Allyson Fiona Hadwin. 21st Century Education: A Reference Handbook. Editor: Thomas L Good. Sage Publication. 2008.
Early psychological theorizing about learning was grounded in behaviorism. Behaviorism posited that human behavior is shaped and controlled externally through stimuli and reinforcers. In contrast, contemporary perspectives recognize that people are not just shaped by environmental input, but also play a role in controlling their own learning and outcomes. This ability to control and shape outcomes is often called human agency. In his social learning theory, Albert Bandura suggested that agency, or the capacity to intentionally plan for, control, and reflect upon our actions, is what makes us human. Understanding the processes by which learners set goals, plan for, execute, and reflectively refine and adapt learning is a primary focus of self-regulated learning theory. In recent years, self-regulated learning has been recognized both nationally and internationally as an important construct in education. It has attracted the attention of researchers and educators alike. As a result, a variety of theories and models have emerged to describe the role of self-regulated learning in academic success.
Defining Self-Regulated Learning
Self-regulated learning is the deliberate planning, monitoring, and regulating of cognitive, behavioral, and affective or motivational processes toward completion of an academic task. During this process students are guided by environmental or contextual features of the task and by their own personal beliefs and goals. Self-regulated learners are intentional learners who choose targets or goals and make plans for their learning. These learners are strategic in adopting and adapting a range of tools and strategies to improve learning. They check their progress and intervene when things are not going as planned. One way to think about self-regulated learning is to think of conducting experiments about your own learning. You identify a problem, make hypotheses, and set goals. To achieve those goals, you make plans and set procedures in action. As you are working, you collect informal data about how things are going. By tracking what is happening to your initial goals and hypotheses, you draw conclusions about your progress. Based on your conclusion you may make changes to your initial plans and procedures or revise previously set hypotheses. Thus, when things do not go as planned, you begin another cycle of experimentation. When this recursive process is applied to learning, we call it self-regulated learning.
Zimmerman (1989, 2000) describes self-regulated learners as those who are motivationally, cognitively, and behaviorally active in their own learning. These learners persist when faced with difficulties or challenges and experiment or test different strategies in order to optimize learning outcomes. Self-regulated learning is also conceptualized as a lifelong process, meaning SRL develops and improves over time and across tasks. Perhaps more important, research shows that students can learn to become self-regulated; productive self-regulation is learned and refined under supportive instructional conditions.
Models of SRL share some central features including (a) agency and goals, (b) metacognition, (c) motivation, (d) adaptation or regulation, and (e) feedback.
Agency, Goals, and Self-Regulated Learning
Human beings are by their very nature agentic. This means learning does not merely happen to students. Instead, learners have the capacity to take control of their learning by intentionally planning and controlling learning processes and products. The agentic nature of learning implies that all learners set goals for themselves; they have ideas about what they are trying to do or to achieve. Those goals may or may not be in line with the goals others set for them; nevertheless, personal goals guide decision making and learning activities.
Goals exist at multiple levels, meaning students may have life goals (I want to be an engineer), course goals (I want to learn as much as I can in this course), and task goals (I want to understand this chapter), each of which shapes learning engagement. We often talk about goals that are far from the specifics of the task (e.g., life goals and course goals) as distal goals and goals that are specific to the current task (e.g., I want to understand this chapter) as proximal goals. Learners also hold multiple goals. For example, a learner may have goals about affect (I want to lower my anxiety while studying), goals about performance (I want to get all the questions correct), and goals about cognition (I want to remember this theory).
Finally, goals may be about processes or outcomes. Process goals outline the ways in which a student can attain a desired eventual target. They provide criterion for properly executing components or steps in a task. When students engage in a rather complex task, they may be aided by breaking the task into smaller steps and setting these steps as “goals in process.” For example, in writing a research paper for a course, students can set small goals, such as deciding a topic, conducting a literature search for two hours a day, and organizing the literature, and use these goals as their criterion for proper execution of their essay writing. These goals are most effective in early stages of the mastery process.
Outcome goals are the student’s desired eventual goals. They provide criterion for examining performance. For example, a student may have an outcome goal to produce a 500-word essay. Outcome goals may be more useful later in the mastery process because they also provide an indirect measure of process attainment.
Overall, goals provide important criteria or standards upon which self-regulating learners can monitor or self-check their performance and progress during, and after, learning. Without goals, there would be nothing to self-monitor, and without self-monitoring, students would be unable to regulate, adapt, or adopt strategies to realign their learning. When we talk about agentic or intentional learning, we always assume intent translates into some sort of goal, and goals in turn drive self-regulated learning.
Metacognition, Self-Monitoring, and Self-Regulated Learning
Metacognition is a common contemporary term in the field of educational psychology. At its most simple level, it is knowledge about cognition (metacognitive knowledge) and regulation of cognition (metacognitive control or regulation). Flavell (1979) was among a group of researchers to introduce the notion that metacognition and metacognitive monitoring play important roles in learning and the development of learning strategies.
Metacognitive knowledge refers to what learners know and believe about themselves, the task, and strategies for completing the task. The belief that “I am not very good at this type of task” is an example of metacognitive knowledge. Metacognitive knowledge is essentially information in memory that may or may not be activated consciously or unconsciously. Furthermore, it may or may not be accurate. Nevertheless, it is widely accepted that metacognitive knowledge plays a central role in self-regulated learning by influencing goals and plans, strategies, and motivational engagement in tasks.
Metacognitive monitoring refers to self-checking thought processes and existing knowledge in order to make evaluations about how well you are doing when measured against a desired set of standards. Many researchers describe metacognitive monitoring as the hub of self-regulated learning. Monitoring in self-regulated learning is more than just cognitive monitoring. It involves a sophisticated checking system, in which learners compare where they are with where they should be (or would like to be) with respect to different kinds of goals. Thus, self-regulated learners monitor progress with respect to goals and standards associated with cognition, motivation, behavior, and affect.
Metacognitive control refers to exercising human agency to adapt or change (a) cognitive conditions, such as beliefs and self-knowledge; (b) task conditions, such as number of available resources; (c) thinking and engagement, such as moving from memorizing to translating ideas; and (d) standards, such as the acceptable level of mastery for this task. Metacognitive control is believed to be exercised as a result of information generated through metacognitive monitoring. Metacognitive control is exercised when monitoring uncovers a discrepancy between desired and actual state whether it be with respect to behavior, motivation, or cognition. Exercising metacognitive control is the act of adapting or regulating specific aspects of learning.
Motivation, Self-Efficacy, and Self-Regulated Learning
Although there is some debate about how motivation weaves into self-regulatory processes, there is fairly consistent agreement that motivation plays an important role in self-regulated learning. Motivation is a complex construct. It includes outcome expectations, judgments of efficacy, attributions, and incentives or values.
There are at least three ways motivation weaves into self-regulated learning. First, motivational knowledge and beliefs inform self-regulated learning by influencing the kinds of goals that are set, strategies that are chosen, and even persistence in a given task. For example, if a student feels anxious about a writing assignment, that anxiety might influence the kinds of goals the student sets for successfully completing the task. A student with high anxiety might lower goal expectations for performance and weight a goal to reduce anxiety as a parallel and equally weighted goal. Second, self-regulatory engagement in learning produces new motivational knowledge and beliefs that shape future engagement in both the current and future tasks. When a strategy for managing time during a writing activity is unsuccessful, a student may revise personal beliefs about how successful she or he can be in this writing activity or generate an anxiety response about finishing the writing task at all.
Finally, students can self-regulate motivational states during learning. For example, when a student experiences initial anxiety about a writing task, she or he may search memory for strategies that might help alleviate anxiety (such as positive self-talk). In this example, the student’s perceptions of affect were not in line with his or her standards for the acceptable level of anxiety in a given task. As a result, she or he evaluated that there was a problem and experienced the drive or motivation to change the level of anxiety.
Self-efficacy is a motivational construct that is frequently discussed and researched in the field of self-regulated learning. Bandura (1977) first introduced the importance of self-efficacy in learning. Self-efficacy is one’s beliefs about one’s capabilities to successfully perform a specific task to a specific level of competence. For example, a student with high self-efficacy for writing believes that he or she is able to produce a high-quality essay. Self-efficacy beliefs influence choice of task, performance, persistence, and level of effort or engagement. Consistent with other forms of motivation described above, self-efficacy beliefs (a) can influence the ways students engage in a task (e.g., engaging minimal effort in choosing and adapting effective strategies); (b) can emerge as a product or outcome of self-regulatory engagement in a task (e.g., generating a low-efficacy judgment for future writing tasks after encountering planning challenges that were not successfully overcome); and (c) can be regulated by the learner (e.g., engaging positive self-talk about what can be successfully accomplished).
Feedback and Self-Regulated Learning
Feedback is an essential component of self-regulated learning. Feedback can be self-generated in the form of self-evaluations or generated by others. The product of self-monitoring during task engagement is internal feedback. As students monitor their progress against a set of motivational, behavioral, or cognitive standards, they generate feedback about how they are doing with respect to those standards and evaluations of what is or is not working. Furthermore, when self-regulating students judge that things are not going well, or in more sophisticated terms that a discrepancy exists between current and desired performance, they may seek out additional external feedback. External feedback may include comments from friends, comparisons with peers, or remarks from the teacher.
Feedback provides information students can use to revise, add to, or replace knowledge about strategies, tasks, beliefs, self, and metacognition. In this way, it is a necessary component of the self-regulatory cycle. Feedback can help students more accurately attune their understandings of a task, revise and reshape goals, evaluate the effectiveness of strategies, and revise knowledge of themselves as learners. While self-monitoring generates internal feedback for the learner, it may be best complimented with other forms of external feedback that help learners better attune their self-monitoring activities and the goals and standards upon which they base self-evaluations.
Models of Self-Regulated Learning
Although self-regulated learning was introduced well over 20 years ago, research has increased exponentially since Zimmerman and Schunk’s book (2001). Because many models of self-regulated learning exist, only a sampling is presented below.
Zimmerman’s Sociocognitive Model
Zimmerman (1989, 2000) built upon Bandura’s (2001) sociocognitive model to propose a triadic model of self-regulated learning that is well recognized in the field of educational psychology and beyond. Zimmerman’s model of self-regulated learning posits three basic forms of self-regulation: environmental, behavioral, and personal (covert) self-regulation. Environmental self-regulation is students’ regulation of their social and physical surroundings. For example, a student studying for an exam may turn off the television (physical) or approach a peer or teacher for help (social). Behavioral self-regulation refers to students’ control over their overt activities. For instance, a student may keep a written record of the number of hours he or she has studied each week. Personal (covert) self-regulation pertains to the regulation of cognitive beliefs and affective states; for instance, a student may set a self-evaluative standard to increase his or her motivation. These three influences interact reciprocally in a feedback loop (also called triadic feedback loop). As such, students must be sensitive to variations in their environmental, behavioral, and cognitive states in order to be optimally self-regulating. Furthermore, Zimmerman identified three important characteristics in self-regulated learning: self-observation (monitoring one’s activities), self-judgment (self-evaluation of one’s performance), and self-reactions (reactions to performance outcomes).
Winne and Hadwin’s Recursive Model
Winne and Hadwin (1998) proposed a model of self-regulated learning as studying. This model depicts self-regulated learning as unfolding over four flexibly sequenced and recursive phases of learning. In the first phase (Task Understanding), students construct a representation of a presenting academic task. This representation draws on task, context, self, and motivational knowledge to produce a personalized profile of the task at hand. This task profile is influenced by motivational conditions (e.g., I am not very good at this type of task), task conditions (e.g., I don’t have much time to complete this), cognitive conditions (e.g., knowledge about this topic or concept area), and metacognitive conditions (e.g., knowledge about tactics and strategies that I can draw upon when I get stuck). The task perception generated by a learner is personalized; this means it represents his or her understanding of the task and may not match the interpretation that was intended by the teacher or instructional designer.
After constructing a personalized interpretation of the task, goals are set and plans for engaging in the task are initiated (Phase 2). Goal setting and planning affords opportunities for students to distinguish between what they think they are supposed to do (task perceptions) and what they want to actually set out to do or achieve. Goal setting involves constructing a profile of standards for what you want to achieve and choosing methods to achieve those goals.
The third phase puts that plan into action. Sometimes this phase involves testing different plans and strategies or balancing between multiple competing goals. For example, students might balance the goals of researching a paper thoroughly with the goal of finding enough time to practice for an upcoming soccer tournament.
Ideally, in a fourth phase students adapt or regulate their studying processes. Adaptation might be backward reaching, such as going back to review the task and update task understanding. For example, when students notice that they seem to be doing something different than the rest of the class, they often go back to consult the initial instructions or task description. Adaptation may also be forward reaching, such as changing beliefs or perceptions about ability to successfully complete this kind of task in the future. This model of self-regulated learning is recursive, meaning that monitoring during any phase might trigger adaptation in previous or subsequent phases.
Pintrich’s Model of Self-Regulated Learning
Pintrich (2000) presented a framework that describes self-regulated learning as unfolding across four weakly sequenced phases with four intersecting areas for selfregulated learning. The four phases include (1) forethought, planning, and activation; (2) monitoring; (3) control; and (4) reaction and reflection. These phases are proposed to reflect a time-ordered sequence through which learners progress when working on a task, but the model does not propose that in practice phases must occur linearly or hierarchically. An important contribution of this model of SRL is that it attempts to tease apart how these phases evolve and are instantiated across four different facets or areas of self-regulation including cognition, motivation and affect, behavior, and context. In this way, it is acknowledged that learners plan for, monitor, control (often by applying strategies), and evaluate aspects of their learning extending well beyond just cognitive engagement. Cognitive strategies that students select and evaluate help them to learn and think, motivation strategies help them to manage motivation and affect, behavior strategies help them to increase and decrease effort, and contextual strategies help them to renegotiate tasks, change, or leave the context. Controlling and regulating strategy use for specific goals across these areas constitute the complex task of self-regulating learning.
Boekaerts’ Model of Adaptable Learning
Boekaerts (2006) proposed a holistic model of self-regulated learning that attempts to account for the interaction between interrelated processes in SRL, including metacognitive control, motivation control, emotion control, and action control. This model of SRL proposes that individuals hold two main priorities that influence their behavior regulation: (a) expanding personal resources by developing and improving knowledge and skills (mastery) and (b) protecting or maintaining available resources to prevent loss, damage, and distortions (coping). Self-regulated learning from this perspective appraises and coordinates three sources of information in a dynamic internal working model: task and context information; cognitive and metacognitive knowledge and skills information; and self-system goals, knowledge, and beliefs.
In this model appraisal is unique to every learning task and situation, and serves a central role in directing goal-setting and goal-striving activities. An assumption in this model is that positive appraisals lead students to invest energy in increasing competence (a mastery mode) by adopting learning strategies. Negative appraisals lead students to prevent loss of resources and protect their ego (a coping mode) by adopting coping strategies. From this perspective, successful self-regulation sets goals, applies control to deal with success and failure, and balances goals related to mastery with those related to coping in order to optimize learning outcomes while preserving a sense of self.
Contrasting Models of Self-Regulated Learning
Each of these models of self-regulated learning attempts to grapple with the roles of intention and control in human learning. Each model acknowledges that learning unfolds in cycles that include (a) preparation or taking stock of the current situation in terms of the task, knowledge, and motivation, and making decisions about desired directions, goals, or states; (b) action or adoption of strategies for regulating or enacting change; and (c) reflection or appraisal that perpetuates evolution and change in current and future learning. That is to say, self-regulated learning across each of these models is concerned with the deliberate planning, monitoring, and regulating of cognitive, behavioral, and affective or motivational processes toward completion of an academic task.
Applying Self-Regulated Learning to the Classroom
Designing Classroom Tasks and Contexts
Not all classroom tasks and contexts are equal in guiding or promoting strategic self-regulated learning. Perry (e.g., 1998) has conducted numerous research studies to explore the types of tasks and contexts that support self-regulated learning. Perry’s work demonstrates that even very young children can learn to self-regulate given the right instructional context. Six recommendations provide a framework for supporting SRL in the classroom.
Design Complex Tasks
In order to self-regulate learning, students need to be confronted with complex tasks that evolve over time. Although teachers may be reluctant to assign challenging and ambiguous tasks for fear of overwhelming young students, research has demonstrated that students are motivated by tasks that pose achievable challenges. This is consistent with Vygotsky’s notion that optimal development occurs in a zone of proximal development; tasks should be set at a level between what a person can accomplish working alone and what can be accomplished with support or guidance from others.
One reason challenging tasks are optimal for promoting self-regulated learning is that they provide opportunities for students to make choices and engage in decision making about their learning. Choices might concern the nature of the task itself, such as choosing to work alone or with a partner, or choosing what items to include in a learning portfolio.
Control over Challenge
Self-regulated learning is also supported when students are given choices to control the level of challenge of a task. When students have opportunities to control challenge, they learn to choose and set suitable goals based on their understanding of the domain, self, task, and context. For example, an occasion to work with peers gives students the opportunity to reduce the level of challenge by seeking help from a peer. Also, students have opportunities to control challenge when they can choose among tasks of differing levels of difficulty or when they have access to additional help resources as needed.
To self-regulate learning, students need to be encouraged to monitor and evaluate their own progress rather than relying solely on external evaluation. Self-evaluation is important because it requires students to revisit task perceptions and articulate goals and standards they are using to evaluate themselves. In addition to helping students regulate their learning, self-evaluation provides a window into student task understanding and goal setting that may lead to instructional opportunities that help students better attune their understanding and implementation of tasks, strategies, and goals. Self-evaluation might be as simple as answering the questions, How am I doing? How do I know? Or it may be as complex as reflecting on the usefulness of a new learning strategy for promoting key thinking processes associated with understanding and remembering (selecting, monitoring, assembling, rehearsing, translating). Self-evaluation that encourages students to reflect on personal progress has potential to improve future learning because seeing mistakes as opportunities supports students to embrace a cycle of self-regulated learning.
Instrumental Support from Teachers
Instrumental support is the act of guiding students as they encounter difficulties by providing just-in-time instruction and scaffolding. In contrast to direct instruction, instrumental support is strategically timed to help students develop the necessary knowledge and skills as they encounter challenges. Furthermore, it is temporary support provided with the ultimate goal of helping students successfully complete tasks on their own. Teachers provide instrumental support when they engage students in discussions about tasks, use open-ended and thought-provoking questions, and cue students to think about aspects of their own self-regulatory activity. Instrumental support helps students to think about and generate accurate perceptions of tasks, identify and articulate learning goals to guide their progress, consider their own strengths and weaknesses with respect to tasks, and strategically adopt and revise strategies. Instrumental support is also important for helping control the challenge of a task.
Support from Peers
Much like instrumental support from teachers, peer support can help students control the level of challenge associated with a task and provide opportunities for self-evaluation and self-reflection. Peer support is important for self-regulated learning because peers model strategies and metacognitive thinking for one another.
Promoting Effective Task Interpretation
Developing accurate and complete understandings of assigned tasks is essential for successful performance and constitutes an important component of self-regulated learning. But academic tasks are often difficult to understand because they are layered with explicit and implicit information (often not well described), deeply embedded with disciplinary thinking and presentation genres, and use language that can have many meanings. Butler and Cartier (2004) have proposed research-based guidelines for instructors to promote effective task interpretation. Not surprisingly, these guidelines complement recommendations presented in the section above on designing classroom tasks and contexts.
Selection of Learning Activities
Accurate interpretation by students is built on thoughtful design and reflection by teachers. When teachers design a learning activity, they might consider the following: (a) their goals for student learning and how those goals relate to the task, (b) what specific tasks are required and which are desired, and (c) what is communicated about the nature of academic work and thinking through descriptions of tasks. For example, when creating an engineering design project, teachers might consider if the description of the design task conveys a commitment to helping students develop specific competencies related to grappling with ill-defined and authentic engineering problems.
Structure of Instruction
It is also important to be explicit about more than the task criteria or procedures themselves. Students need instruction that supports developing accurate perceptions of the kind of thinking that is encouraged and valued; strategy knowledge that should develop or be demonstrated; and the kind of metacognitive knowledge, monitoring, and evaluation that should be engaged during task completion.
Evaluation and feedback provide important information to students about the nature of the task and strongly influence student interpretations of tasks. In order to help students interpret tasks, evaluation criteria should be aligned with task purposes. When students are encouraged to engage in self- and peer-evaluation activities, they are forced to grapple with the specifics of the task and to clarify task interpretations. When students are asked to generate grading rubrics, they must also clarify and make public their task interpretations. When students are asked to translate or interpret feedback that they receive about an academic task or assignment, they are invited to monitor and evaluate task interpretations. These types of activities assign instructional time and value to the act of task interpretation and afford opportunities for students to successfully self-regulate their learning processes by starting with goals and standards that are in line with the instructors.
Strategy Instruction and Interventions to Enhance Self-Regulation
While some debate exists in the literature about the need for direct strategy instruction, there is consensus students need to develop strategy knowledge to self-regulate learning.
Self-Regulated Strategy Development Approach (SRSD)
SRSD is an instructional approach for supporting self-regulated strategy development (cf. Harris, Graham, & Mason, 2003). Although the implementation and evaluation of SRSD has focused primarily on writing instruction and students with learning disabilities, SRSD has been successfully implemented in other domain areas such as reading and mathematics. Extensive research about SRSD has demonstrated its effectiveness. Teachers can easily apply the instructional approach in large class instruction and small group tutoring, and students trained using the SRSD approach improve the quality of their writing, knowledge about writing, approaches to writing, and self-efficacy about writing tasks.
SRSD has three main goals: (a) developing strategy knowledge, (b) supporting students to monitor and regulate strategy use, and (c) promoting self-efficacy and positive beliefs. As students engage in flexibly sequenced SRSD stages, they are continually encouraged to use strategies in other contexts, monitor and reflect upon successes and failures in using strategies, and revise and modify strategies accordingly.
SRSD consists of six main stages that frame strategy instruction. Stages may be ordered differently, combined and modified to suit particular students and contexts. Stage 1 (develop and activate background knowledge) focuses on developing the knowledge and skills necessary to use strategies and self-regulated learning procedures. In writing, students might be reminded of story-planning strategies they have used before or a planning strategy for getting started with writing. In stage 2 (discuss it) the teacher and students discuss strategies to be learned including steps or procedures in implementing the strategies; ways of remembering the strategy, such as a mnemonic; and how, when, and why to use the strategy. In stage 3 (model it) a teacher or peer models the strategy. Emphasis is placed on sharing the kind of metacognitive thinking that accompanies strategy use such as self-talk about the strategy process, monitoring, and evaluation. Students and teachers discuss the modeling process as well as the kind of self-talk that accompanied each step or action. In stage 4 (memorize it) students commit the strategy to memory. They may do this by developing mnemonics and external memory aids. Stage 5 (support it) and stage 6 (independent performance) represent a transition from using the strategy with some external prompting and guidance to implementing the strategy independently.
Strategic Content Learning Approach (SCL)
Butler (1998) posited that in order for students to learn to monitor and regulate strategy use, they need opportunities to develop and individualize their own learning strategies. As a result, the strategic content learning approach (SCL) introduces students to a framework for strategically approaching and engaging in new tasks. It reserves explicit strategy instruction for occasions when students are not able to activate, draw upon, or revise strategies they already hold in their own repertoire. In SCL, structured and explicit instruction target the self-regulatory process rather than strategies themselves. Students are encouraged to construct knowledge and strategies with the goal of promoting strategy transfer. Discussion provides a means for sharing ideas and constructing transactional understandings about learning. Although SCL was originally extensively implemented in one-to-one tutoring sessions, it was later successfully implemented in larger group contexts.
The instructional approach for SCL follows a sequence of four steps. In step 1 (the task analysis stage), students are encouraged to analyze a task by exploring and articulating task demands, criteria, and parameters. Since many students have misconceptions about academic tasks, this step is a critical component of the SCL approach. Step 2 (strategy selection, adaptation, or invention) encourages students to make decisions about learning strategies they might use based upon the task performance criteria identified in stage 1. SCL recognizes that students often have a repertoire of strategies to choose from. Even when they do not, they are often able to use their knowledge about the task and domain to invent their own strategies. As a result, the SCL tutor encourages the student to brainstorm and occasionally provides suggestions or ideas to consider. Rather than being directed to adopt a strategy, students are empowered to make decisions about their own strategies. Students are encouraged to articulate or record these strategies as sequences of activities so that they have a reference point for monitoring strategy use. In step 3 students monitor strategy use. They are prompted to articulate what is working, what is not working, and reasons for success or challenges using the strategies. Finally, in step 4 students are supported to evaluate the effectiveness of the strategies they have used and to make necessary revisions in those strategies for future use. In this way, students begin to construct a library of their own strategies that have been individualized and self-tested.
The SCL approach to strategy instruction is designed to help students think through their own learning tasks, make decisions about those tasks and the strategies they use, and develop skills and habits associated with reflecting upon and revising strategies.
Self-Regulation Empowerment Program (SREP)
Cleary and Zimmerman (2004) developed and tested the self-regulation empowerment program (SREP), designed to help students become more self-sufficient and independent in their learning. In addition to advocating a preliminary diagnostic component to identify motivational and strategic weaknesses, the program focuses on a three-step process for modifying or improving motivational and strategic weaknesses. The empowerment step (step 1) strives to help students gain perceptions of control of their learning. Specifically, it guides students to make connections between their strategy use and success or failure outcomes. This step uses methods and tools for self-recording and monitoring specific aspects of progress, including sources of errors and success. The study/learning strategy step (step 2) supports students to increase their repertoire of strategies. Strategies are supported by modeling, coaching, and guided practice such as that described in the next section. The final step (step 3) guides students to make use of the cyclical feedback loop in self-regulated learning. Students are taught to engage in forethought (e.g., set goals, articulate plans), record performance, evaluate goal attainment, and self-reflect about how strategies helped or hindered and ways to adapt or improve the effectiveness of strategies.
Sociocognitive Modeling and Self-Regulatory Competence
Sociocognitive modeling is the process of observing more capable others as they pattern thoughts, beliefs, and strategies associated with self-regulated learning (Schunk, 1987). Students can learn to regulate their own learning when they have opportunities to observe a proficient model, participate in guided practice, and receive instrumental feedback about their learning. Modeling is most effective when it is matched to a student’s level of self-regulatory competence.
First, students are introduced to the instructions and provided with a modeled demonstration. This is followed by students engaging in a hands-on activity that is supported through guided practice. During guided practice, the model provides corrective feedback and self-regulatory training. Through self-regulatory training students are supported to verbalize goals, plans, and strategies. Modeling sessions wrap up with independent practice during which students self-reflect by verbalizing self-monitoring, beliefs, and self-evaluations.
Under the right conditions, modeling can lead to the acquisition of knowledge and strategies as well as increased self-efficacy for successfully completing tasks. Coping models who demonstrate skills and strategies for coping with stressful or challenging conditions are most effective when students have encountered failure. In contrast, mastery models who demonstrate strategies for correctly working through tasks, may be more effective under other conditions. Models are effective when observers perceive them as competent, regardless of model age, and when learners are exposed to multiple peer models. Children are more influenced by models they perceive as similar in ability to themselves.
Contemporary Issues and Future Directions
Social Aspects of Self-Regulated Learning
Historically, self-regulated learning was considered to primarily involve individual cognitive, metacognitive, motivational, and behavioral processes (Zimmerman 1989, 2000). Social context or environment was a factor influencing those individual processes. Over the last 20 years, however, increasing emphasis has been placed on understanding the role of social and contextual influences on self-regulated learning. As a result, new models and languages for describing social aspects of self-regulated learning have emerged. Although debate continues about these perspectives, awareness of these factors is important for understanding the current state of the field. Three main terms frame discussions about social aspects of self-regulated learning: self-regulated learning, co-regulated learning, and socially shared regulation of learning.
Self-regulated learning is the process of becoming a strategic learner by actively monitoring and regulating metacognitive, motivational, and behavioral aspects of one’s own learning. The focus of research is the individual learner with environment as an influence (Schunk & Zimmerman, 1997). Social context is examined separately or manipulated as an independent variable. Research on the individual aspects of self-regulated learning has relied heavily on self-reports, think aloud protocols, interviews, and various performance measures.
Co-regulated learning is the transitional process in a learner’s acquisition of self-regulated learning, during which experts and learners share a common problem-solving plane and self-regulation is gradually appropriated by the individual learner through interactions (e.g., McCaslin & Hickey, 2001). Research on co-regulated learning focuses on aspects of interaction, speech, and discourse with an emphasis on scaffolding and interdependence. Data primarily consist of interaction and discussion records. Research about co-regulated learning strives to examine the ways social practices support individuals to appropriate self-regulatory knowledge and processes. Social support in the form of scaffolding tends to take on some of the self-regulatory processes or burdens, rather than merely instructing or prompting students to engage in those processes.
Socially shared regulation of learning refers to the processes by which multiple others regulate their collective activity. From this perspective, goals and standards are co-constructed, and the desired product is socially shared cognition. Similar to co-regulated learning, interaction and discussion records are primary sources of data in the study of shared regulation. Unlike research about co-regulation, however, research about shared regulation tends to examine contributions, roles, the evolution of ideas, and the ways groups collectively set goals, monitor, evaluate, and regulate their shared social space. Examining socially shared regulation requires a shift toward new forms of instructional tools, data collection, and data analysis that acknowledge individuals as part of social entities and shared tasks.
To date, research exploring and comparing self-, co-, and socially shared-regulation of learning have not been conducted. Targets for future research include developing analytical techniques for examining shared regulation in social task spaces and experimenting with research designs and methodologies that allow transitions from the individual to the different ways in which self and social inform the regulation of learning.
Technology and Self-Regulated Learning
Current research on self-regulated learning explores ways technologies (particularly computer-based learning environments) can be used to support and research self-regulated learning. Research and discussion about computers as tools for supporting self-regulated learning include: (a) scaffolding self-regulated learning and meta-cognition in computer-based learning environments, (b) researching the design and features of pedagogical agents in supporting self-regulation, and (c) creating Web-based environments and tools that foster strategy use and self-regulation. In creating and researching instructional innovations for self-regulated learning, these new technologies afford opportunities to collect new types of data about self-regulated learning unfolding over time and across context. Software such as gStudy (Winne, Hadwin, Nesbit, Kumar, & Beaudoin, 2005) can be used to collect logfile traces of student engagement with the software. Data of this type have not been harnessed in the study of self-regulated learning but have been used in fields such as computer and information science to conduct usability testing.
New technologies afford opportunities to design and compare instructional programs, interventions, and environments as well as collect new kinds of data about students’ learning and strategy use.