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1. Adding self-regulated learning instruction to an introductory physics class

   https://doi.org/10.1119/perc.2023.pr.Maldonado  

   Maldonado, Danielle; Stewart, John (October 2023, 2023 PERC Proceedings)

   Self-regulated learning (SRL) is an essential factor in academic success. Self-regulated learning is a process where learners set clear goals, monitor progress toward attainment of those goals, and adapt their strategies to improve their learning. Because SRL is often not explicitly integrated into the classroom, students struggle to identify and use learning techniques empirically proven to be more successful than others. SRL is a learned skill students can develop over time that has been found to be related to high achievement and self-efficacy. This paper examines the effects of introducing SRL strategies into an undergraduate introductory physics classroom. The degree to which the students were self-regulated learners was correlated with their test averages (r = 0.23, p < 0.05). Students reported that they found the SRL instruction helpful (3.5 out of 5.0 on a 5-point scale) and 86% of the students felt the time spent on the instruction was generally appropriate. Students’ preferred study methods changed over the course of the semester, indicating that students applied SRL by adapting their learning processes based on which methods were most effective in helping them study for an upcoming exam and opting not to use techniques no longer perceived as useful. Higher achieving students were more likely to settle on highly effective techniques by the end of the semester, while lower achieving students continued to modify their learning processes. 

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2. From Analysis to Feedback: Using Large Language Models to Support Self-Regulated Learning

   https://doi.org/10.1145/3774398.3811565  

   Park, Saerok; Nguyen, Ha (June 2026, Association for Computing Machinery)

   Supporting students' self-regulated learning (SRL) at scale requires tools that can assess SRL depth and provide tailored feedback. We demonstrate the feasibility of using large language models (LLMs) to code students' written responses for SRL and generate adaptive feedback. Using a dataset of 606 reflection texts from 45 students, we evaluated the inter-rater agreement between several LLMs and human researchers in scoring SRL depth. Based on the LLM assessment, we generated feedback for students to apply SRL in a modeling task and explored the feedback's quality in a focus group with six SRL and science education researchers. LLMs showed substantial agreement with human raters in assessing students' SRL levels (low, medium, high) in specific strategies, including content evaluation and strategy monitoring. LLMs also produced theory-aligned and pedagogically relevant feedback, particularly for learners with higher SRL. We discuss design implications for implementing scalable SRL assessment and adaptive feedback in science learning. 

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3. Relations between undergraduates’ self-regulated learning skill mastery during digital training and biology performance

   https://doi.org/10.1007/s11409-023-09356-9  

   Bernacki, Matthew L.; Cogliano, Megan Claire; Kuhlmann, Shelbi L.; Utz, Jenifer; Strong, Christy; Hilpert, Jonathan C.; Greene, Jeffrey A. (December 2023, Metacognition and Learning)

   Anastasia Efklides, Ph.D. (Ed.)

   Abstract Undergraduate STEM lecture courses enroll hundreds who must master declarative, conceptual, and applied learning objectives. To support them, instructors have turned to active learning designs that require students to engage inself-regulated learning(SRL). Undergraduates struggle with SRL, and universities provide courses, workshops, and digital training to scaffold SRL skill development and enactment. We examined two theory-aligned designs of digital skill trainings that scaffold SRL and how students’ demonstration of metacognitive knowledge of learning skills predicted exam performance in biology courses where training took place. In Study 1, students’ (n = 49) responses to training activities were scored for quality and summed by training topic and level of understanding. Behavioral and environmental regulation knowledge predicted midterm and final exam grades; knowledge of SRL processes did not. Declarative and conceptual levels of skill-mastery predicted exam performance; application-level knowledge did not. When modeled by topic at each level of understanding, declarative knowledge of behavioral and environmental regulation and conceptual knowledge of cognitive strategies predicted final exam performance. In Study 2 (n = 62), knowledge demonstrated during a redesigned video-based multimedia version of behavioral and environmental regulation again predicted biology exam performance. Across studies, performance on training activities designed in alignment with skill-training models predicted course performances and predictions were sustained in a redesign prioritizing learning efficiency. Training learners’ SRL skills –and specifically cognitive strategies and environmental regulation– benefited their later biology course performances across studies, which demonstrate the value of providing brief, digital activities to develop learning skills. Ongoing refinement to materials designed to develop metacognitive processing and learners’ ability to apply skills in new contexts can increase benefits. 

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4. A Systematic Implementation of Four Versions of a Course-Based Intervention to Reduce Attrition Among Civil Engineering Students: Overall Study Design and Implementation of First Version

   https://doi.org/10.18260/1-2--42521  

   Wittig, Beth (June 2023, ASEE Conferences)

   This works seeks to develop and assess a retention intervention that addresses the key drivers of attrition and learns from existing interventions for engineering students. The resulting intervention addresses key competencies for the major and profession, and also addresses a gap in current approaches: the need to synergistically support students’ social-cognitive disposition with respect to attrition by training them in social-cognitive skills and strategies adapted from the theories of Sense of Belonging (SOB) and Self-Regulation of Learning (SRL). Because the degree of skills and strategies around SRL and SOB needed to make the largest impact to retention is unknown, four versions of the intervention are proposed: A base intervention which provokes students to think about their learning and belonging, an intervention augmented with specific training in effective SRL, an intervention augmented with specific training in SOB; and an intervention augmented with training in both effective SRL and SOB. An overarching research design plans the offering and assessment of each version of the intervention, including a numerical longitudinal analysis of retention at the end of the study, with the ultimate goal of identifying which version of the intervention has the largest positive impact to retention and other key metrics. After a general description of the intervention as a while, the focus was reoriented to the base version of the intervention. The detailed design was presented along with the assessment methods for short-term effectiveness and the preliminary results for its first offering in Fall 2022. Overall, students found the topics covered in the intervention to be helpful and used many of the skills and strategies from the intervention in other major courses. The impact of the intervention on performance in major courses taken alongside the intervention and their persistence rate in the major for another semester improved significantly for one major course but were inconclusive for a second major course. Recommendations were made to refine the materials provided to students and several of the activities in the base intervention; and the formative assessment tool. 

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5. Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining

   https://doi.org/10.3389/fpsyg.2022.813514  

   Taub, Michelle; Banzon, Allison M.; Zhang, Tom; Chen, Zhongzhou (March 2022, Frontiers in Psychology)

   Success in online and blended courses requires engaging in self-regulated learning (SRL), especially for challenging STEM disciplines, such as physics. This involves students planning how they will navigate course assignments and activities, setting goals for completion, monitoring their progress and content understanding, and reflecting on how they completed each assignment. Based on Winne & Hadwin’s COPES model, SRL is a series of events that temporally unfold during learning, impacted by changing internal and external factors, such as goal orientation and content difficulty. Thus, as goal orientation and content difficulty change throughout a course, so might students’ use of SRL processes. This paper studies how students’ SRL behavior and achievement goal orientation change over time in a large ( N  = 250) college introductory level physics course taught online. Students’ achievement goal orientation was measured by repeated administration of the achievement goals questionnaire-revised (AGQ-R). Students’ SRL behavior was measured by analyzing their clickstream event traces interacting with online learning modules via a combination of trace clustering and process mining. Event traces were first divided into groups similar in nature using agglomerative clustering, with similarity between traces determined based on a set of derived characteristics most reflective of students’ SRL processes. We then generated causal nets for each cluster of traces via process mining and interpreted the underlying behavior and strategy of each causal net according to the COPES SRL framework. We then measured the frequency at which students adopted each causal net and assessed whether the adoption of different causal nets was associated with responses to the AGQ-R. By repeating the analysis for three sets of online learning modules assigned at the beginning, middle, and end of the semester, we examined how the frequency of each causal net changed over time, and how the change correlated with changes to the AGQ-R responses. Results have implications for measuring the temporal nature of SRL during online learning, as well as the factors impacting the use of SRL processes in an online physics course. Results also provide guidance for developing online instructional materials that foster effective SRL for students with different motivational profiles. 

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