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This contribution to the symposium focuses on our approach to support the development of self-regulated learning (SRL) expertise through a large first-year undergraduate course titled Learning Strategies for University Success. This SRL approach extends beyond teaching strategies to encourage the development of a growth mindset wherein academic challenges create opportunities to strategically take control of learning. Challenges provide opportunities for learners to engage in deliberate self-regulation and for researchers to study it in situ (Hadwin, Järvelä, & Miller, 2011). Our approach has been to support SRL processes related to students’ academic work by drawing from and reinforcing Winne and Hadwin’s (1998) cyclical model of SRL. Therefore, all data collection serves dual purposes by: (a) increasing students’ metacognitive knowledge, awareness, and control in response to learning conditions and challenges they experience while (b) examining students’ regulatory actions and adaptations across tasks and time. Academic challenges affect regulation across SRL phases, however they become particularly salient when goal progress is disrupted. Detecting disruptions in goal progress, relies on self-monitoring. This metacognitive exercise invites regulatory action: choosing, using, and adapting a strategy, or adapting something in another SRL phase. From this perspective, examining the challenges students encounter and the ways they strive to overcome them reveals information about their SRL as a process.
Participants were drawn from four semesters (N~650). Data sources include two measures designed to promote and examine regulatory responses to challenges including: (a) MyPlanner weekly diary tool, and (b) Regulated learning questionnaire (RLQ). Due to space limitations, we snapshot findings from one data source here. The MyPlanner is a weekly SRL diary tool that reveals student planning and reflection about a weekly goal. MyPlanner Part 1 was completed during class time where students set a goal for an upcoming study session in a regular academic course. MyPlanner Part 2 was completed outside of class after completing the study session. A narrative response constructor in the MyPlanner prompted students to identify and reflect on a main challenge encountered that week (see Figure 1 in PDF).
Contingent regulatory actions in response to challenge were examined using probabilistic decision maps tracking within student goal quality, challenges encountered, strategy choice, and perceived strategy effectiveness. An example of a regulatory pattern averaged across students for Week 1 is included in Figure 2 (in PDF). As demonstrated in the figure, a high proportion of students (p=.52) encountered a motivation challenge that was most commonly addressed (p=.52) by adjusting or changing at least one aspect of their learning (e.g., task understanding, goals, or learning strategy). This dominant regulatory pathway was perceived to be moderately effective (M=3.06). In the following week, students who followed this decision path were more likely to encounter a planning challenge rather than re-encountering the same motivation challenge. This change in challenge type may be an indication of an adaptive response to regulating motivation.
Findings from this study contribute to understandings of self-regulation as a complex pathway comprised of strategic contingencies between goals, challenges, and strategies.
Allyson F. Hadwin, University of Victoria
Aishah Bakhtiar, University of Victoria
Sarah K Davis, University of Victoria