Individual Differences and Learning Contexts: A Self-Regulated Learning Perspective
by Adar Ben-Eliyahu - 2017
This article examines how individual differences (giftedness) interact with learning contexts (favorite versus least favorite courses) to influence learning processes and outcomes. The findings show that gifted and typically developing students differ solely in their expectancies for success and grades among a large variety of measures, including motivation (goal orientations, expectancies, and values) and self-regulated learning (self-regulated emotions, behaviors, and cognitions). These results imply that the learning context can override individual differences. Through the lens of the integrated self-regulated learning model (iSRL; Ben-Eliyahu & Bernacki, 2015), the article discusses why there are contextual differences in learning. By bridging the literature on mastery goal structure and self-determination theory, it is proposed that learning contexts focused on development and self-progress (i.e., mastery goal structured contexts) lead to adaptive achievement outcomes because competing basic needs are satisfied, competition decreases, and resources for learning are freed. Given the importance of self-regulated learning, students should be encouraged to develop learning habits and strategies based on self-regulation, which should be considered a 21st-century skill that can be scaffolded by educators in formal and informal learning settings.
In the 21st century, information literacy has become a critical skill, in addition to the more traditional skills of critical thinking and problem solving (Boyaci & Atalay, 2016). Information literacy includes knowledge about where and how to obtain knowledge as an individual and how to disseminate this knowledge through groupwork or communication technologies (Dede, 2009). The gifted population is considered exceptional at retaining information as well as being analytically advanced in thinking about how to obtain information and use it for problem solving. Highly intelligent students, or gifted youth, are defined as such by their performance at or above two standard deviations on IQ or on academic achievement tests (e.g., SAT or ACT). In this sense, gifted youth exceed typical development. However, gifted students are not necessarily more motivated to learn, nor are they more regulated than typically developing students, especially when considering typical learning contexts. Comparable with typically developing students, empirical evidence points to a range of motivations for gifted learners (Alexander & Schnick, 2008; McNabb, 2003). In fact, gifted youth can, and do, underachieve (Reis & McCoach, 2000; Snyder & Linnenbrink-Garcia, 2013). Moreover, although there is a notion that gifted youth are motivated and regulated enough to complete high school (Dai, Moon, & Feldhusen, 1998), dropout rates and reasons for dropout are similar for gifted and typical students (Landis & Reschly, 2013).
Many times, highly talented or gifted students attend similar academic institutions as do typically developing students, as in the study presented in this article. Gifted students in such contexts tend to have greater self-efficacy because they perceive themselves to be more adept academically than their typically developing counterparts (Marsh, Chessor, Craven, & Roche, 1995). However, considering other forms of motivation for gifted youth in typical learning contexts, there is no real reason why gifted youth should be differently motivated or regulated than typical learners. This helps account for the development of in-school and summer programs to serve gifted youth (e.g., Putallaz, Baldwin, & Selph, 2005).
After defining self-regulated learning and achievement motivation, this article describes a study investigating whether gifted youth are more motivated and self-regulated than typically developing youth in two at-school learning contexts: favorite and least favorite courses. This is followed by a consideration of how structuring the learning environment to support development and learning (mastery goal structure; Ames, 1992) satiates basic human needs (Ryan & Deci, 2000) and, as a result, frees up regulatory resources for self-regulated learning. Finally, I draw on the lens of the integrated self-regulated learning model (iSRL; Ben-Eliyahu & Bernacki, 2015) to discuss how different contextual components draw on self-regulated learning resources.
A common tenet of self-regulated learning models is the presence of a feedback loop in the monitoring and change/control cycle (Ben-Eliyahu & Linnenbrink-Garcia, 2015; Pintrich, 2004; Winne & Hadwin, 1998; Zimmerman, 2006). Contemporary models concur that self-regulated learning comprises affective, behavioral, and cognitive components, though the operationalization of these components differs across models and research programs (Ben-Eliyahu & Bernacki, 2015; Ben-Eliyahu & Linnenbrink-Garcia, 2015; Efklides, 2011; Harley, Bouchet, Hussain, Azevedo, & Calvo, 2015). A vital component of self-regulated learning is metacognition, or the ability to think about, observe, and refine cognitions (Winne & Hadwin, 1998). Recently, in iSRL, we identified the role played by metaemotion and metabehavior, alongside metacognition (Ben-Eliyahu & Bernacki, 2015; Ben-Eliyahu & Linnenbrink-Garcia, 2015). That is, students know how to actively adjust their emotions (metaemotions) and behavior (metabehavior) to optimize learning, similar to the way they monitor and calibrate cognitions in learning.
Self-regulated emotions refer to monitoring and controlling ones emotions during learning. For example, if a learner feels frustrated while writing a paper, he or she could think about the situation differentlyas a learning process or as an achievable goalin order to alleviate negative feelings and feel more at ease about writing. Thinking about the situation in a different light is known as reappraisal (Gross & John, 2003). Another strategy to regulate emotions during learning is to eschew expressing them through suppression. A third form of highly studied emotion regulation in the clinical realm is that of rumination or overthinking about the situation (Nolen-Hoeksema, Morrow, & Fredrickson, 1993). These three forms of self-regulated emotions (reappraisal, suppression, and rumination) comprise strategies that shape the individuals emotions, and hence regulate emotions. Metaemotion refers to the knowledge of how these emotional processes work.
In recent work on self-regulated emotions, we investigated how emotion regulation is related to emotions across emotionally charged learning contexts (Ben-Eliyahu & Linnenbrink-Garcia, 2013, 2015). Specifically, we contrasted the processes in courses that students love or favor with those that students dislike. This line of research, which focuses on the effects of course preference on learning, is surprisingly underdeveloped, despite the belief that students learn more and achieve more in courses and subjects that they like. Our reasoning was that learning is the same as any other form of consumption: Would you ever ask for a second serving of a dish you did not like? Within the social psychology literature on persuasion, liking is one of the principles of persuasion and is central to openness: People prefer to say yes to those they like (Cialdini, 2001, p. 78). Similarly, students prefer saying yes to learning and learning materials in courses they like.
By focusing on liking within formal learning settings, we found that students harness self-regulated learning to a greater extent in their favorite courses than their least favorite courses (Ben-Eliyahu & Linnenbrink-Garcia, 2013, 2015). Furthermore, suppression was related differently to emotions across favorite and least favorite courses (Ben-Eliyahu & Linnenbrink-Garcia, 2013), whereas learning context did not influence the relationship between reappraisal and rumination to emotions. In favored courses, suppression was negatively related to positive emotions, replicating work that conceptualizes emotion regulation as a trait (e.g., Gross & John, 2003). However, in least favorite courses, suppression was positively related to positive emotions. This finding diverges from previous work in which suppression was measured as a trait, and suggests that self-regulated emotion is context specific and requires further research. There is scant work on emotional regulation and giftedness. Prior work on gifted students used the Rorschach test to measure emotional adjustment (Gair, 1944). This study found that gifted students were better emotionally adjusted and had more mature personalities than did typically developing peers. Based on this finding, gifted youth should be especially facile in regulating their emotions in disliked courses.
Self-regulated behavior comprises strategies aimed at controlling and monitoring ones behavior (Pintrich, Smith, Garcia, & McKeachie, 1991; Zimmerman, 2000). Planning when and where to be and having concrete plans for when to do what are rudimentary for determining actions and behaviors. A persons ability to choose a place conducive to learning and change it if it ceases to be so is referred to as environmental regulation (Zimmerman, 2006). Because planning and environmental regulation signify knowledge about how behaviors are regulated, they comprise metabehaviors.
Self-regulated cognition has been studied for over three decades (Corno & Mandinach, 1983). Although initially focused on levels of cognitive engagement, over the years, self-regulated cognition has come to include a wide range of processes and strategies aimed at controlling learning and information processing (Winne & Hadwin, 1998). One of the most highly studied forms of self-regulated cognition is metacognition, which refers to ones knowledge of cognitive processes and the ability to adjust and use them as necessary (Pintrich et al., 1991). A less studied form of self-regulated cognition, though important, is ones focus or monitoring and actively drawing attention toward the task at hand (Ben-Eliyahu & Linnenbrink-Garcia, 2015; Rueda, Posner, & Rothbart, 2004).
Finally, self-control refers to a general capacity to withhold gratification and take charge of oneself (Tangney, Baumeister, & Boone, 2004). This general form of self-regulation seems especially pertinent when studying competes with more attractive distractions, such as social media or watching a movie. In earlier work, self-control was found to be related to a higher grade point average, higher self-esteem, less binge eating and alcohol abuse, better relationships and personal skills, and more secure attachment (Muraven & Baumeister, 2000).
More specific forms of self-regulated learning strategies include deep and surface learning strategies such as elaboration and rehearsal, respectively, and organization (Pintrich et al., 1991). Elaboration refers to connecting new material with familiar material and linking these across learning activities and topics. Rehearsal is the repetition of material in order to learn it. Organization is the process of summarizing, highlighting, and structuring learning materials and content to facilitate learning.
There has been a recent surge in studies on a wide array of motivations from both achievement goal theory and expectancy-value model (e.g., Conley, 2012; Plante, OKeefe, & Théorêt, 2012). Within achievement goal theory (Dweck & Leggett, 1988; Elliot, 1999), two main reasons for learning, or goal orientations, have been studied: Students either adopt goals focused on learning and developing (mastery goal orientations) or focus on outshining and besting others (performance goal orientations) (Ames, 1992; Pintrich, 2000; see Anderman & Wolters, 2006; Urdan, 1997, for reviews). Students can approach or avoid these goals. Specifically, according to the trichotomous framework for achievement goals, students focus on developing and learning (mastery goal orientation), focus on normatively outperforming others (performance-approach goal orientation), or focus on avoiding failure or not appearing incompetent (performance-avoidance goal orientation) (Elliot, 1999; Elliot & Harackiewicz, 1996).
Mastery goal orientations are considered adaptive because they are positively associated with engagement, effort, persistence, academic achievement, and positive affect (Grant & Dweck, 2003; Huang, 2011; Hulleman, Schrager, Bodmann, & Harackiewicz, 2010; Liem, Lau, & Nie, 2008; Linnenbrink-Garcia, Tyson, & Patall, 2008; Pekrun, 2006; Pekrun et al., 2009; Vrugt & Oort, 2008). Performance-approach goal orientations are associated with both adaptive outcomes, such as achievement and positive emotions (Elliot & Church, 1997; Elliot & McGregor, 2001; Harackiewicz, Barron, Carter, Lehto, & Elliot, 1997), and maladaptive outcomes, such as negative emotions (Pekrun, 2006; Pekrun, Elliot, & Maier, 2009; Roeser, Midgley, & Urdan, 1996). Performance-avoidance goal orientations are more consistently related to maladaptive outcomes, such as anxiety and avoidance of help seeking (Pekrun, 2006; Pekrun et al., 2009).
The expectancy-value model (Wigfield & Eccles, 1992, 2000) offers an additional approach to motivation. Expectancies for success refers to an individuals self-perception of future prospects and the potential to succeed on a future task or subject-matter area. Students differ in their expectancies to successfully acquire knowledge, perform skills, master the material, and so forth. Positive self-perceptions, such as expectancies for success and self-efficacy, are robustly related to achievement outcomes, such as achievement, persistence, task completion, and strategy use (Eccles, 1983; Schunk, 1989, 1991; Wigfield & Eccles, 2000).
Expectancies for success may be somewhat complex for gifted youth. Although gifted youth have the ability to achieve, not all gifted youth manage to reach their full potential (Reis & McCoach, 2000). Underachievement among gifted youth may result from a lowered academic self-concept when placed in ability groups where they experience their giftedness as a lesser value, a phenomenon known as the Big Fish Little Pond effect (Marsh et al., 1995). When placed in ability groupings, learners comparison with peers of similar abilities can diminish self-concept. Recently, Snyder and Linnenbrink-Garcia (2013) proposed that when high self-concept is coupled with caring adults high expectations or noncontingent praise for ability and when success is attributed to a gifted label, underachievement is more likely because of increased academic challenge. Thus, even gifted youths successful prior experiences may not lead to heightened expectancies for success.
The three task values in the expectancy-value model pertain to an internal positive valuing of the task (intrinsic value), identification with the task (attainment value), and valuing of the usefulness of the task (utility value) (Eccles, 1983; Wigfield & Eccles, 2000). Values predict intentions and actual decisions to take a course (Eccles, 1984a, 1984b; Eccles, Adler, & Meece, 1984; Wigfield & Eccles, 1992). As with typical students, Snyder and Linnenbrink-Garcia (2013) proposed that low task value beliefs in gifted students will lead to underachievement. However, there do not seem to be any empirical findings or hypotheses differentiating task value levels between gifted and typically developing students.
It is now well documented that students learning contexts influence learning (Patrick, Kaplan, & Ryan, 2011; Pintrich, 2004). Much of this work has focused on aspects related to the achievement goal supported through classroom practices (i.e., the classroom goal structure, as outlined in Ames, 1992). Until recently, the extent to which students like a course has been neglected in the literature (see Ben-Eliyahu & Linnenbrink-Garcia, 2013, 2015), although studies of emotions clearly point to differences in psychological processes based on negative or positive valence (Fredrickson, 1998; Tice, Baumeister, & Zhang, 2008).
According to the broaden-and-build model, positively valenced stimuli generate a growth pattern, whereby individuals broaden their perspective and build their repertoire (Fredrickson, 1998). This contrasts with negative emotions that lead to breakdowns in behavioral regulation and cognitive processing, even on the simplest of tasks (e.g., solving a puzzle) (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Tice et al., 2008). The breakdown occurs through the process of ego depletion. Ego depletion is the exhaustion of self-regulatory capacities in the wake of a previous task. In classic ego-depletion studies, an activity requiring self-regulation is introduced, such as not eating cookies or radishes. After this activity, participants perform a task, such as solving geometry problems. Because of the limited capacity of regulation, peoples regulatory resources are depleted (Muraven & Baumeister, 2000) and they need to rest and replenish regulatory resources so that regulation can reoccur.
One reason that negative emotions are more depleting than positive emotions is that when faced with negative stimuli, people are likely to enter a fight or flight response, thus depleting their regulatory capacities and causing narrowed thinking and focus. It is thus reasonable to assume that negative and positive emotions triggered by the learning context have differential effects on learning. Negatively valenced learning environments are detrimental and more difficult for learners and, as a result, threaten students feeling of competence and autonomy. In contrast, positively valenced learning environments broaden learners scope by shaping deeper, more intentional learning. In recent work on course preference, we found that within positively or negatively valenced learning contexts (favorite versus least favorite courses), self-regulated learning functions somewhat differently (e.g., Ben-Eliyahu & Linnenbrink-Garcia, 2013, 2015).
Gifted students, who are typically more analytical and sophisticated thinkers, may be better equipped to cope with diverse learning contexts (Neihart, 1999) because they should be relatively more metacognitive, metaemotional, and metabehavioral. Hence, gifted youth should be better able to regulate and motivate themselves in disliked learning contexts than would typical students. Stemming from this, gifted youth should report higher levels of self-regulated learning and motivation compared with typically developing studentsunless the context is more influential than individual differences such as intelligence, in which case such differences would not be found.
COMPARING GIFTED AND TYPICALLY DEVELOPING STUDENTS
The studys main hypothesis was that there would be no mean differences in self-regulated learning and motivation between gifted and typical learners in the same learning contexts. The study used a data set described in Ben-Eliyahu and Linnenbrink-Garcia (2015), comprising 178 high school and 277 undergraduate students from the southeastern United States. In that study, we found differences in self-regulated learning based on learning context. Here, I examined differences between gifted and nongifted students, using the full breadth of this data set, including a broader array of self-regulated learning and motivational measures.1 Specifically, eight forms of self-regulated learning strategies, seven forms of motivation, three learning strategies, behavioral-cognitive engagement, and grades were investigated for the high school and college student participants in their favorite and least favorite courses.
Measures of self-regulated emotions included reappraisal, suppression, and rumination. Environment regulation and planning were used to measure self-regulated behaviors. Self-regulated cognition comprised metacognition and focus. A more general form of self-regulation was defined as self-control. Organization, surface processing and deep processing (Pintrich et al., 1991), and cognitive-behavioral engagement (Assor, Kaplan, & Roth, 2002) were used to assess learning strategies. To measure motivation, the three forms of goal orientations were evaluated: mastery, performance-approach, and performance-avoidance. Expectancies for success were assessed by asking how likely the participants thought it was that they would succeed in their class. Three forms of values were measured: interest/intrinsic value, attainment value, and utility value. Reliabilities ranged from .75 < a < .93.
Achievement was reported by the participants at the end of the semester for the college students and derived from school records for the high school students. Giftedness was a self-report measure comprising a single question2: Have you ever been identified as gifted? Among all participants, 304 self-identified as gifted (74% of the high school students and 69% of the college students), 123 did not, and 31 left the question unanswered. Self-reports of giftedness have been used in previous studies (Snyder, Barger, Wormington, Schwartz-Bloom, & Linnenbrink-Garcia, 2013). The high rate of giftedness is not surprising in this sample, given that the college students were from an elite university from the southeast United States, and the high school students were identified as college-bound within their schools and were taking Advanced Placement classes.
Gifted and typical youth were compared on all the measures using a general linear model repeated-measures multivariate analysis of variance (MANOVA). Because of gender distribution differences across the gifted/typical samples, gender was also examined.3 Class type (favorite/least favorite) was entered as a within-person factor, and gender, giftedness, and school level (high school/college) as between-person factors. This made it possible to account for the multiple analyses and shared variance. In the MANOVA,4 all main effects emerged significant: giftedness (F = 2.41, p = .001), gender (F = 1.88, p = .014), school level (F = 9.50, p < .001), and class liking (F = 45.06, p < .001). The interaction between giftedness and school level was statistically significant (F = 1.72, p = .030), whereas the other interactions with giftedness were not significant. Previous work (Ben-Eliyahu & Linnenbrink-Garcia, 2015) on this data set also found a significant interaction between course liking and school level on most of these measures.
The main research question was whether there would be empirical evidence for differences between gifted and typically developing youth on motivation and self-regulated learning in typical formal schooling courses. When looking at giftedness, except for expectancies for success (F = 25.74, p < .001) and grades (F = 11.07, p = .001), none of the measures was significant, with p values ranging from .10 to .89 (reappraisal, suppression, rumination, metacognition, focus, planning, environmental regulation, self-control, goal orientations, task values, organization, rehearsal, elaboration, and engagement). Participants who identified themselves as gifted reported higher expectancies for success (M = 3.73, SD = .66) than typically developing students (M = 3.47, SD = .56) and received higher grades at the end of the semester (Mgifted = 3.33, SDgifted = .72; Mtypical = 3.13, SDtypical = .80). Thus, gifted students evaluated themselves as more likely to succeed and earned higher grades than typically developing students. Although not a focus of this study, gender differences were found for mastery goal orientations (F = 5.80, p = .017), environmental regulation (F = 4.23, p = .040), planning (F = 16.76, p < .001), metacognition (F = 11.16, p = .001), surface processing (F = 15.19, p < .001), and organization (F = 14.91, p < .001) (see Figure 1). In summary, contrary to the notion that gifted youth are likely to be more analytical and therefore more regulated, no differences were found between gifted and typically developing students across contexts.
Figure 1. Differences between males and females in self-reported motivation and self-regulated learning; y-axis indicates the extent to which students reported motivated and self-regulated on a 5-point Likert-type scale, ranging from 1 (not at all) to 5 (very much)
UNDERSTANDING THE INDIVIDUALCONTEXT RELATIONSHIP: A SELF-REGULATED LEARNING PERSPECTIVE
The lack of differences between gifted and typically developing students can be understood through the perspective that the learning context overrides individual differences, especially in a typical setting where classroom dynamics are complex. At the higher end of the spectrum, gifted students are intellectually advanced and have high processing speeds. At the other end of the spectrum, weaker students take longer to understand concepts and may need information repeated in different ways. For the interaction between the student and learning context to be beneficial, teachers can make intentional decisions about the individualcontext relationship (Gestsdottir, Bowers, von Eye, Napolitano, & Lerner, 2010). Based on the above, I argue that a mastery structured classroom promotes the use of self-regulated learning by satisfying three basic needs. This reasoning draws on the integrated self-regulated learning model (iSRL: Ben-Eliyahu & Bernacki, 2015) to capture this mechanism.
The tremendous complexity of teaching a classroom comprising such a variety of students undeniably results in some students being bored and unchallenged, because the material is either too hard or too easy. Classroom practices employed by teachers influence the quality and strength of motivation and self-regulation that students adopt. Ames (1992) defined task, type of authority, and evaluation/recognition that teachers use as the three main classroom structure components that shape instructional strategies and motivational patterns. According to Ames (1992), to scaffold mastery goal orientations (mastery classroom structure), tasks should deal with meaningful aspects of learning, as well as be novel, be challenging, and support the development of effective learning strategies. Authority should provide students with choices, allowing them to participate in the decision-making process through opportunities to develop responsibility and independence while promoting the use of self-management and monitoring skills. Evaluation/recognition should focus on individual improvement and progress through private evaluation and recognition of the students effort by providing opportunities for improvement and construing mistakes as learning moments.
In our work on mastery classroom goal structure, we found that gifted youth attending a mastery structured summer program reported higher levels of personal mastery goal orientations; however, these decreased to previous levels just 6 months after the summer program, when students were back in their typical classrooms (OKeefe, Ben-Eliyahu, & Linnenbrink-Garcia, 2013). The same effect was found for self-worth contingent on others evaluations. That is, for gifted youth, just like typically developing students, participating in mastery-structured learning environments was found beneficial for their self-worth and attenuated the comparison with others (performance-approach and performance-avoidance goal orientations).
Support for the influence of mastery goal structure in both the academic and social domains comes from a long line of work (Eccles, Wigfield, Harold, & Blumenfeld, 1993; Kaplan, 2004; Lazarus, 1993; Patrick, 1997; Patrick et al., 2011). In our recent work investigating elementary school students concurrent changes in academic and social coping and mastery structured classrooms, we found that a decrease in mastery goal structure from third through sixth grades was concurrent with a decrease in academic positive coping (similar to reappraisal) and an increase in projective academic coping (blaming others) (Ben-Eliyahu & Kaplan, 2015). These parallel trajectories point to the possible role of learning context in shaping the coping strategies used by students. Because mastery structured learning contexts allow students to focus on their self-improvement, they scaffold feelings of competence, a basic need that drives human behaviors (Ryan & Deci, 2000).
Another basic need fulfilled by a mastery structured classroom is that of autonomy (Ryan & Deci, 2000). Autonomy (or authority) is supported through choices and challenges presented in diverse tasks. Students autonomy was found to be related to maintained persistence, deeper information processing, performance, a higher quality of behavioral engagement, and greater well-being (e.g., Black & Deci, 2000; Williams, McGregor, Zeldman, Freedman, & Deci, 2004).
Finally, a third basic need fulfilled through mastery structured classrooms is that of relatedness (Ryan & Deci, 2000). Patrick (2004) suggested that students evaluate the emphasis on mastery goal structure through the teachers promotion of real understanding and personal improvement, support and confidence in the students learning, and the perception of their teachers messages about interpersonal relationships (e.g., support, respect, helping one another). Students identify teacher support of a mastery goal structure as pivotal in promoting student interaction so that a mastery goal structure and the classroom social climate converge (Patrick et al., 2011; Stornes, Bru, & Idsoe, 2008).
To understand how self-regulated learning is supported through a mastery goal structured classroom, I propose a self-regulated learning perspective through the lens of the integrated self-regulated learning model (iSRL: Ben-Eliyahu & Bernacki, 2015; see Figure 2). Specifically, a mastery goal structure fulfills the needs for competence, autonomy, and relatedness (Ryan & Deci, 2000). Satiating such needs through a mastery structured classroom frees up resources that would be otherwise allocated for need-based regulation. For example, feeling rejected or disconnected (unsatiated need for relatedness) would give rise to negative emotions. These negative emotions would be regulated, thereby drawing regulatory capacities away from self-regulated learning. When a mastery structured classroom satisfies the need for relatedness (or any other need), resources become available for more self-regulated learning, which in turn leads to deeper learning and greater engagement.
Figure 2. The integrated self-regulated learning model (iSRL), reprinted from Ben-Eliyahu and Bernacki (2015)
There are two main postulates to iSRL: self-regulation is (1) context specific and (2) a limited resource. The first postulate, that self-regulation is context-specific, refers to the classroom as the proximal environment in which students learn. Human development is a product of natural variation in different life contexts, such as neighborhood, family, school, and peer groups (the bioecological model; Bronfenbrenner & Ceci, 1994). Learning can and does occur in all these contexts, although most research on self-regulated learning has focused on formal learning contexts and studying (Winne & Hadwin, 1998). Nonacademic situations such as social relationships and family wealth influence academic processes through the allocation of resources, opportunity, and interactions. In this way, there is a spillover effect, whereby influences from one contextual system (i.e., a macrosystem or exosystem) percolate into another through a mesosystemic effect. A good example of how these interrelations influence learning is that of breakfast. Children who live in an unstable home or in low-income homes lacking a regular supply of food may arrive to school without having eaten breakfast and are therefore hungry and consequently less available to learn. The physiological need for nutrition preoccupies students cognitions, behavior, and emotions, so that it depletes regulatory resources via an ego-depletion mechanism and results in decreased self-regulated learning. By recognizing that regulating hunger depletes self-regulated learning, the state of North Carolina has instituted a No Kid Hungry policy so that children are served a free breakfast.
The second postulate of iSRL is that self-regulated learning is a limited resource; when expended, it is depleted and needs to be replenished. The more students need to regulate aspects not related to the taskwhether academic or nonacademicthe fewer resources are available to regulate academic issues. For example, when preoccupied with social rejection from a classmate (need for relatedness), a student may be unable to focus on the math problem on the board. Coping with a social situation through emotional reappraisal, rumination, or behavioral regulation draws resources away from self-regulation related to the in-class task.
Similarly, students preoccupied with how they are faring compared with others (performance goal orientation) are busy with a number of impression-management personas (Leary & Allen, 2011). Students focus on appearing competent drains resources from their learning. Performance-goal orientations therefore deplete resources from deeper learning because of this multiple comparison with others as students seek to avoid threatening their competence, a basic need. In contrast, for a mastery-oriented student using oneself as a referent, the point of comparison is with oneself, thus decreasing the need to manage multiple personas. In this way, the characteristics of a mastery structured classroom decrease competition between components that require self-regulation, thus making resources available for self-regulated learning.
Students in school who are occupied with fulfilling their basic needs in any contextlearning, school, home, neighborhoodare using up regulatory resources that would otherwise be allotted to learning. A mastery structured classroom does not threaten these needs that drive human behavior. For example, employing grouping and collaborative work fulfills the need for relatedness, thus freeing up resources for learning. Decreasing competition also frees up resources for more self-regulated learning because students do not feel that their competence is threatened, and they can be intentionally involved in deeper processing.
IMPLICATIONS FOR THE 21ST-CENTURY EDUCATOR AND STUDENT
One of the aims of this NSSE Yearbook is to consider how SRL corresponds to 21st-century skills in diverse educational contexts. The goal of this chapter was to provide an example of the potency of the educational context beyond individual differences such as intelligence. Although regulation has not been considered a 21st-century skill, perhaps it should be. Providing students with knowledge on how to self-regulate on and off academic tasks has great advantages. Beyond the mastery structured classroom, a specific regulatory curriculum and activities can educate students about the feedback loop between awareness/monitoring and control/change, which is at the heart of regulation. Providing students with opportunities to practice self-regulation within schools would allow them to develop and strengthen this important skill, which, like a muscle, can be strengthened with practice.
I would like to thank Kate Snyder for her comments on an earlier version of this article.
1. Because of space limitations, measures could not be included in this article; however, all measures will be supplied on request.
2. The measure of giftedness was an answer to the question, Have you ever been identified as gifted? and has some limitations, although it has been used in previous studies (Snyder et al., 2013). More work needs to be done on different criteria for giftedness, such as school or parent nominations, because some students may not know whether they are really gifted, talented, or excellent.
3. Pearson chi-square analysis found no differences in the number of participants who responded yes to being identified as gifted across the high school (74%) and college (69%) samples, χ² (1) = 1.20, p = .318, and participants did not differ in their ethnicity by giftedness, χ² (7), 8.98, p = .254. There was a significant difference between female participants who identified themselves as gifted (76%) versus male participants who identified themselves as gifted (67%), χ² (1) = 3.84, p = .05.
4. Degrees of freedom for all main effects and interactions: (20, 310).
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