing the notion of cognitive flexibility is to support teachers capacity to manage the demands of teaching and their potential to promote meaningful engagement in learning. One implication from this theoretical and empirical examination is that cognitive flexibility theory could guide the design of interventions that promote teachers advanced knowledge acquisition in the domain of student engagement. Cognitive flexibility theory asserts that re-presenting the same information in different contexts and from different perspectives can promote a more complex understanding and counter tendencies toward oversimplification (Spiro & Jehng, 1990). We beseech researchers and practitioners to develop intervention designs in which teachers study different models of engagement through multiple mental and pedagogical representations. Teachers could collaborate with colleagues to critically examine and discuss multiple alternative student engagement models and make connections among knowledge elements as well as applications to teaching practice. Rather than the common approach of presenting one model or conceptualization of engagement and then providing a list of ways to change instruction, we advocate for teachers to be involved in their schema assembly through a process of analyzing multiple models in light of their perspectives on engagement and in the context of valid learning purposes and tasks. The intention would be to present the complexity of engagement as it naturally occurs so that teachers may acquire knowledge of that complexity. As part of these interventions, cognitive flexibility theory asserts teachers best develop functional, conceptual understanding by looking at cases of application with examples of engaging instruction and discussing mini-cases drawn from full cases of instructional practice (Spiro et al., 2007b). Effective interventions would thus include examination of mini-cases, which could serve to consolidate the processes of acquiring experience, analyzing teaching, and promoting teachers development of their own schema. Teachers participatory learning and the opportunity to acquire more case-processing experience could promote teachers capacity in the ill-structured domain of engagement. In turn, the examination of knowledge in many different ways could enhance multidimensional knowledge representations regarding student engagement, and teachers capacity to form complex and varied understandings incorporating other aspects of existing knowledge, especially in new contexts that require knowledge use. Furthermore, effective interventions would be ongoing and involve structured support for teachers (Wei et al., 2009). Many teachers require tutorial guidance, adjunct support, and coaching to develop the cognitive skills needed to adapt, assemble, and apply knowledge in response to new situations and different contexts (Spiro et al., 1988). There is a wide range of variability in the way new knowledge may be used in the classroom, and teachers need ongoing, structured support to be able to independently apply their knowledge, rather than rely on prescribed tips and techniques. In turn, it is essential that teachers have guidance, coaching, and opportunities to collaborate over time as they learn about and critically reflect on multiple representations of engagement and discuss the variety of possibilities for knowledge use. School-wide examination of different engagement theories and a focus on promoting student engagement in the classroom, while acknowledging the important role of family, peers, and broader socio-cultural contexts, could promote advanced knowledge acquisition about student engagement without sacrificing complexity. A second implication is the need for further research to determine factors that promote teachers development of cognitive flexibility over time and ways to intentionally foster this development in school settings. In addition, the acknowledgement of the inherent simplification in student engagement models, as well as relationships among different models, could facilitate the use of engagement research in interventions for teachers. We call for a synthesis of the different ways in which student engagement is being studied and an organization of the types and purposes of various models to support the use of this research in interventions designed to promote teachers open and flexible knowledge structures regarding student engagement. CONCLUSION Across the evidence presented here, we argue that cognitive flexibility theory provides critical insight for conceptualizing how teachers understand and enact classroom strategies for engaging students. The phenomenon of being engaged with school and learning has extensive benefits for all students (Shernoff, 2013), and thus efforts to increase and enhance students experiences of engagement should be central to efforts to improve teachers practice. To this end, we acknowledge the disservice to teachers and students when teachers are presented with a list of tips or techniques for engaging students through one model, conceptualization, or tool for engagement. The tendency to simplify engagement for educational practitioners undermines the often involved and challenging work of cultivating rich, meaningful, and engaging classrooms and learning experiences for students. These experiences must be relevant to students lives and provide them with opportunities to participate in discussion, construct knowledge, and develop appreciation for the complex and multidimensional nature of learning. Future research and practice will not only benefit from, but also require, approaches to enhancing student engagement that represent the complexity of engaging students and offer a professional learning environment that promotes teachers deep learning experiences based on conceptual mastery, knowledge application, and cognitive flexibility. Notes 1. The name of the school and all participants are pseudonyms. 2. Compared with the school enrollment data, the survey option allowed students to report as mixed race, so the smaller percentage of Latino and Black respondents in the survey data are likely due to the 8% of students who self-identified as mixed race. 3. In a prior publication from this study (Cooper Stein, Kintz, & Miness, 2016), Kayla was identified by the pseudonym Tiffany. However, to prevent readers from confusing Tina and Tiffany, we have changed Tiffany to Kayla here. References Appleton, J., Christenson, S., Kim, D., & Reschly, A. (2006). Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument. Journal of School Psychology, 44, 427445. Boser, U., & Rosenthal, L. (2012). Do schools challenge our students? What student surveys tell us about the state of education in the United States. Washington, DC: Center for American Progress. Bundick, M., Quaglia, R., Corso, M., & Haywood, D. (2014). Promoting student engagement in the classroom. Teachers College Record, 116, 134. Cohen, D. K., & Ball, L. B. (1999). Instruction, capacity, and improvement (Report from the Consortium for Policy Research in Education). Philadelphia, PA: University of Pennsylvania. Cooper, K. S. (2014). Eliciting engagement in the high school classroom: A mixed-methods examination of teaching practices. American Educational Research Journal, 51(4), 363402. Cooper Stein, K., Kintz, T., & Miness, A. (2016). Reflectiveness, adaptivity, and support: How teacher agency promotes student engagement. American Journal of Education, 123(1), 109136. Dweck, C. S. (2006). Mindset: The new psychology of success. New York, NY: Random House. Education Week Research Center. (2014). Findings from a national survey: Engaging students for success. Bethesda, MD: Editorial Projects in Education. Erikson, E. H. (1968). Identity: Youth and crisis. New York, NY: W. W. Norton & Company. Feltovich, P. J., Coulson, R. L., & Spiro, R. J. (2001, December). Learners (mis)understanding of important and difficult concepts: A challenge to smart machines in education. In K. D. Forbus & P. J. Feltovich (Eds.), Smart machines in education (pp. 349375). Cambridge, MA: MIT Press. Lawson, M., & Lawson, H. (2013). New conceptual frameworks for student engagement research, policy, and practice. Review of Educational Research, 83, 432479. Martin, A. J., & Dowson, M. (2009). Interpersonal relationships, motivation, engagement, and achievement: Yields for theory, current issues, and educational practice. Review of Educational Research, 79(1), 327365. Nakkula, M. (2003). Identity and possibility: Adolescent development and the potential of schools. In M. Sadowski (Ed.), Adolescents at school: Perspectives on youth, identity, and education (pp. 718). Cambridge, MA: Harvard Education Press. National Center for School Engagement. (2006). Quantifying school engagement (Research report). Denver, CO: Author. Piaget, J. (2000). Piagets theory of cognitive development. In K. Lee (Ed.), Childhood cognitive development: The essential readings (pp. 3347). Malden, MA: Blackwell. Sawyer, R. K. (2006). Introduction: The new science of learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 116). New York, NY: Cambridge University Press. Schlechty, P. C. (2002). Working on the work: An action plan for teachers, principals, and superintendents. San Francisco, CA: Jossey-Bass. Shernoff, D. J. (2013). Optimal learning environments to promote student engagement. New York, NY: Springer. Skinner, E., Furrer, C., Marchand, G., & Kindermann, T. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Educational Psychology, 100(4), 765781. Spiro, R. J., Collins, B. P., & Ramchandran, A. R. (2007a). Modes of openness and flexibility in cognitive flexibility hypertext learning environments. In B. H. Khan (Ed.), Flexible learning in an information society. Hershey, PA: Information Science Publishing. Spiro, R. J., Collins, B. P., & Ramchandran, A. R. (2007b). Reflections on a post-Gutenberg epistemology for video use in ill-structured domains: Fostering complex learning and cognitive flexibility. In R. Goldman, R. Pea, B. Barron, & S. J. Derry (Eds.), Video research in the learning sciences (pp. 93100). New York, NY: Routledge. Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. K. (1988). Cognitive flexibility theory: Advanced acquisition in ill-structured domains (Technical Report No. 441). Champaign, IL: Center for the Study of Reading. Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. K. (2004). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains. In R. B. Ruddell & N. J. Unrau (Eds.), Theoretical models and processes of reading, 5th edition (pp. 640653). Newark, DE: International Reading Association. [Reprinted from Proceedings of the 10th Annual Conference of the Cognitive Science Society. (1988). Hillsdale, NJ: Lawrence Erlbaum.] Spiro, R. J., & Jehng, J. (1990). Cognitive flexibility and hypertext: Theory and technology for the nonlinear and multidimensional traversal of complex subject matter. In D. Nix & R. Spiro (Eds.), Cognition, education, and multimedia: Exploring ideas in high technology (pp. 163205). Hillsdale, NJ: Lawrence Erlbaum. Wei, R. C., Darling-Hammond, L., Andree, A., Richardson, N., & Orphanos, S. (2009). Professional learning in the learning profession. Dallas, TX: National Staff Development Council.
|
|
|
|
   |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
||||||
![[39_22160.htm_g/00002.jpg]](/Html/39_22160.htm_g/00002.jpg)
Executive Summary
Full Text in PDF Format
Email this article
Print this article
Post a Comment
