Schunk, Chapter 10

Assumptions

Throughout the various theories of self-regulated learning, several assumptions are shared

• Self-regulated learning “invovles being behaviorally, cognitively, metacognitively, and motivationally active in one’s learning and performance (Zimmerman, 2001)” (Self-Regulated Learning, “Assumptions”, para. 1)
• It is a “dynamic and cyclical process comprising feedback loops (Lord et al., 2010)” (para. 1)
• Setting goals interacts with self-regulated learning “by guiding individuals’ focus on goal-directed activities and use of task-relevant strategies (Sitzmann & Ely, 2011)” (para. 2)
• Motivation to self-regulate is important

In groups, “learners jointly use their skills and strategies to develop new or expanded self-regulatory capabilities”; this is related to socially shared regulation, which “refers to interdependent regulatory processes aimed at attaining a mutual outcome (Hadwin et al., 2018)” (para. 4-5).

Behavioral Self-Regulation

The first lens through which Schunk investigates self-regulation is behavioral. From this perspective, “self-regulation involves choosing among different vehaviors and deferring immediate reinforcement in favor of delayed (and usually greater) reinforcement” (“Behavioral Self-Regulation”, para. 3). The regulation follows in three steps: self-monitoring, self-instruction, and self-reinforcement.

• Self-monitoring: This is the “deliberate attention to some aspect of one’s behavior” and often incorporates “crecording its frequency or intensity (Mace et al., 2001; Mace & Kratochwill, 1988)” (Self-Monitoring, para. 1). Regulation requires conscious awareness of the behavior, and assessment of those actions; this can occur in many distinct domains. Many methods exist: narrations (“written accounts of behavior and the context in which it occurs”); frequency counts (“instances of specific behaviors during a given period”); duration measures (“amount of time a behavior occurs during a given period”); time-sampling measures (“[dividing] a period into shorter intervals and [recording] how often a behavior occurs during each interval”); behavior ratings (estimates “how often a behavior occurs during a given time”) (para. 2). Two important criteria are regularity (“monitoring behavior on a continual basis”) and proximity (“behavior is monitored close in time to its occurence rather than long afterward”) (para. 4). Ultimately, the burden of the assessment is placed on the student, but have been shown to have good results on self-regulation.
• Self-instruction: This is the “[establishment of] discriminative stimuli that set the occasion for self-regulatory responses leading to reinforcement (Mace et al., 1989)” (Self-Instruction, para. 1). Two kinds of self-instruction are “arranging the environment to produce discriminative stimuli (para. 1) and “statements (rules) that serve as discriminative stimuli to guide behavior” (para. 1). Another is strategy instruction; this has been shown to be an “effective means of enhancing comprehension and self-efficacy among poor readers” (para. 2).
• Self-reinforcement: This is “the process whereby individuals reinforce themselves contingent on their performing a desired response, which increases the likelihood of future responding (Mace et al., 1989)” (Self-Reinforcement, para. 1). Reinforcement in this sense follows the behavioral tradition. These have been shown to be effective for academic performance, but studies are unclear about how it compares to other forms of non-self reinforcement.

Social Cognitive Influences

Under social cognitive theory, learner choice is a crucial component. Table 10.1 identifies choices with the appropriate self-regulatory process: |Choice | Self-Regulatory Processes| | ———– | ———– | | Choose to participate | Goals, self-efficacy, values | | Choose method | Strategy use, relaxation | | Choose outcomes | Self-monitoring, self-judgment | | Choose social and physical setting | Environmental structuring, help seeking| (“Social Cognitive Influences”, Conceptual Framework, Table 10.1)

Under social cognitive theory, self-regulation can be broken down into three processes (which are ultimately similar to their behaviorist counterparts): self-observation; self-judgment; self-reaction.

• Self-observation: This involves “judging observed aspects of one’s behavior against standards and reacting positively or negatively” within the environment (Self-Regulatory Processes, para. 3).
• Self-judgment: This “refers to comparing present performance level with one’s goal”, and depends on specific standards called self-evaluative standards (para. 5). These standards can be fixed (absolute) or normative (“frequently acquired by observing models”) (para. 7).
• Self-reaction: These are the perceptions to how one is progressing with goal progress. Beliefs about “adequate” progress will tend to increase self-efficacy; on the other hand, “negative evaluations do not decrease motivation if individuals believe they are capable of improving (Schunk, 1995)” (para. 17). As a corollary, motivation does not improve “if students believe they lack the ability and will not succeed no matter how hard they try (Schunk, 2008)” (para. 17).

Zimmerman (1998, 2000) created a three-phase model of self-regulated learning, which highlights its cyclical and dynamic nature. In it, performance/volitional control lead to self-reflection, which in turn leads to forethought, and back to performance/volitional control. At each set, different processes for the self-regulation come into control; these all seem fairly intuitive, given the description in Zimmerman’s model. A similar model is proposed by Pintrich (2000b): forethought, planning, activation; monitoring; control; reaction and reflection. One key element is that “within each phase, possible areas for self-regulation are cognition, motivation and affect, behavior, and context” (Cyclical Nature of Self-Regulated Learning, para. 4).

Information Processing

The nature of information processing theories lends itself well to discuss theories of self-regulation. Gitomer & Glaser (1987) equate self-regulated learning with metacognition, “where individuals monitor, direct, and regulate actions toward goals (Paris & Paris, 2001)” (“Information Processing”, Model of Self-Regulated Learning, para. 2). This “requires learners to have a sound knowledge base comprising task demands, personal qualities, and strategies for competing the task” (para. 2).

Problem-solving production systems comprise a foundation for self-regulation in some views; the Test-Operate-Test-Exit (TOTE) model developed by Miller, Galanter, and Pribham (1960) is one example of this. In it, the learner initially tests against a standard, operates to resolve discrepancy (if any), retests, and if satisfied, exits this procedure. Winne and Hadwin (1998, 2008; Winne, 2001, 2011, 2018) propose a model consisting of three primary phases (task definition; goals/plans; tactics) and an optional adaptation phase. In all of this long-term memory is accesses as a mechanism to compare previous performance against current standards.

Learning strategies “are cognitive plans oriented toward successful task performance (Pressley et al., 1990; Weinstein & Mayer, 1986)” (Learning Strategies, para. 1). Learning strategies are diverse, and include operations such as organization, rehearsing, relating, as well as specific techniques “that create and maintain a positive learning climate” (para. 1). Learning strategies figure into the Information Processing construct quite easily: “learners initially attend to relevant task information and transfer it from the sensory register to WM” and “activate related knowledge in LTM” (para. 2). Some strategies include rehearsing (which includes underlining/highlighting and summarizing), reciprocal teaching (a la the zone of proximal development), elaboration (such as mnemonics or note taking), organization (which also includes mnemonics, grouping, outling, and mapping), monitoring (self-questioning, rereading, checking consistencies, paraphrasing), and affective methods (such as self-verbalization).

Constructivism

Constructivism holds several assumptions (by nature) for self-regulated learning:

• There is an intrinsic motivation to seek information
• Understanding goes beyond the information given
• Mental representations change with development
• There are progressive refinements in levels of understanding
• There are developmental constraints on learning
• Reflection and reconstruction stimulate learning (“Constructivism”, Table 10.6)

Naturally, because constructivism takes the social component into account, the two underlying points about the assumptions are sociocultural influences and the idea that people will form “implicit theories about themselves, others, and how to best manage demands” (para. 2). For Vygotsky, self-regulation is a higher mental function: “a consciously directed thought process” (Sociocultural Influences, para. 1). This is because coordination of “memory, planning, synthesis, and evaluation (Henderson & Cunningham, 1994)” are critical elements of self-regulated learning (para. 2). Ultimately, “internalization [in the sense of Vygotsky] becomes the key to use of self-regulatory processes (Schunk, 1999)” under the constructivist model (para. 6).

Implicit theories “are inherent features of constructivist accounts of learning, cognition, and motivation” (“Implicit Theories”, para. 1). Some implicit theories include: beliefs about academic abilities; competence relative to peers; theories of agency. As a result of these theories, self-regulation “invovles individuals constructing theories about themselves (e.g., abilities, capabilities, typical effort), others, and their environments” (para. 7). These theories are grounded in social environment interaction, as well as sociocultural tools.

Motivation

Motivation and self-regulated learning have a reciprocal relationship; “people motivated to attain a goal engage in self-regulatory activities they believe will help them… in turn, students self-regulate their motivation to learn, and the perception that one is learning sustains motivation and self-regulation to attain new goals (Schunk & Ertmer, 2000)” (para. 1). Many of the theories discussed in previous sections rely on the idea of motivation to undergird the self-regulated learning process.

Volition is a construct which is particularly of interest. If “will reflect[s] one’s desire, want, or purpose”, then volition is “the act of using the will” (Schunk et al. 2014) (Volition, para. 1). Historically, there are different views about the location of volition within learning and behavior. Modern characterizations rely on action control theory espoused by Heckhausen (1991) and Kuhl (1984). The two authors “proposed differentiation predecisional processing (cognitive activities invovled in making decisions and setting goals) from postdecisional processing (activities engaged in subsequent to goal setting)” (para. 4). Motivation appear around predecisional processing while volition appears in postdecisional processing. There are two aspects to volition that should be distinguished with regard to self-regulated learning: action control (“potentially modificable regulatory skills or strategies”) and volitional style (“stable, individual differences in volition”) (para. 7-8).

Values are an important motivational aspect that impacts self-regulated learning as well. This is because “students who do not value what they are learning are not motivated to improve or exercise self-regulation over their learning activities (Wigfield et al., 2004)” (Values, para. 1). Values “have a direct link to such achievement vehaviors as persistence, choice, and performance”; they can also “relate positively to many self-regulating processes such as self-observation, self-evaluation, and goal setting” (para. 2). Two other important pieces are self-schemas (“cognitive manifestations of enduring goals, aspirations, motives, fears, and threats” (as cited in Markus & Nurius, 1986, p. 954)) and help seeking (“a way to self-regulate the social environment to promote learning (Karabenick & Gonida, 2018)) (Self-Schemas, para. 1; Help Seeking, para. 1)

Citations

Schunk, D.H. (2019). Learning theories: An educational perspective (8th ed.). Pearson.