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Development Aspects Of Self Regulated Learning Essay

Zimmerman: A Socio-cognitive Perspective of SRL Grounded by Three Models

Zimmerman was one of the first SRL authors (e.g., Zimmerman, 1986). He has developed three different SRL models, being the first one published in 1989 representing what was the first attempt to explain the interactions that influence SRL.

History and Development of the Models

Zimmerman (2013) reviewed his career and the development of his work, framing it into the socio-cognitive theory (i.e., individuals acquire knowledge by observing others and social interaction). Zimmerman’s work started from cognitive modeling research in collaboration with Albert Bandura and Ted L. Rosenthal. Later Zimmerman began to explore how individual learners acquire those cognitive models and become experts in different tasks.

As one of the most prolific SRL writers, Zimmerman has developed three models of SRL (Panadero and Alonso-Tapia, 2014). The first model (Figure ​1), known as the Triadic Analysis of SRL, represents the interactions of three forms of SRL: environment, behavior and person level (Zimmerman, 1989). This model describes how SRL could be envisioned within Bandura’s triadic model of social-cognition. The second model (Figure ​2) represents the Cyclical Phases of SRL, which explains at the individual level the interrelation of metacognitive and motivational processes. This model was presented in a chapter in the 2000 handbook, and it is usually known as Zimmerman’s model. There the subprocesses that belong to each phase were presented, but it was not until 2003 that these subprocesses were embedded in the figure (Zimmerman and Campillo, 2003). Finally, in Zimmerman and Moylan (2009) the model underwent some tweaks (Figure ​3), including new metacognitive and volitional strategies in the performance phase. The third model Zimmerman developed (Figure ​4), which recently has been called the Multi-Level model, represents the four stages in which students acquire their self-regulatory competency (Zimmerman, 2000). In this review, Cyclical Phases model will be analyzed, as it describes the SRL process at the same level as the models from the other authors analyzed here.

Zimmerman’s Cyclical Phases Model

Zimmerman’s (2000) SRL model is organized in three phases: forethought, performance and self-reflection (see Figure ​3). In the forethought phase, the students analyze the task, set goals, plan how to reach them and a number of motivational beliefs energies the process and influence the activation of learning strategies. In the performance phase, the students actually execute the task, while they monitor how they are progressing, and use a number of self-control strategies to keep themselves cognitively engaged and motivated to finish the task. Finally, in the self-reflection phase, students assess how they have performed the task, making attributions about their success or failure. These attributions generate self-reactions that can positively or negatively influence how the students approach the task in later performances.

Empirical Evidence Supporting Zimmerman’s Cyclical Model

An overview of Zimmerman’s empirical evidence can be found in his career review (Zimmerman, 2013). A special feature of Zimmerman’s empirical research is the use of athletic skills, along with more typical academic skills. A number of studies have been conducted to test different aspects of Zimmerman’s models (Puustinen and Pulkkinen, 2001; Zimmerman, 2013), especially the Multi-level and the Cyclical phase models. Zimmerman conducted work with Kitsantas and Cleary that tested the Multi-level model (Zimmerman and Kitsantas, 1997, 1999, 2002; Kitsantas et al., 2000). Those four studies can be grouped in two types. First, the articles published in 1997 and 1999 studied the differential effect of outcome and process goals with high school students in two different tasks dart throwing and writing, finding support for the model. And second, the articles published in 2000 and 2002 studied the effect of observing different types of models in the development of SRL skills in dart throwing and writing.

The cyclical phase model has been tested in a series of four studies. First, Cleary and Zimmerman (2001) studied the SRL skills showed by adolescent boys who were experts, non-experts and novices in basketball, finding that experts performed more SRL actions. Second, in a similar study, Kitsantas and Zimmerman (2002) compared college women that were experts and non-experts in volleyball, finding that the SRL skills predicted 90% variance in serving skills. Third, Cleary et al. (2006) trained 50 college students in basketball free throws organized in five different conditions: one-phase SRL, two-phases SRL, three-phases SRL, control group practice-only and control group no-practice. The results showed a linear trend: the more phases trained the better the participants’ scores. Finally, fourth, DiBenedetto and Zimmerman (2010) studied 51 high school seniors during science courses seniors finding that higher achievers showed more use of subprocesses from Zimmerman’s model.

Another important piece of research into Zimmerman’s model is the work performed by Bernhard Schmidt and colleagues. As already mentioned, Schmidt has developed a SRL model based on Zimmerman’s and influenced by Kuhl’s (2000) model with changes in the names of the phases and subprocesses included (Schmitz and Wiese, 2006). This theoretical proposal gives a major emphasis to the role of self-monitoring in SRL (Schmitz et al., 2011). Additionally, Schmitz has developed significant research on how the use of learning diaries and its different data analysis known as time-series analysis. His main results have been that the use of learning diaries enhances all SRL phases being an effective way to impact in students’ SRL and performance.

Instruments and Measurement Methods

Under Zimmerman’s model umbrella, five instruments and measurements have been developed. First, the subprocesses present in Zimmerman’s model are partly based on the results found in the validation process of the Self-Regulated Learning Interview Schedule (SRLIS) (Zimmerman and Martinez-Pons, 1986, 1988). Second, Zimmerman has developed procedures to assess SRL in experimental training settings for writing and dart throwing (Zimmerman and Kitsantas, 1997, 1999). Third, Cleary and Zimmerman (2001, 2012), Kitsantas and Zimmerman (2002), DiBenedetto and Zimmerman (2010) developed microanalytic measures to assess the validity of the Cyclical Phases model. Fourth, Zimmerman has developed different measures of self-efficacy to self-regulate (Zimmerman and Kitsantas, 2005, 2007) and calibration measures of self-efficacy and self-evaluation (Zimmerman et al., 2011). And, fifth, anchored on the framework of SRL by Zimmerman and Martinez-Pons (1986, 1988), Magno (2010) developed the Academic Self-Regulation Scale (A-SRL) which has been validated analyzing its functional correlation against two well-established SRL instruments the MSLQ and the Learning and Study Strategies Inventory (LASSI) (Magno, 2011).

Boekaerts: Different Goal Roadmaps (Top–Down/Bottom–Up) and the Role of Emotions

The work by Boekaerts is also one of the earliest in the SRL literature and can be traced back to the late 1980s (e.g., Boekaerts, 1988). Shortly after she presented her first SRL model (Boekaerts, 1991). Her work has focused in explaining the role of goals (e.g., how students activate different types of goals in relation to SRL), and she was the first to use situation-specific measures to evaluate motivation and SRL. In addition, Boekaerts has demonstrated a vast knowledge of the clinical psychology literature on self-regulation and emotion regulation (see Boekaerts, 2011).

History and Development of the Models

Boekaerts has developed two models of SRL. First, she developed a structural model (Figure ​5) in which self-regulation was divided into six components, which are: (1) domain-specific knowledge and skills, (2) cognitive strategies, (3) cognitive self-regulatory strategies, (4) motivational beliefs and theory of mind, (5) motivation strategies, and (6) motivational self-regulatory strategies (Boekaerts, 1996b). These were organized around, what she then considered to be, the two basic mechanisms of SRL: cognitive and affective/motivational self-regulation. This model has been mainly used to (a) gain more insight into domain-specific components of SRL, to (b) train teachers, to (c) construct new measurement instruments for research, and to (d) design intervention programs (Boekaerts, M. personal communication to author 08/06/2014).

Second, most of Boekaerts’ publications were set up to formulate a second SRL model, namely, the Adaptable Learning Model. This model (see Figure ​6) was presented at the beginning of the 90s (Boekaerts, 1991, 1992). It describes the dynamic aspects of SRL, and later, evolved into the Dual Processing self-regulation model (Figure ​7). The Adaptable Learning Model offered a theoretical scaffold for understanding the findings from diverse psychological frameworks, including motivation, emotion, metacognition, self-concept, and learning. The model described two parallel processing modes: (a) a mastery or learning mode and (b) a coping or well-being mode. In a chapter of the 2000 Handbook of self-regulation, Boekaerts and Niemivirta (2000) presented new ideas on goal paths using different figures to visualize how they influence students’ behavior (see pp. 434–435). Although, in 2000, Boekaerts had already presented some notions on her vision of top–down and bottom–up theory, it was not until mid-2000 that these theoretical insights were clearly defined in her model, which was then renamed as the Dual Processing self-regulation model (Boekaerts and Corno, 2005; Boekaerts and Cascallar, 2006). In the 2011 SRL handbook of SR, Boekaerts presented an extended version of this model, which pointed to the different purposes of self-regulation during the learning process, namely, (1) expanding one’s knowledge and skills, (2) protecting one’s commitment to the learning activity, and (3) preventing threat and harm to the self. Boekaerts emphasized the key role that positive and negative emotions play in SRL, and described two different bottom–up strategies, namely, volitional strategies and emotion regulation strategies (Figure ​7; Boekaerts, 2011).


Model of adaptable learning. Extracted from the 2000 handbook but cited there as: the original model of adaptable learning. Adapted from Boekaerts (1996a).

Boekaerts’ Dual Processing Model

In the Dual Processing model (Boekaerts and Cascallar, 2006), the appraisals made by the students are crucial to determine which goal pathway the students will activate. Here, goals are viewed as the “knowledge structures” that guide behavior. For example, if students perceive that the task could be threatening to their well-being, negative cognitions and emotions are triggered. Strategies are then directed to protect the ego from damage, and thereby, students move onto a well-being pathway. On the other hand, if the task is congruent with the students’ goals and needs, they will be interested in amplifying their competence, triggering positive cognitions and emotions, and thereby, moving onto the mastery/growth pathway. Boekaerts (2011) also explains that students who have started a task in the mastery/growth pathway may move to the well-being pathway if they detect cues that they might not be successful.

According to Boekaerts (2011), there are three different purposes for self-regulation:

(a) expanding knowledge and skills…(b) preventing threat to the self and loss of resources so that one’s well-being is kept within reasonable bounds…and (c) protecting one’s commitments by using activities that re-route attention from the well-being pathway to the mastery pathway (pp. 410–411).

The first is what she called “top–down,” as the pursuit of task goals is driven by the students’ values, needs and personal goals (mastery/growth pathway). The second purpose is called “bottom–up,” as the strategies try to prevent the self from being damaged (well-being pathway), and students may experience a mismatch between the task goals and their personal goals. The third purpose occurs when students try to redirect their strategies from the well-being to the mastery/growth pathway, which may happen via external (e.g., teacher or peer pressure) or internal (e.g., self-consequating thoughts) forces. Therefore, emotions are essential in Boekaerts’ model, because when students experience negative emotions, they will activate the well-being pathway and use bottom–up strategies. Pursuant to this interest, Boekaerts has studied, in depth, the different emotion regulation strategies (see Boekaerts, 2011).

Empirical Evidence Supporting the Dual Processing Model

Most of the empirical support was provided by Boekaerts and her Ph.D. students using the On-line Motivation Questionnaire (OMQ) – next section for more information- and other specific measures. Their work on the Model of Adaptable Learning concentrated on the top half of the model in Figure ​7. Four main areas of research can be identified using different measurement tools. First, Seegers and Boekaerts (1993, 1996) studied different aspects of cognitive appraisals and how they determine prospective, anticipatory positive and negative emotions and learning intentions; they found gender differences in the types of appraisals activated. In another publication, Boekaerts (1999) demonstrated that these task specific indices of the students’ interpretations of the learning activity explain more of the variance in learning intention than domain measures, such as self-concept of ability, activation of mastery and performance goals, and interest in the domain.

Second, the effect of prospective cognitions and emotions on learning intention was also studied using the OMQ (Boekaerts et al., 1998; Crombach et al., 2003); a confirmatory factor analysis revealed that seven of the eight presupposed factors could be distinguished empirically, as the internal structure of the tested model was invariant over the academic tasks and also seemed stable over a half-year period.

Third, gender differences in prospective cognitions and emotions were studied using the OMQ and the Confidence and Doubt scale -which measures students’ feelings of confidence every 40 s while they are performing word problems-, (Boekaerts, 1994; Boekaerts et al., 1995; Vermeer et al., 2001). It was found that boys and girls attend differently to math problems, especially word problems. Boys expressed higher confidence, more liking for the tasks, more positive emotions and more willingness to invest effort than girls. Vermeer et al. (2001) using the Confidence and Doubt scale, led to the conclusion that girls view solving math problems basically as applying mathematical rules.

Fourth, several interventions in Dutch secondary vocational schools were conducted that focused on building up metacognitive knowledge and creating opportunities to use deep-level processing (Rozendaal et al., 2003; Boekaerts and Rozendaal, 2006). It was found that the intervention worked best for students who were already familiar with (and used) deep-level processing strategies at the beginning of the study.

Boekaerts has also conducted research on the Dual Processing model and the factors that determine students’ outcome assessments, their reported effort after a task, and their attributions (bottom part of the model). There are two main lines of research here. First, using structural equation models, Boekaerts (2007) looked more closely at the effect of competence and value appraisals on the students’ outcome assessments and reported effort; she also explored the influence that positive and negative emotions during a task have on these outcome variables. She found that students who reported that they had invested effort after doing their mathematics homework, had initially reported that they were competent to do their homework tasks, which produced positive emotions during the task. Valuing a task initially also substantially increased the reported effort. In further research (Boekaerts et al., 2003; Boekaerts, 2007), it was found that outcome assessments after doing homework were positively influenced by both competence and value appraisals. The second line of work, using Neural Network Methodology (family of statistical learning models inspired by the central nervous systems of animals, more specifically biological neural networks) it was examined whether the quality of students’ writing performance (poor/mid/high performance group) could be predicted on the basis of characteristics of the SR system (measured with a specially designed software program based on the OMQ) (Cascallar et al., 2006; Boekaerts and Rozendaal, 2007). It was found that neural networks could predict with high accuracy (ranging 94 and 100%) which students would be in the poor, mid, or high performance groups, based on 56 predictors.

Instruments and Measurement Methods

Boekaerts has written a number of reflection papers about the measurement of SRL (the most known Boekaerts and Corno, 2005), and has participated in the creation of four instruments and assessment methods. First, she developed the OMQ (Boekaerts, 1999), which measures the “sensitivity to learn in concrete situations.” It is composed of two parts: (a) students self-report their feelings, thoughts and the effort they want to expend on a concrete task, and (b) after the task, the students report how they feel and their attributions. The validation of her SRL model with the OMQ can be found in Boekaerts (2002). Second, she created an instructional design for secondary vocational schools in the Netherlands based on SRL principles that was called the Interactive Learning Group System (ILGS) innovation (Boekaerts, 1997; Boekaerts and Minnaert, 2003). Third, Boekaerts developed an instrument to record student motivation: the Confidence and Doubt Scale (Vermeer et al., 2001) – explained earlier. And, fourth, she has collaborated with other scholars in the implementation of neural networks for SRL finding high predictive power in such models (e.g., Cascallar et al., 2006).

Winne and Hadwin: Exploring SRL from a Metacognitive Perspective

Winne and Hadwin’s model of SRL has a strong metacognitive perspective that recognizes self-regulated students as active and managing their own learning via monitoring and the use of, mainly, (meta)cognitive strategies (Winne, 1995, 1996, 1997; Winne and Hadwin, 1998) while asserting the goal driven nature of SRL and the effects of self-regulatory actions on motivation (Winne and Hadwin, 2008). It has been a widely used model, especially in research implementing computer supported learning settings (Panadero et al., 2015b).

History and Development of the Model

Winne and Hadwin’s model is strongly influenced by the Information Processing Theory (Winne, 2001; Greene and Azevedo, 2007), exploring the cognitive and metacognitive aspects of SRL in more detail than the other SRL models with the exception of Efklides’. Some of Phil Winne earliest ideas that led to the model can be traced to his conceptualization of SRL as a fusion of information processing and information processed (Winne, 1995) and Butler and Winne (1995) in their theoretical review of feedback and SRL, in which the concept of internal feedback had a major role and the first version of the model was presented (Figure 1 in Butler and Winne, 1995). Additionally, they presented a second figure in which they explored the different profiles a goal can take and the discrepancy between the goal aims and the current state of work monitoring (Figure 2 in Butler and Winne, 1995). In 1996, Winne presented an updated version of his model (Figure ​8) in which the two just mentioned figures were fused into one, along with a reflection about the metacognitive aspects that explains the differences in SRL (Winne, 1996). In 1997, he presented the COPES script ideas -see next section- (Winne, 1997). Finally, in 1998, a new version of his model was released (Figure ​9) including more details and a clearer presentation of COPES (Winne and Hadwin, 1998). It is usually the latter work that is cited when the model is referenced: Winne and Hadwin instead of Winne’s model. That denomination is also used in this review for now onward to keep the consistency with the SRL community, but it is important to keep in mind that the model was firstly presented in previous work (Winne, 1996, 1997). Additionally, these two authors, while collaborating in usual basis, have followed different paths within SRL research as signaled by different chapters in the 2011 SRL handbook (Hadwin et al., 2011; Winne, 2011). Winne has continue examining (meta)cognitive aspects of the model, such as his work on gStudy and nStudy (Winne et al., 2010). Furthermore, he performed minor enhancements to the model although the figure that illustrates the process remains the same (Winne, 2011). Hadwin, while continuing collaborating in the empirical evidence of the model (Winne et al., 2010; Winne and Hadwin, 2013) has additionally focused on the situational, contextual and motivational SRL aspects in collaborative learning settings. This line of work has produced the model of Socially Shared Regulated Learning (SSRL) in collaboration with Järvelä and Miller (see Hadwin, Järvelä, and Miller: SRL in the Context of Collaborative Learning). This present section will explore in more detail the work by Winne as the one by Hadwin will have its own section.

Winne and Hadwin’s Model of SRL

According to Winne and Hadwin’s model (e.g., Winne, 2011), studying is powered by SRL across four linked phases that are open and recursive and are comprehended in a feedback loop. These four phases are (Figure ​9): (a) task definition: the students generate an understanding of the task to be performed; (b) goal setting and planning: the students generate goals and a plan to achieve them; (c) enacting study tactics and strategies: the use of the actions needed to reach those goals; and (d) metacognitively adapting studying: occurs once the main processes are completed and the student decides to make long-term changes in her motivations, beliefs and strategies for the future. Winne especially emphasizes that mistakes can be detected in a posterior phase to the one in which they occurred.

Additionally, SRL deploys five different facets of tasks that can take place in the four phases just mentioned (Winne and Hadwin, 1998). These five facets are identified using the COPES acronym, that was used for the first time in Winne (1997) -i.e., Carla COPES with an arithmetic worksheet- (p. 399). It stands for (a) Conditions: resources available to a person and the constrains inherent to a task or environment (e.g., context, time); (b) Operations: the cognitive processes, tactics and strategies used by the student that are referred to as SMART -Searching, Monitoring, Assembling, Rehearsing and Translating- (Winne, 2001) (e.g., planning how to perform a task); (c) Products: the information created by operations (e.g., new knowledge); (d) Evaluations: feedback about the fit between products and standards that are either generated internally by the student or provided by external sources (e.g., teacher or peer feedback); and (e) Standards: criteria against which products are monitored (definitions taken from Winne and Hadwin, 1998; Greene and Azevedo, 2007) (e.g., assessment criteria).

Furthermore, Winne (2011) model explains in detail how students’ cognitive processing operates while planning, performing and evaluating a task. A crucial aspect is the use of criteria and standards to set goals, monitor and evaluate, aspects which are aligned with self-assessment research (Andrade, 2010; Panadero and Alonso-Tapia, 2013). The model describes how students constantly monitor their activities against standards and use tactics to perform tasks (Winne and Hadwin, 1998). One salient feature is that, in the model figure there is no reference to emotions, and there is only an allusion to motivation. Regardless of this Winne and Hadwin also agrees that SRL is goal-driven in nature and has built connections between his model and research by Pintrich (2003) and Wolters (2003) on regulation of motivation (Winne and Hadwin, 2008).

Empirical Evidence Supporting Winne and Hadwin’s SRL Model

Greene and Azevedo (2007) reviewed the empirical evidence for the model. Although they presented it as a theoretical review, due to the fact that they did not perform a “comprehensive review of the empirical literature” (p. 338), they reviewed a compelling number of studies (113) that provide empirical support for the model. The review covered all the aspects considered in the model, and made inferences that may have been beyond the initial scope of the work (e.g., they included a section for emotion, which is not explicitly mentioned in the original model). In their conclusions, they stated the model’s potential for future research and pointed out four challenges that needed additional clarification. First, phase four and external evaluations, especially clarifying long-term changes in the students’ SRL and more details on how phase four works (e.g., describing the role of conditions as products of the SRL activity). Second, they made a call for Winne and Hadwin’s model to incorporate the regulation of motivation, using Wolters (2003) as a reference to build the connection. Third, Greene and Azevedo recommended a discussion of how SRL skills develop over the life span. And, fourth, they made a call to consider how student characteristics (e.g., learning disabilities) might impact SRL.

In the later years, Winne and his team have been building a basis for gathering solid empirical evidence on the model based on the work with computers that scaffold students’ SRL while measuring it at the same time (Panadero et al., 2015b). These will be described in the next section. Additionally, Winne has also been exploring the potential of data mining and learning analytics and their application to SRL (e.g., Winne and Baker, 2013).

Instruments and Measurement Methods

No classical measurement instruments have been constructed based on Winne and Hadwin’s model, but there are a number of scaffolding tools that measure traces of SRL using the model as theoretical framework (e.g., Winne et al., 2010). They have developed nStudy and gStudy, which are computer-supported learning environments in which the use of SRL is scaffolded while students’ activities are recorded for trace and log data (Winne et al., 2010; Winne and Hadwin, 2013). Additionally, trace data which was brought to SRL research via Winne’s earlier work (Winne, 1982; Winne and Perry, 2000) has opened up new opportunities for the temporal and sequential analysis of SRL which is showing promising new insights for the field (Azevedo et al., 2010; Malmberg et al., 2013). Furthermore, Winne has written important reflection papers on SRL measurement, especially in Winne and Perry (2000) which emphasized the importance of “on-the-fly” or “online” SRL measures and opened up new approaches to the measurement of SRL (Panadero et al., 2015b); and in Winne et al. (2011) which reviews the SRL methods using trace data.

Pintrich: Grounding the Field and Emphasizing the Role of Motivation in SRL

Pintrich’s work continues to be important in the field as he made a major contribution toward clarifying the SRL conceptual framework (e.g., Pintrich and de Groot, 1990), he conducted crucial empirical work on the relationship of SRL and motivation (Pintrich et al., 1993a), and his questionnaire -MSLQ- (Pintrich et al., 1993b) continues to be widely used (Schunk, 2005; Moos and Ringdal, 2012).

History and Development of the Model

Pintrich was one of the first to analyze the relationship between SRL and motivation empirically (Pintrich and de Groot, 1990), theoretically (Pintrich, 2000), and the lack of connections between motivation and cognition (Pintrich et al., 1993a). Further, he later emphasized and clarified the differences between metacognition and self-regulation (Pintrich et al., 2000) and pointed out the areas of SRL that needed further exploration (Pintrich, 1999). In terms of the model itself, there is only one version of it, the one presented in the first handbook of SRL (Pintrich, 2000).

Pintrich’s SRL Model

According to Pintrich (2000) model, SRL is compounded by four phases: (1) Forethought, planning and activation; (2) Monitoring; (3) Control; and (4) Reaction and reflection. Each of them has four different areas for regulation: cognition, motivation/affect, behavior and context. That combination of phases and areas offers a comprehensive picture that includes a significant number of SRL processes (e.g., prior content knowledge activation, efficacy judgments, self-observations of behavior) (see Figure ​10). Furthermore, in that chapter, Pintrich (2000) explained in great detail how the different SRL components/areas for regulation are deployed in the different phases. Next how the different areas were conceptualized will be shortly presented. First, in terms of regulation of cognition, Pintrich incorporated metacognitive research such as judgments of learning and feelings of knowing. This incorporation emphasizes how important is cognition for Pintrich’s. Regarding the second area, regulation of motivation and affect, Pintrich explained that motivation and affect could be regulated by the students based on his own empirical work (Pintrich et al., 1993a; Pintrich, 2004). Three years later, Wolters (2003) continued this line of work finding more empirical evidence. The third area, regulation of behavior, is based on the work by Bandura (1977, 1986, 1997) and the Triadic model by Zimmerman (1989). In this area Pintrich incorporated the “individual’s attempts to control their own overt behavior” (Pintrich, 2000, p. 466). There is no other SRL model analyzed here that comprehends such area, making Pintrich’s in this sense unique. And, fourth area, the regulation of context which Pintrich included because it addresses those aspects of SRL in which the students attempt to “monitor, control and regulate the (learning) context” (p. 469).

Empirical Evidence Supporting Pintrich’s SRL Model

There is no empirical evidence directly addressing Pintrich’s model validation. However, there is empirical data on the validation of the MSLQ, questionnaire that is the initial empirical work in which Pintrich based his SRL model. That instrument will be analyzed in the next section. Additionally, in a special issue dedicated to his memory, Schunk (2005) reviewed Pintrich’s major contributions to the SRL field identifying six different areas: (a) a conceptual framework and model for SRL (just described in the previous section); (b) the role of motivation in SRL with a special focus on goal orientation; (c) the relationship between SRL, motivation and learning outcomes; (d) the role of classroom contexts in SRL and motivation; (e) the development of SRL through empirical studies; and (f) the development of an instrument to measure SRL (MSLQ).

Instruments and Measurement Methods

One major contribution to the SRL field is the MSLQ (Pintrich et al., 1993b). The MSLQ is composed of 15 scales, divided into a motivation section with 31 items, and a learning strategies (SRL) section with 50 items which are subdivided into three general types of scales: cognitive, metacognitive, and resource management (Duncan and McKeachie, 2005). One of the strengths of the MSLQ is its combination of SRL and motivation, which offers detailed information about students’ learning strategies use. Two versions of the questionnaire have been developed for college (Pintrich et al., 1993b) and high school students (Pintrich and de Groot, 1990). For further information on the instrument Duncan and McKeachie (2005) and Moos and Ringdal (2012) provided a list of studies that have used MSLQ. More recently, two reviews have found that the MSLQ is the most used instrument in SRL measurement (Roth et al., 2016) and in self-efficacy measurement (Honicke and Broadbent, 2016). This emphasizes the highly significant impact of Pintrich’s work in SRL.

Efklides: The Missing Piece between Metacognition and SRL

Efklides (2011) model has a stronger metacognitive background than the other models, except Winne and Hadwin’s which is also metacognitively based. However, when comparing with the latter in Efklides’ model motivation and affect occupy a central role in Efklides’ figure. The model has been cited a significant number of times despite being recently published.

History and Development of the Model

Efklides (2011) presented the Metacognitive and Affective Model of Self-Regulated Learning (MASRL) in 2011, which extended her ideas previously published in two theoretical articles (Efklides, 2006, 2008). The model is grounded in classic socio-cognitive theory (Bandura, 1986), as stated by the author herself. Efklides has been influenced by the existing SRL models, along with metacognitive models such as those created by Dunlosky and Metcalfe (2008), Ariel et al. (2009), and Koriat and Nussinson (2009). The distinction of Efklides with the metacognitive models mentioned is that hers is theoretically grounded on previous SRL models (e.g., Zimmerman’s Winne and Hadwin’s, and Pintrich’s). Additionally, Efklides’ model adds to the other SRL models analyzed here, a thorough presentation of the implications of metacognitive models for SRL.


In the MASRL, there are two levels (Figure ​11). First, there is the Person level-also called macrolevel-which is the most “traditional” view of SRL and comprehends the personal characteristics of the student. In Efklides’ own words: “The Person level represents a generalized level of SRL functioning. It is operative when one views a task resorting mainly on memory knowledge, skills, motivational and metacognitive beliefs, and affect” (Efklides, 2011, p. 10). Therefore, it is composed of: (a) cognition, (b) motivation, (c) self-concept, (d) affect, (e) volition, (f) metacognition in the form of metacognitive knowledge, and (g) metacognition in the form of metacognitive skills. A key aspect is that Efklides considers the Person level to be top–down because it is structured around students’ goals for the task. In other words, the thrust of the student’s goals “guides cognitive processing and the amount of effort” the student will invest, a decision based “on the interactions of the person’s competences, self-concept in the task domain, motivation, and affect, vis-à-vis the perception of the task and its demands” (Efklides, 2011, p. 12).

The second level, the Task × Person level–also known as microlevel–is where the interaction between the type of task and the student’s characteristics –i.e., person level–takes place. This level is bottom–up, as the metacognitive activity takes control of the student’s actions, which causes activity to be “data-driven” with the focus on addressing the demands of the specific task. To put it more simply, the student’s attention moves toward the specific mechanisms of performing the task, and the general learning goal (for example, finishing a summary) is subsumed in a more specific goal (for example, checking for spelling mistakes). Here, the microlevel monitoring is the main process; motivation and affect reactions depend on the evolution of the metacognitive resources and the feedback that comes from the person’s performance – i.e., if s/he is progressing appropriately. Finally, Efklides identifies four basic functions at this level: (a) cognition, (b) metacognition, (c) affect, and (d) regulation of affect and effort, which can be conceptualized independently, vertically, or, in an integrative way, horizontally (see Figure ​11).

This distinction between the Person level and the Task × Person level is probably the most salient feature of the MARSL model. The Person level represents the general trait-oriented features of students’ SRL, which are goal-driven and top–down. At this level, the MASRL model is similar to other more person-level-oriented models, such as Zimmerman’s (2000). At the Task × Person level, the actions that take place are less conscious and person-oriented: the execution of the task occupies most of the student’s attention and processing, and the actions are data-driven and bottom–up, showing similarities with Winne’s (2011).

In sum, the MASRL model clarifies, in detail, the relationship among metacognition, motivation, and affect via the interaction of the macro and micro levels, and presents a different conceptualization of the top–down/bottom–up implications from the one provided by Boekaerts and Corno (2005). Importantly, the model also illustrates how students perform during the task execution, the phase with the highest cognitive load where all the cognitive resources are leading the activity.

Empirical Evidence Supporting the MASRL Model

Efklides (2011) explored the basic MASRL features that have received empirical support by reviewing a compelling amount of evidence from the last two decades. First, she presented the three basic tenets of the model that the empirical evidence needs to address: (a) identifying the MASRL’s two levels (macro- and microlevel), the effects of the task demands on both levels, and what the interactions among them are; (b) the interaction of motivation and affect in the two levels; and (c) the different forms that metacognition takes at both levels. Then she argued that research showing interactions among metacognition, motivation, and affect at the two levels and their interaction actually supports the model. Finally, she presented a large number of studies addressing some of these aspects, grouped in different sections such as “Relations of cognition, metacognition and motivation/affect at the Task × Person level” or “Effects of affect on metacognitive experiences.”

Instruments and Measurement Methods

There are two instruments that reflect aspects of the MASRL model. First, Dermitzaki and Efklides (2000) constructed a questionnaire to measure self-concept for a language task. This instrument compares students’ language performance against the four reported categories: self-perception, self-efficacy, self-esteem, and perception of their abilities by others. The interaction of these components is a key aspect of the MASRL model as, for example, these interact at both the Person and the Person × Task levels with metacognition. Secondly, Efklides (2002) created the Metacognitive Experiences Questionnaire, which explores judgments and feelings about cognitive processing. In that paper, the relationship between metacognitive experiences and performance was explored, as well as the effect of task difficulty on metacognitive experiences.

Hadwin, Järvelä, and Miller: SRL in the Context of Collaborative Learning

Hadwin et al. (2011, in press) and Järvelä and Hadwin (2013), together with other colleagues (for a review, see Panadero and Järvelä, 2015), have explored the potential of SRL theory in explaining regulation in social and interactive features of learning, e.g., use of information and communication technology (ICT) and computer-supported collaborative learning (CSCL) settings. The exploration of SRL and metacognition with this particular purpose is relatively recent, with 2003 identified as the year for the first empirical evidence published (Panadero and Järvelä, 2015). Additionally, the model is strongly influenced by Winne and Hadwin’s (1998) model, as noted in Section “History and Development of the Model.”

History and Development of the Model

One of their premises is that, despite the advantages of collaboration and computer-supported collaboration for learning (Dillenbourg et al., 1996), collaboration poses cognitive, motivational, social, and environmental challenges (Järvelä et al., 2013; Koivuniemi et al., 2017). To collaborate effectively, group members need to commit themselves to group work, establish a shared common ground, and negotiate and share their task perceptions, strategies, and goals (Hadwin et al., 2010); in other words, they need to share the regulation of their learning (SSRL). The key issue in SSRL is that it builds on and merges individual and social processes, and it is not reducible to an individual level. It is explained by the activity of the social entity in a learning situation (Greeno and van de Sande, 2007), including situational affordances that provide opportunities for SSRL to happen (Volet et al., 2009).

As mentioned above, SSRL is a field recently developed within SRL. Because of this, the model proposed by Hadwin, Järvelä, and Miller (hereinafter referred to as the SSRL model) has changed significantly from their first proposition in the 2011 handbook to the chapter in the forthcoming SRL handbook.2 The two biggest changes incorporated in the latest are: the authors have clarified their perspective on what is Co-regulated Learning (definition below) and they have incorporated and clarified the influence of COPES (Winne, 1997) in their model (Hadwin et al., in press).

The Model

The SSRL model (Hadwin et al., 2011, in press) proposed the existence of three modes of regulation in collaborative settings: self-regulation (SRL), co-regulation (CoRL), and shared regulation (SSRL) (Figure ​12). First, SRL in collaboration refers to the individual learner’s regulatory actions (cognitive, metacognitive, motivational, emotional, and behavioral) that involve adapting to the interaction with the other group members.

Secondly, CoRL in collaboration “refers broadly to affordances and constraints stimulating the (student’s) appropriation of strategic planning, enactment, reflection, and adaptation (occurring when in interaction with other students or group members)” (Hadwin et al., in press, p. 5). This regulatory level is the one that has been in the most dispute in the field, as its use has not been consistent (Panadero and Järvelä, 2015).

Finally, the third type, SSRL in collaboration, occurs when “deliberate, strategic and transactive planning, task enactment, reflection and adaptation” are taken within a group (Hadwin et al., in press, p. 5). The key difference between SSRL and CoRL is that, in the former, the regulatory actions “emerge through a series of transactive exchanges amongst group members” whilst in CoRL they are guided or directed by (a) particular group member/s.

What are the significant changes between the 2011 model version and the forthcoming version? First, the CoRL mode has been reconceptualized based on the empirical evidence (Panadero and Järvelä, 2015). Hadwin et al. (2011) proposed three types of CoRL: (a) temporary mediation (by other than the learner) of regulated learning to promote SRL, (b) distributed regulation of each other’s learning in a collaborative task, and (c) a microanalytic approach focusing on interactions through which social environments co-regulate learning. In their forthcoming proposal, they have shifted the focus to the effects of collaborating alone and have not discussed the microanalytic approach in such detail. Another crucial change is that they have considered the reviewed empirical evidence that CoRL and SSRL could both occur “as groups progress through different phases on their collaboration and not always SSRL nor will co-regulation happen in isolation” (Panadero and Järvelä, 2015, p. 199).

Hadwin et al. (2011) conceptualized SSRL as unfolding in four loosely sequenced and recursively linked feedback loops (Figure ​13) taken from Winne and Hadwin (1998). During the first loop, groups negotiate and construct shared task perceptions based on internal and external task conditions. Through the second loop, groups set shared goals for the task and make plans about how to approach the task together. In the third loop, groups strategically coordinate their collaboration and monitor their progress. Based on this monitoring activity, the groups can change their task perceptions, goals, plans, or strategies in order to optimize their collective activity. Finally, in the fourth loop, groups evaluate and regulate for future performance. In essence, when groups engage in SSRL, they extend regulatory activity from the “I” or “you” level to regulate their collective activity in agreement (Hadwin et al., 2011).

In the forthcoming model proposal, the four-phase cycle remains, but under different labels, now using the ones proposed in Winne and Hadwin’s work. Additionally, there is a crucial change: Winne’s (1997) COPES architecture is introduced for the first time in the SSRL model. This addition clarifies, especially, the (meta)cognitive processing at the three regulatory modes along with the effects on motivation and emotion (Hadwin et al., in press, Figure ​1).

Empirical Evidence Supporting the Model

The SSRL authors have been working toward empirical verification of their model (e.g., Järvelä et al., 2013). Meanwhile, other researchers have also conducted a growing number of studies on SSRL. A review on CoRL and SSRL by Panadero and Järvelä (2015) extracted three main conclusions. First, different levels of social regulation were identified: a less balanced type called co-regulation, in which one member of the group takes the lead; and a jointly regulated type, in which goals are negotiated and strategies are shared, known as SSRL. Because of this, those authors proposed to reconceptualize how the CoRL and SSRL modes intertwine, which constitutes one of the main changes in the forthcoming version of the model. Secondly, empirical evidence of the occurrence of SSRL in cognitive, metacognitive, motivational, and emotional shared areas were found. This finding is important, as it shows that shared regulation happens within all SRL areas. And third, there was evidence that SSRL might promote learning and performance. Additionally, new research published after the review has continued strengthening the empirical evidence around the model (e.g., Järvelä et al., 2016a,b).


At this time, no classical measurement instruments (e.g., questionnaires) have been developed under the SSRL model, even though there is research in the field using self-reported data (e.g., Panadero et al., 2015a). Because of the contextual nature of interpersonal regulation of learning (Vauras and Volet, 2013), new methodologies have been developed to investigate SSRL. Current instruments combine scaffolding and measures in context as, for example, a computer-supported environment to promote group awareness, planning, and evaluation (e.g., Järvelä et al., 2015). Additionally, the joint effort of the model authors has been in developing multi-modal data collections including objective data (e.g., eye tracking, physiological responses) triangulated with subjective data, such as students’ conceptions and intent (Hadwin et al., in press).

Department of Education, Gustavus Adolphus College, Mattson Hall, 800 West College Avenue, Saint Peter, MN 56082, USA

Copyright © 2012 Daniel C. Moos and Alyssa Ringdal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Empirical research has supported the long held assumption that individual differences exist in how students learn. Recent methodological advancements have allowed educational research to examine not only what students learn, but also how they learn. Research has found that active involvement in learning, including setting meaningful goals, selecting appropriate and task-specific strategies, monitoring motivational levels, and adapting based on feedback are all positively related to learning outcomes. How can teachers support students’ development and use of these learning processes? The goal of this paper is to examine research that has used the Self-Regulated Learning (SRL) theory to consider this broad question. Methodological advancements recently used in this field of research, various SRL theoretical frameworks guiding this research, and studies that empirically examined self-regulation with both preservice and inservice teachers are discussed. The paper concludes with the theoretical, methodological, and practical implications of the reviewed studies.

1. Introduction

Empirical research has supported the long held assumption that individual differences exist in how students learn. Recent methodological advancements have allowed educational research to examine not only what students learn, but also how they learn. Moos and Azevedo, for example, have used a think-aloud protocol to capture the dynamic nature of how individual students use strategies, monitor emerging understanding, and make plans during learning. The think-aloud has provided rich data, as evidenced by the following excerpt from one of their studies [1]. This study provided process data on how students learn about a conceptually complex science topic. The regular font indicates the student’s thoughts as she thinks aloud, whereas the italicized font indicates reading from the material during the learning task.

I am going to start with the circulatory system just because I am already there…and I’m just reading the introduction…circulatory system…also known as the cardiovascular system and it deals with the heart…it transports oxygen and nutrients and it takes away waste…um, it does stuff with blood and I’m kind of remembering some of this from bio in high school, but not a lot of it.

Reads: The heart and the blood and the blood vessels are the three structural elements and the heart is the engine of the circulatory system, it is divided into four chambers.

I knew this one, two right and two left…the atrium, the ventricle and the left atrium, and the left ventricle…okay start the introduction [of the heart], just kind of scout it out real quick…and there’s a section called function of the heart…and it looks like it will give me what I need to know…um…introduction, oh that’s just basic stuff that we’ve been doing…

Reads: Structure of the heart has four chambers…

We did that…

Reads: The atria are also known as auricles. They collect blood that pours in from veins…

So, it looks like the first step is atria in the system and then the veins.

Though this segment is a small snapshot of the student’s learning process for this particular task (see [2] for the complete data), it is clear that she was actively engaged in the learning process. She monitored the relationship between the content and her prior domain knowledge (i.e., “I am kind of remembering some of this from bio in high school”), while also using appropriate strategies. Even within this short learning segment, the student engaged in these monitoring processes and used strategies at multiple points (i.e., “We did that…” and “So, it looks like the first step is atria…”). This student’s active engagement was not observed with all the participants in this study, as demonstrated by the following excerpt from another participant who was asked to learn the same material in an identical context as the above student.

I am going to the introduction…

Reads: Circulatory system, or cardiovascular system, in humans, the combined function of the heart, blood, and blood vessels to transport oxygen and nutrients to organs and tissues throughout the body and carry away waste products…

I’m going to take notes…transport oxygen…nutrients…to organs and tissues and carry away waste products.

Reads: Among its vital functions, the circulatory system increases the flow of blood to meet increased energy demands during exercise and regulates body temperature. In addition, when foreign substances or organisms invade the body, the circulatory system swiftly conveys disease-fighting elements of the immune system, such as white blood cells and antibodies to regions under attack…

I’m writing down the structural elements…

Reads: The heart is the engine of the circulatory system. It is divided into four chambers: The right atrium, the right ventricle, the left atrium, and the left ventricle. The walls of the chambers are made of a special muscle called myocardium, which contract continuously and rhythmically to pump blood.

…okay, the heart…engine…the chambers…right and left atrium…right and left ventricle. Okay…special muscle…myocardium…mmmm…

Reads: The human heart has four chambers, the upper two chambers…the right side of the heart is responsible for pumping oxygen-poor blood to the lungs…This oxygen-poor blood feeds into two large veins, the superior vena cava and inferior vena cava. The right atrium conducts blood to the right ventricle, and the right ventricle pumps blood into the pulmonary artery. The pulmonary artery carries the blood to the lungs, where it picks up a fresh supply of oxygen and eliminates carbon dioxide.

This student exhibited a different learning process, with limited monitoring activities and use of a small subset of strategies. Such differences in how students learn explain variability in what they learn.

These two examples are consistent with the long held theoretical assumption that students actively construct knowledge in an idiosyncratic process (i.e., Constructivism; [3]). Ideally, students actively engage in the learning process, such as setting meaningful goals, selecting appropriate and task-specific strategies, monitoring motivational levels, and adapting based on feedback are all positively related to learning outcomes [1, 2, 4–12]. However, empirical research has provided process data that reveal the substantial individual differences with which students engage in the learning process. Certainly, individual cognitive characteristics (e.g., prior domain knowledge), motivational levels (e.g., self-efficacy), and developmental constraints affect how students learn. Aside from personal variables, the context can assume a particularly powerful role in how students approach the learning process and further develop their learning skills. Imagine, for example, a teacher who holds a personal belief that authority figures have knowledge that is inaccessible to novices. This teacher may resort to more didactic classroom practices, such as a reliance on lecturing/direct instruction, and thus may limit opportunities for students to engage and further develop learning skills. A fundamental question arises as to how teachers can best support students’ development and use of learning processes. The goal of this paper is to examine research that has considered this broad question. The first step in examining this question is to articulate a theoretical framework that is robust enough to explain the complexities of learning. As such, the next section will first provide an overview of the Self-Regulated Learning (SRL) theory. Following this overview, SRL in the context of the classroom will be briefly examined and the role of the teacher in self-regulation will be introduced. Finally, a detailed rationale for this paper is provided at the conclusion of this section.

2. Overview of SRL Theories

In order to examine how teachers can best support their students’ SRL, it is necessary to first understand how students can self-regulate their learning. Though the field of SRL has led to the development of distinct theoretical approaches that focus on a variety of constructs [13, 14], there are four common assumptions regarding how students can self-regulate their learning [15]. First, it is assumed that students can potentially monitor and regulate their cognition, behavior, and motivation, processes that are dependent on a number of factors including individual differences and developmental constraints. A second assumption suggests that students actively construct their own, idiosyncratic goals and meaning derived from both the learning context and their prior knowledge. Thus, students engage in a constructive process of learning. Not surprisingly, then, it is also assumed that all student behavior is goal-directed and the process of self-regulation includes modifying behavior to achieve goals. Lastly, it is assumed that self-regulatory behavior mediates the relationship between a student’s performance, contextual factors, and individual characteristics.

While these assumptions provide the foundation for most SRL theories (see [14] for an overview), specific approaches have been predominant in research examining how students self-regulate their learning within the context of the classroom. Zimmerman’s [16] SRL theory is one of the most common theories in this line of research. In this model, self-regulation is composed of three phases: forethought, performance control, and self-reflection. In the first phase, the student “sets the stage” for the upcoming learning task. Self-regulated students develop realistic expectations, create goals with specific outcomes, and identify plans to maximize success in the particular learning task. It is in this phase that self-regulated students may ask themselves such questions as “Where is the best place for me to complete the work?”, “What conditions will create challenges for me?”, and “How will I start?” Performance control, the second phase of SRL in this theoretical approach, constitutes processes that are involved during learning. This phase includes specific strategies such as self-talk and self-monitoring that are used to maximize success on a learning task. Questions that self-regulated students may ask themselves in the second phase are “Am I following my plan correctly?”, “Am I being distracted?”, and “What strategies can I use to help me keep working?” Lastly, self-regulated students reflect at the conclusion of the learning activity, the third phase of SRL. This self-evaluation compares the performance outcome to goal(s). Self-regulated students in the phase will ask themselves such questions as “Did I meet all of the goals?”, “Which conditions helped me be successful and what conditions distracted them?”, and “Which strategies were effective given the context and learning activity?”

Pintrich [15] offers a slightly different perspective on how students can self-regulate their learning, with a comprehensive framework of four phases and four areas. The four phases include planning, monitoring, control, and reflection. These phases are intended to reflect common assumptions shared by many SRL models [17]. In phase one, the student plans, sets goals, and activates knowledge about the context, text, and self. Phase two is defined when the student exhibits metacognitive awareness and monitoring of cognition. In phase three, the student selects cognitive strategies and regulates different aspects of the context, task, and self. Lastly, in phase four, the student makes cognitive judgments and reflections on the context, task, and self. Within these individual phases, Pintrich [15] also proposes four different areas in which self-regulation can occur. Based on different psychological functioning (see [18]), the first three areas for regulation are cognition, motivation/affect, and behavior. The last area reflects contextual features, such as task characteristics, which can impede or facilitate an attempt to self-regulate their learning. As commonly suggested by most SRL theories, Pintrich’s [15] model assumes that these phases are not hierarchical because they can occur concurrently and dynamically.

Winne and colleagues (i.e., [19–22]) offer another perspective that is guided by the Information Processing Theory (IPT). This model includes four phases of SRL: (1) understanding the task, (2) goal-setting and planning how to reach the goal(s), (3) enacting strategies, and (4) metacognitively adapting to studying. In the first phase, the student constructs a perception of the task from information in the learning context (Task Conditions) as well as information from prior experience and knowledge (Cognitive Conditions). The student develops goals and plans in the second phase, followed by selection and use of tactics and/or strategies in the third phase. Phase four includes monitoring activities and making cognitive evaluations about discrepancies between goal(s) and current domain knowledge. This model assumes that SRL has a recursive nature due to a feedback loop, during which discrepancies revealed by monitoring activities will lead self-regulated students to adapt their planning and/or strategies.

3. SRL in the Classroom

All three of these theoretical frameworks explicitly account for the role of context in students’ SRL. The social cognitive approach to SRL (Zimmerman, 1994 [17]), for example, assumes that environmental factors have a bidirectional interaction with students’ personal and behavioral characteristics. Interaction with the context results in cyclical development and adaptation of students’ SRL. For example, teachers could foster their students’ self-reflection by prompting them with questions such as “Did you meet all of the goals of the learning task?” and “Which strategies were effective for this particular learning task?” This prompting by the teacher may, in turn, foster the students’ engagement in forethought as they “set the stage” for the subsequent, upcoming learning task.

Though the IPT approach [20, 21] offers distinct assumptions, it also provides an explanation of how context affects SRL. According to this theory, students develop perceptions of the learning task partly based on information provided in the context. This theory assumes a cyclical nature to SRL; information processed in one phase can become an input to subsequent information processing. Teachers’ support of metacognitive monitoring, for example, can assist students in this critical component of SRL. These theoretical assumptions regarding the importance of the context and documented empirical relationships between SRL and learning outcomes have led to recommendations that classroom instruction should extend beyond factual knowledge. It has been argued that competencies with the process of learning, such as students’ ability to self-regulate their learning, should be a central, explicit aim within education [23]. Thus, teachers’ ability to support students’ development of self-regulation should be carefully considered if students’ SRL is an educational goal [24].

Research has also suggested that teachers should focus on their own self-regulated learning skills because it allows them to more deeply reflect on their own teaching practices, which can lead to increased student performance (Let and Lin 2003; Xiaodong et al., 2005). Others have argued that teachers need to be self-regulated learners themselves due to ever-changing curricular revisions, which require innovation and adaptability [25]. Teachers who engage in self-regulation are better able to meet these demands because they can balance a variety of professional demands, engage in reflective thinking, and embrace adaptation. Furthermore, a growing body of research has found a significant relationship between teachers’ personal beliefs and their instructional pedagogy [26–28] (Shraw and Olafso, 2002). Teachers who are incapable of self-regulating their own learning and/or do not hold personal beliefs that students can engage in SRL are less likely to support the development of these capabilities in the classroom [29–31].

4. Rationale of Literature Review

Given the importance of SRL in the context of classrooms, it is not surprising that a rich body of empirical research has emerged examining how teachers support their students’ self-regulation, as evidenced by literature reviews on classroom applications of SRL. For example, Paris and Paris [32] provide an incredibly informative literature review that categorizes relevant research into two groups, both of which focused on promoting SRL in students. One group of studies assumed a developmental view of SRL and sought to examine how students self-regulate learning to meet personal goals. A second group of studies examined the role of a transmission model in the acquisition of SRL. These studies considered the effect of explicit instruction in the use of self-regulated learning strategies. Such reviews have greatly advanced the field by providing clear and explicit guidelines for promoting SRL in the classroom. It was our aim to provide a literature review that offers a slightly different perspective from existing reviews by considering the methodological advancements recently used in this field of research (i.e., process data), discussing various theoretical frameworks guiding this research, and summarizing studies that empirically examined SRL with both pre-service and in-service teachers. This literature review aims to systematically consider each of these areas through the following research questions:(1) What implications do the literature provide for supporting SRL in teacher education programs?(2) What implications do the literature provide for supporting SRL with different kinds of teachers?(3) How is SRL measured in research that examines self-regulation in the classroom?

5. Method

5.1. Criteria of Selection

The empirical studies selected for this Literature Review examined the teacher’s role in relation to SRL. After the initial selection of articles, inclusion criteria were used to identify which studies would be examined for this Literature Review. These criteria centered on three main areas: (1) Theoretical Framework; (2) Focus on Teachers; and (3) Methodology.

First, studies were chosen that were explicitly guided by a SRL theory and used this theoretical framework as a lens to interpret the results. Studies were excluded from this review if they examined a specific process of SRL, such as strategy use, but did not explicitly reference a SRL theory. Secondly, studies had to include either pre-service or in-service teachers in the sample. Because our research questions consider the teacher’s role in SRL, it was necessary for included studies to measure and assess teachers in some way. Third, the methodology of each study was evaluated in order to determine the soundness of its statistical analyses. In addition, the sample of the study needed to be appropriately described. Lastly, studies that focused on development of SRL measures were excluded due to the scope of this Literature Review.

5.2. Search Procedures

Based on a suggested framework for developing literature reviews (see [33]), the literature search was comprised of two stages: (1) Identify all relevant articles in an initial search; (2) Select articles to review based on inclusion criteria. First, a search for articles from the PsycInfo database was performed. During this initial literature search, a variety of keywords (“self-regulated learning”; “self-regulat*”; “SRL”; “teacher”; “student teacher”; “pre-service teacher”) from the articles’ abstracts were used to identify the most relevant articles.

The first stage of the search produced 186 articles on SRL and teachers. In the second stage of the search, dissertation, chapters, literature reviews, and technical reports were removed from the pool of potential articles. In the third stage of the search, the inclusion criteria were applied to the remaining articles. The articles that were explicitly guided by a SRL theory, had a focus on in-service and/or pre-service teachers, and used a sound methodological approach were included. This final stage of the search, which concluded in June of 2011, resulted in 38 articles to be included in this Literature Review. Articles published after June of 2011 were not included in this Literature Review.

We examined these remaining articles for natural groupings and created three research questions that captured what we believe to be important components of the topic. The articles in these three main research questions were further divided into subsections illustrating specific trends within each question (see Figure 1 for the research questions and subsections). The organization of the articles for the first research question was not explicitly guided by predetermined categories, but rather was done post-hoc to determine the most natural groupings. This bottom-up approach was deemed to be most appropriate given there were no inherent assumed categories for this question, particularly when compared to the second and third research question. Many of the articles could have been placed in multiple categories so we assigned them according to the best fit (see Table 1 for complete list of articles, by research question). We chose thirteen of these articles to address the first research question, which considered the implications for teacher education programs. Nineteen studies examined the implications for in-service teachers supporting SRL with different kinds of teachers, our second research question. The final six articles formed a group relating to the third research question that considered how SRL is measured in the studies.

Table 1: Complete list of reviewed studies by research question.

Figure 1: List of research questions and subsections.

6. Results

6.1. What Implications Does the Literature Provide for Teacher Education Programs?

This section synthesizes studies that empirically examined self-regulation within populations of preservice teachers. Not surprisingly, a group of these reviewed studies considered the relationship between preservice teachers’ characteristics and attitudes with SRL. For example, Bråten and Strømsø [34] examined the role of personal theories of intelligence and epistemological beliefs in “motivational and strategic components” of SRL with 108 student teachers and 178 business administration college students. Multiple regressions revealed a significant effect of personal beliefs on SRL. Specifically, beliefs about knowledge construction were a strong predictor of SRL for the student teachers. Other studies reveal that preservice teachers’ personal beliefs regarding SRL may be conceptually different than their teacher educators. Kremer-Hayan and Tillema [35] interviewed 32 Israeli and 58 Dutch teacher educator, and student teachers in order to investigate potential differences in how these two groups view the meaning and implementation of SRL in the classroom. Somewhat surprisingly, the teacher educators were found to have a less positive attitude towards SRL and lower expectations about their competencies related to self-regulation. Other research has focused on the relationship between preservice teachers’ motivation and use of learning strategies during education courses. Atputhasamy and Aun [36] found a positive relationship between those who used deeper level processing strategies such as metacognition and elaboration and learning goal orientation. Student teachers who reported an achievement goal orientation, on the other hand, used fewer self-regulatory processes, including organization and critical thinking.

The relationship between preservice teachers’ personal beliefs and SRL raise the question of whether there is a developmental trajectory with their self-regulation competencies during teacher education programs. Some research has shown that appropriate contextual support can enhance SRL development. Hutchinson and Thauberger [37] present compelling evidence that student teachers can, in fact, be mentored to more effectively foster elementary children’s use of SRL. Detailed analyses of transcripts revealed that a variety of scaffolding techniques during discussions support student teachers’ development of SRL practices within elementary classrooms. Perry et al. [38] provide additional information on how student teachers can be mentored to design instructional contexts that support SRL. These two studies provide promising data that student teachers are capable of designing such tasks. These findings are contrary to the notion that several years of experience are required before teachers can begin to consider students’ needs and abilities when planning and implementing instruction [39]. Direct scaffolding and explicit instruction, both in education courses and during student teaching, can assist preservice teachers’ implementation of classroom tasks that offer autonomy, control challenge, and non threatening self and peer evaluations, all of which are hallmarks of classrooms that support SRL [38]. Perry et al. [40] also examined whether master teachers can mentor student teachers to develop and implement classroom practices that foster SRL in an elementary school setting. Data indicate mentoring is effective and that master teacher practices accounted for 20% of the variance observed in the student teachers’ SRL practice.

Other research has turned to technology as a means to support preservice teachers’ SRL development. Kramarski’s robust and innovative line of research has evidenced the potential of emerging technology in preparing teachers, particularly for supporting self-regulation in the classroom. Kramarski and Michalsky [29], for example, investigated how the development of three dimensions (SRL in pedagogical context, pedagogical knowledge, and perceptions of teaching and learning) was affected by various contextual supports, namely, e-learning with and without SRL support and face-to-face learning with and without SRL support. In this study, preservice teachers randomly assigned to the e-learning condition were asked to solve pedagogical tasks (i.e., compare different types of cooperative learning) with a nonlinear technology environment. Preservice teachers assigned to the face-to-face condition, on the other hand, were asked to solve the same pedagogical tasks with material provided by the teacher (i.e., a more “traditional” classroom setting). Results indicated a significant effect of SRL support; those who received it in both the face-to-face and e-learning conditions outperformed those who did not receive it. However, those preservice teachers who received this support in the context of technology (e-learning) demonstrated highest SRL ability, pedagogical knowledge, and student-centered learning perceptions. Kramarski and Michalsky [29] argue that the nature of emerging technology, such as the e-learning environment used in this study, encourages the use of exploration, elaboration, and activation of prior knowledge because of its inherent nonlinear design. Therefore, explicit support of SRL in these environments promotes more active engagement of learning material. Kramarski and Michalsky [41] found similar results in another study that examined the effects of two hypermedia environments and SRL support with 95 preservice teachers. This study focused on a specific component of SRL, metacognition. Originally conceptualized as “thinking about thinking” (see Miller et al. 1970, p. 613), metacognition has more recently been conceptualized to include both the conscious awareness and regulation of one’s own learning. Metacognition is a construct that focuses on processes related to the abstraction of existing or new cognitive structures [42]. A number of SRL theories highlight the importance of metacognition in self-regulation (e.g., [21]), noting its role in effective task execution. Metacognitive processes affect the use of cognitive activities, which support the acquisition and retention of knowledge (Ku and Ho, 2010). Kramarski and Michalsky [41] found that exposing preservice teachers to metacognitive support in hypermedia environments enhances their own metacognition. Participants who received metacognitive support during the experimental learning session demonstrated a significantly better ability to regulate and reflect on their own learning processes. Based on these findings, Kramarski and Michalsky [41] suggest that preservice teachers with more developed metacognition ability will be better prepared to support this aspect of SRL with their own students [43]. Other studies have shown that metacognitive scaffolding can effectively foster preservice teachers’ ability to use SRL processes such as self-monitoring and evaluation strategies [44, 45].

Karmarski and colleagues’ work has typically used technology as the context for SRL support. Given the emerging nature of technology in preservice teacher education, this context is important to consider. Online classrooms, distance education, and hybrid classroom settings are becoming more commonplace. Some argue that these environments can optimize the SRL development of preservice teachers. Delfino et al. [25], for example, used an interaction analysis to examine how collaborative activities in an online classroom can develop preservice teachers’ ability to support students’ SRL. Participants repeatedly demonstrated SRL, including self-reflection, self-awareness, and setting immediate goals. Dettori et al. [46] also examined the impact of online learning environment on preservice teachers’ development of SRL. Their study identifies specific aspects of the environment that can foster self-regulation, including social competencies, motivational aspects and metacognitive and cognitive skills.

6.2. What Implications Do the Literature Provide for Supporting SRL with Different Kinds of Teachers Groups?

A small subset of the reviewed studies for this research question focused on how inservice teachers themselves use SRL to learn new information and engage in professional development. Kreber et al. [47] used semistructured interviews to examine how 31 university science teachers engage in SRL when developing their expertise about teaching. Guided by both Zimmerman’s SRL model [16] and Kreber and Cranton’s Scholarship of Teaching model, the researchers came to the conclusion that individual differences in how university teachers engage in SRL are a product of educational development experiences. Workshops on teaching, active solicitation of student feedback, and adaptation of teaching practices positively affect SRL of university teachers. Tillema and Kremer-Hayon [48] found that in addition to previous experiences, personal beliefs of university teachers also affect the extent to which they engage in SRL. Data gathered from 12 Israeli and 17 Dutch teacher educators surprisingly suggest a divergence between teachers’ pedagogy and the extent to which the teachers were engaging in SRL themselves. van Eekelen et al. [49] found similar results with fifteen experienced college teachers from The Netherlands. Using semi-structured interviews and a digital diary study, they found limited examples of teachers engaging in self-regulation with their learning. This somewhat surprising lack of SRL within this particular group of in-service teachers raises pedagogical issues and questions for teacher education programs, particularly if it is assumed that teachers’ own self-regulatory behavior affects the classroom environment [50]. Research has suggested that changes in experienced teacher’s self-regulatory behavior are related to their experimentation with new teaching methods and active reflection on the effectiveness of a variety of teaching methods [51].

From a developmental perspective, Oolbekkink et al. [52] considered the differences and similarities between 36 university and secondary teachers’ perspectives on SRL. The researchers aimed to use teachers’ perspective on SRL as an explanatory lens for why some students face a problematic transition from secondary to higher education. Not surprisingly, a qualitative analysis of the interview protocols in the study revealed that while university teachers focus on the variety of content, secondary teachers tend to consider the variety within students, particularly with how they engage in self-regulation. Kistner et al. [53] provide perspectives on how high school teachers support SRL with a study that included 20 German mathematics teachers and their 538 secondary students. A coding system was used to assess the teachers’ explicit and implicit instruction of various SRL strategies, including motivation (resource management), metacognition (planning), and cognition (organization). Students’ performance, which was measured before and after the learning lesson, was positively related to cognitive strategies. However, these researchers also note that while explicit instruction of cognitive strategies was positively related to student performance (more so than implicit instruction), the occurrence of this embedded instruction was rare. Veenman et al. [54] provide a potential explanation for the rarity of explicit SRL instruction in high school classrooms. This study included a SRL training program for 25 Dutch secondary school teachers, with a quasiexperimental, treatment-control group. Classroom observations and ratings from both teachers and students led to the conclusion that the SRL training program had little effect on classroom practices. It was concluded that training secondary teachers to explicitly embed the instruction of SRL strategies is time consuming and effects may not been seen immediately. Actively embedding SRL instruction is important, though, even for the older development group of high school students. Research has demonstrated that students of this age benefit from explicit instruction and teachers’ willingness to adapt classroom practices to meet their developmental level with respect to SRL [55].

Substantially fewer studies have considered the role of the middle school teacher in supporting SRL. Pauli et al. [56] explored the extent to which 8th grade math teachers implement various features of SRL to promote problem solving and mathematical modeling. Measurement techniques included videotapes of lessons, student and teacher questionnaires, and math achievement tests. Teachers reported how frequently they provided opportunities for SRL and independent problem solving. Results indicate that teachers’ personal beliefs influenced the extent to which they fostered independent problem solving. Furthermore, opportunities for SRL were positively related to students’ learning experience. However, research has demonstrated that these opportunities need to be explicit to the students, as evidenced by Cooper et al. [57] study. This study examined how 7th grade high school English teachers can foster SRL. The researchers collaborated with the participating teachers, meeting once a week over a three-month period to discuss how to design higher-order reasoning questions in a myriad of class assignments. Interviews revealed that conscious and explicit embedded instruction of SRL resulted in students’ increased understanding of self-regulation, particularly with goal setting.

Research has supported the assumption that SRL can be fostered with even younger students in upper elementary grades. Ee et al. [58] study examined the relationship between teachers’ goal orientations and instructional practices with their Primary 6 students’ SRL. The sample included 566 high achieving Primary 6 students and 32 teachers across 34 Singapore schools. Surprisingly, this study found a negative relationship between teachers’ explicit SRL instruction (primarily cognitive strategies) and students’ ego goal orientation. One explanation is that participants were all high-achieving and thus may have reached a certain level of automaticity with the use of cognitive strategies. As a consequence, explicit instruction of a skill that was already possessed may have had negative motivational effects on the students. This somewhat surprising finding concerning explicit SRL instruction with middle school students contradicts other findings with upper elementary students. For example, Hilden and Pressley [62] found evidence that a year-long professional development program in which 5th grade teachers were trained how to explicitly teach SRL resulted in the improvement of both their reading comprehension instruction and their students’ self-regulated use of comprehensive strategies.

These two findings suggest that the effectiveness of explicit SRL instruction may be mediated by personal characteristics. Housand and Reis [59] present an argument that gifted and high achieving students as early as fifth grade may have already obtained the capacity to engage in a variety of SRL processes. Their findings suggest some upper elementary students demonstrate the ability to engage in self-regulation even in classrooms that are characterized as low self-regulation. Though fewer in numbers, these studies indicate that while the environment certainly can affect the development of SRL, there are personal characteristics that play a role. However, it is commonly assumed that in addition to personal characteristics the context of the environment and instructional opportunities need to be clearly considered, if SRL is an educational goal [61]. Some research has pointed to technology as an instructional opportunity to foster self-regulation for upper elementary students. Meyer et al. [60], for example, examined the impact of an electronic portfolio, ePEARL, on the literacy and SRL of 296 4th–6th graders across three Canadian provinces. The 14 teachers who participated in this study reported that the use of this electronic portfolio had a positive impact on their SRL teaching strategies and that the students’ increased literacy was a result of the planning and reflecting required by this learning tool.

Given the developmental trajectory of students’ SRL, it has been questioned whether younger elementary students can engage in SRL. Perry and colleagues have added significant work in this area. Their rich line of research has provided compelling evidence that the youngest elementary-aged students are capable of self-regulating their learning. Perry’s earlier work [63] challenged the notion that young children lack the capacity to engage in SRL and adapt their motivational orientations. Contextual factors of the environment can provide the necessary support for students as young as 2nd and 3rd grade to develop the ability to self-regulate their learning. Classroom observations led Perry and VandeKamp [64] to the conclusion that nonthreatening evaluation practices, involvement in complex reading and writing activities, the provision of autonomy related to what the students read and write about, and the ability to modify learning tasks to control challenge are all contextual features of classrooms that promote SRL in younger elementary-aged children. Others have shared Perry’s findings, such as Perels et al. [65]. This study examined the effect of SRL training on 35 German kindergarten teachers. Results from teacher self-report questionnaires and student interviews suggest that training effectively improves teachers’ ability to foster SRL with students as young as preschool.

In sum, these lines of research suggest that teachers of different age groups distinctly support SRL in their classrooms. Generally speaking, research has found limited examples of learning opportunities that support SRL in university and college classrooms [49]. Findings suggest that these teachers tend to focus on the content of their class. On the other hand, secondary (high school) teachers tend to consider the variety within students, particularly with how they engage in self-regulation [52]. However, research suggests that while high school teachers may offer more opportunities for students to engage in SRL, these experiences may be implicit in the teacher’s pedagogical approach [53]. Explicit instruction of SRL is not readily apparent in high school teachers’ instruction, findings that have also been replicated within middle school classrooms. Though middle school students benefit from explicit SRL instruction, teachers of this developmental group do not routinely integrate this component into lesson plans [57]. Quite surprisingly, while empirical documentation of explicit teacher support with middle and high school students’ SRL has not been substantially documented, research at the elementary level suggests that these teachers do, and should, support SRL. Nonthreatening evaluation practices, involvement in complex reading and writing activities, the provision of autonomy related to what the students read and write about, and the ability to modify learning tasks to control challenge are all pedagogical practices observed of elementary school teachers that promote SRL [64].

One common thread among the empirical findings from different groups of teachers is the existence of individual differences in how they support SRL. While the findings suggest some differences between groups of teachers, there are also distinctions within these groups of teachers. The findings suggest that personal beliefs explain these individual differences, an assumption that is supported by previous work. Sugrue [70] argued that teacher’s beliefs are the latent foundation for their behaviors and instructional decisions, a notion that has been supported by various lines of research (see [71–75]). For example, teachers who hold personal beliefs that authority figures have knowledge that is otherwise inaccessible may resort to classroom practices that do not explicitly support SRL, such as lecture/direct instruction. Furthermore, previous research suggests that teachers’ beliefs concerning student capacities affect implementation and planning of instruction [76]. For example, teachers are more likely to integrate student-centered activities in their instruction planning if they believe their students have the capacity to be active participants in their own learning. Taken together, future research would be well served to consider the interaction between the personal beliefs of inservice and preservice teachers and their instructional support of SRL.

6.3. How Is Self-Regulation Measured in Research That Examines How SRL Is Supported in the Classroom?

The empirical research reviewed for the first two research questions illustrates how teachers support SRL in the classroom. Clearly self-regulation affects learning, and thus the teachers’ role in supporting SRL is an important topic to empirically explore. Critical examinations of the teacher’s role, though, are incomplete without consideration of the methodology behind the research. Researchers have used a variety of measures to examine SRL in the classroom, each of which reflects a distinct perspective of the underlying properties of self-regulation. Thus, a review of how teachers support SRL needs to also examine the underlying methodology. Winne (1997) and Winne and Perry [22] proposed that SRL could be viewed as having one of two properties, aptitude or event. An aptitude is a relatively enduring trait of an individual, which can be used to predict future behavior [13]. Based on this assumption, self-perceptions are considered valid measures of SRL. These perceptions often are derived from self-report questionnaires [22]. Relatively easy to administer and score, self-report questionnaires are an efficient tool in measuring students’ self-perception of how they regulate their learning. On the other hand, viewing self-regulation as an event suggests that SRL unfolds within particular contexts and self-regulatory processes are dynamic unfolding events [13]. Several different protocols have been used to measure SRL as an event, including error detection tasks, observations, concurrent and retrospective think-alouds, and diaries.

A majority of the reviewed studies assumed SRL is a stable characteristic (i.e., an aptitude), as evidenced by their use of self-report questionnaires. The Motivated Strategies for Learning Questionnaire (MSLQ) has received considerable attention within this body of research. This self-report questionnaire includes declarations and conditional relations and was developed to assess “college students’ motivational orientations and their use of different learning strategies for a college course” ([77]; page 3). Kramarski and Michalsky [66] used the MSLQ to investigate the effect of metacognitive prompts in a web-based learning environment for 144 first-year preservice teachers. Results from this self-report questionnaire found that supporting the participants’ through the evaluation phase [16] was the most effective approach for fostering their perceived SRL in both learning and teaching contexts. Research has also used the MSLQ to examine the effect of diaries on preservice teachers development of SRL. Arsal [78] presented data that suggests preservice teachers’ metacognition and time management can be improved by asking them to self-report their engagement in SRL use with a daily diary. Others have used revised versions of the MSLQ, such as Hwang and Vrongistinos [68]. The College Students’ Self-Regulated Learning Questionnaire (CSSRQ) is a revised version of the MSLQ and consists of 93 items related to seeking help, time management, regulatory process, metacognition, critical thinking, organization, elaboration, rehearsal, self-efficacy, causal attributions, task value, extrinsic motivation, and intrinsic motivation. All items are answered on a Likert scale ranging from 1 (not at all true) to 6 (very true). This study found that the academic performance of inservice teachers is positively related to self-reported use of SRL processes. High-achieving inservice teachers were more likely to engage in elaboration, metacognition, and other self-regulatory processes. Lombaerts et al. [67] used a different self-report questionnaire, the Self-Regulated Learning Inventory for Teachers (SRLIT) to assess elementary teachers’ perceptions of SRL practices (Lombaerts, Engels, and Athanasou, 2007). The SRLIT consists of three subscales that represent Zimmerman’s SRL model [16]: forethought, performance control, and self-reflection. The questionnaire contains 23 items answered on a six-point Likert scale ranging from 0 (never) to 5 (always). Results indicated that while demographic and background variables did not affect teachers’ SRL recognitions, teacher-level variables had a positive impact. Beliefs concerning the influence of SRL in elementary school settings and school context satisfaction both positively are related to the teachers’ self-reported SRL recognition.

Other lines of research have taken a different methodological approach by assuming SRL is a dynamic event that should be captured in real time. For example, Perry et al. [30] used observations of mentor and student teachers, videotapes of professional seminars, and samples of student teachers’ reflections on lesson plans to capture how beginning teachers support SRL. Perry and colleagues [30, 79] have argued that process data, such as observations and other running records, address many of the challenges of measuring SRL in a valid manner. Observations and running records allow for the measurement of self-regulation in real time and enable the researcher to accurately identify behavior and classroom contexts that effectively support SRL. These measurements do not rely on the teacher or students’ ability to predict how they will support SRL or use self-regulatory processes in the classroom, making these measurements ostensibly more accurate. Davis and Neitzel [69] have also used observational data to examine upper-elementary and middle school teachers’ conceptions of their classroom practices. This methodological approach revealed that the teachers generally did not create an environment that optimally supported the development of their students’ SRL, despite the teachers’ deep understanding of classroom assessment.

7. Discussion

7.1. Overview

Theoretical assumptions that individual differences exist in student learning have been supported by empirical research. Paper, such as the one provided by Paris and Paris [32], offer a synthesis of this research and provide critical implications for how teachers can support their students’ learning. Our goal in writing this paper was to extend the current research by summarizing studies that empirically examined SRL with preservice and/or inservice teachers. The following research questions guided the scope of this paper: (1) What implications do the literature provide for supporting SRL in teacher education programs? (2) What implications do the literature provide for supporting SRL with different kinds of teachers? (3) How is SRL measured in research that examines self-regulation in the classroom? The studies that were reviewed for these three questions provide theoretical, methodological, and practical implications for research that focuses on how teachers can support SRL.

7.2. Theoretical Implications

While a variety of SRL theories have guided research in this area, Zimmerman’s [16] theory has been the most frequently cited in the reviewed studies. For example, Perels et al. [65] used this theory to guide their research on the effect of training 35 German kindergarten teachers on developing their own self-regulation skills and SRL within their students. Results are similar to that of Perry’s research. Findings suggest that students as young as kindergarten have the capacity to self-regulate and training can effectively support teachers’ ability to create classroom environments that foster SRL. Others have used Zimmerman’s theory to examine the SRL of experienced teachers, including Hoekstra et al.’s [51]. This study examined changes in SRL of 32 teachers in informal learning environments. The findings suggest individual differences regarding how teachers change their SRL orientation. The extent to which teachers reflected on the effectiveness of their lessons seemed to positively correlate with conceptions of SRL. Zimmerman’s [16] perspective of SRL has understandably been the predominant theory in this line of research. It offers a robust, explanatory lens that articulates the bidirectional interaction with students’ personal and behavioral characteristics and their environment, as evidenced by both Perels et al. [65] and Hoekstra’s [51] studies.

Other SRL theories have received less empirical attention in this line of research but are worth considering. Winne’s model (i.e., [21, 22]), for example, provides unique assumptions that should be more closely examined with research considering the broad question of how teachers can support their students’ SRL. This particular model of SRL stresses the role of metacognitive monitoring in the process of self-regulation. When students engage in metacognitive monitoring, they identify potential discrepancies between any teacher and/or student set goals and their current profile on a task [19, 20]. As such, metacognitive monitoring provides internally generated feedback, which assists students in adapting their SRL. Furthermore, metacognition allows students to regulate and govern task execution and is a critical process in the acquisition and retention of knowledge (Ku and Ho, 2010). As Michalsky and Kramarski [43] have argued, teachers with more developed metacognitive ability themselves will be better equipped to support this critical aspect of SRL. While Kramarski’s body of research has provided robust findings on types of metacognitive scaffolds that can foster preservice teachers’ ability to use processes such as self-monitoring, there remains a paucity of research that is guided by Winne’s model of SRL (i.e., [21, 22]).

7.3. Methodological Implications

In addition to theoretical considerations, the reviewed studies also provide methodological implications. A vast majority utilized self-report questionnaires to measure SRL, most notably the MSLQ [77]. This methodological approach assumes that self-regulatory processes are stable. Furthermore, self-report measures assume that both students and teachers can accurately report how they engage in the learning and teaching process. However, Perry et al. [79] suggest that self-regulatory processes should be examined in real time because SRL is an ongoing process that unfolds within particular contexts. As such, recent research has advocated that SRL should be considered an event and that self-regulation data should be collected during learning [22, 63, 80–82]. A smaller body of the reviewed studies utilized this type of process data. For example, Perry and colleagues’ have successfully employed observations to measure both teachers and students’ SRL, even with students as young as 2nd grade [63]. Other forms of process data may provide additional measures to successfully capture SRL in the classrooms.

The think-aloud, which has recently emerged as a useful protocol to measure SRL with emerging technologies (e.g., [4, 8, 11, 12, 83, 84]), offers an additional approach to capturing SRL in the classroom. This protocol is an on-line trace methodology that captures SRL during learning [80]. The think aloud has an extensive history in cognitive psychology and cognitive science (see [85–87]), where both concurrent and retrospective think-aloud protocols are used as data sources for cognitive processes [88]. While the think-aloud protocol has been most popular in reading comprehension [89, 90], it has been shown as an excellent tool to gather verbal accounts of SRL and to map out self-regulatory processes during learning (e.g., [13, 84]). Concurrent think-aloud protocols may be most appropriate with empirical research examining how preservice teachers use SRL. A concurrent think-aloud protocol asks participants to verbalize their thoughts, but not describe or explain what they are doing, while performing a task [86]. Based on the assumption that thought processes are a sequence of states and that information in a state is relatively stable [85] (Ericsson and Simon, 1993), verbalizing thoughts during learning will not disrupt the learning process. Empirical evidence has supported this assumption and suggested that an appropriately designed experimental session with a concurrent think-aloud protocol will not significantly affect cognitive and metacognitive processes during learning (i.e., [91–93]). An alternative approach is a retrospective think-aloud protocol, which involves participants verbalizing their thoughts following the completion of the task. For example, a teacher’s lesson would be video and audio recorded without any disruption from the researcher(s) (other than the recording). Following the completion of the lesson, the teacher would watch the video and verbalize thoughts as they relate to how he or she supported SRL in the classroom. The teacher’s firsthand account of how she or he supports SRL in the classroom diminishes potential validity issues associated with self-report questionnaires.

7.4. Practical Implications

Students’ ability to actively engage with the learning material, such as setting appropriate goals, accurately monitoring their emerging understanding, and adapting the use of strategies, are critical competencies that should be a central, explicit aim within education [23]. Despite the importance of these self-regulatory processes, several of the reviewed studies suggest that explicit instruction of SRL is often rare. Veenman et al. [54], for example, found that the occurrence of embedded instruction of cognitive strategies was rare in high school classrooms. Why might teachers rarely integrate explicit SRL instruction into their lesson plans when it is shown to be effective? The reviewed studies indicate that this answer is not related to the effectiveness of training, professional development, and/or scaffolding. Empirical research has demonstrated that professional development programs are effective in improving teachers’ ability to explicitly teach SRL within their classroom (e.g., [62]). Furthermore, Perry and colleagues provide robust evidence that student teachers can improve their ability to create classroom tasks that offer autonomy, and nonthreatening self and peer evaluations, as well as control challenge with the assistance of direct scaffolding from expert teachers. If, then, professional development and scaffolding can support inservice and preservice teachers’ ability to create classrooms that support SRL, what accounts for an apparent rarity in this type of instruction [49, 54]?

Research suggests that changes in experienced teacher’s support of SRL in the classroom are related to their willingness to experiment with new teaching methods and active reflection on the effectiveness of various teaching methods [51]. Furthermore, empirical findings indicate that instructional practices are significantly associated with personal beliefs (e.g., [74]). Sugrue [70], for example, found that teachers’ beliefs are the latent foundation for their behaviors and instructional decisions, a notion that has been supported by various lines of research (see [71, 72, 75]). Bruning et al. [76] further suggests that teachers’ behavior is directly aligned with their beliefs concerning specific components of the classroom, including beliefs about course content and teaching. Teachers’ treatment of course content is, in part, dependent on their views about the nature of knowledge. Collectively, these beliefs represent personal epistemology, a field of study that has enjoyed a long history (Perry, 1970). Originally describing the understanding of knowledge as a progression from dualistic to relativist thinking, the field of epistemology has evolved and models have emerged suggesting that epistemology is composed of distinct dimensions (e.g., [27, 94–99]). Teachers’ beliefs regarding teaching, on the other hand, relate to the implementation and planning of instruction [76], which is affected by a teacher’s personal epistemology. Take, for example, a hypothetical teacher who has a more naïve personal epistemology and thus believes that knowledge is certain and absolute. This particular teacher would be more likely to resort to didactic instruction, which is not a characteristic of classrooms that support SRL. While this hypothetical teacher may have the capacity to learn how to support SRL in the classroom through a training program, personal beliefs mediate the teacher’s willingness to do so. Thus, it would stand to reason that any SRL professional development for inservice teachers and direct SRL instruction to preservice teachers should be accompanied by consideration of their personal epistemologies. The formulation of personal beliefs in teacher education programs can create the foundation that guides teachers’ behavior in the classroom.

In addition to considering personal beliefs of teachers, successful implementation of learning tasks that support SRL requires careful consideration of students’ needs and abilities. A number of the reviewed studies support the notion that explicit SRL instruction has positive effects in the classroom. Kistner et al. [53] found that high school math teachers’ explicit instruction of SRL was positively related to their students’ performance, findings that were echoed in middle school students (e.g., [57]) as well as elementary students (e.g., [62, 63]). However, the reviewed studies also provide empirical evidence that explicit SRL instruction may not always benefit students. Ee et al. (2010) found a negative relationship with this type of instructional practice and the motivation of high-achieving students. The researchers suggest that the students in this study were all high achieving and had reached automaticity with cognitive strategies. Thus, the execution and retrieval of cognitive strategies for these students did not require the use of any of the working memory resources [100]. Automaticity bypasses the limited space associated with working memory and allows cognitive resources to be used in other capacities. In other words, students who already have the capacity to use cognitive strategies may have adverse reactions to explicit instruction with these SRL processes. As with any other classroom practice, optimal SRL instruction requires the consideration of students’ individual differences with their self-regulation ability.