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1. 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. (October 2023, Metacognition and Learning)

   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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2. Modeling temporal self-regulatory processes in STEM learning of engineering design

   Zheng, Juan; Pan, Zilong; Li, Shan; Xie, Charles (October 2024, Educational technology society)

   Self-regulation is crucial for student success in scientific inquiry and engineering design. However, it remains unclear how students dynamically engage in self-regulated learning (SRL) processes to achieve high performance. In this study, we investigated the temporal nature of self-regulation during engineering design by leveraging computer trace data from 101 high school students who designed an energy-plus house in a simulated learning environment. Using sequential mining, we found that high-performing students were more engaged in the Observation, Analysis, and Evaluation phases of SRL than low-performing students. Additionally, high-performing students demonstrated consecutive sequential patterns between Observation and Analysis, Reformation and Evaluation, and Analysis and Evaluation behaviors. These findings provide insights into students’ SRL processes and the design of scaffoldings. 

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3. Using multimodal learning analytics to validate digital traces of self-regulated learning in a laboratory study and predict performance in undergraduate courses.

   https://doi.org/10.1037/edu0000890  

   Bernacki, Matthew L; Yu, Linyu; Kuhlmann, Shelbi L; Plumley, Robert D; Greene, Jeffrey A; Duke, Rebekah F; Freed, Rebekah; Hollander-Blackmon, Christina; Hogan, Kelly A (February 2025, Journal of Educational Psychology)

   Undergraduates enrolled in large, active learning courses must self-regulate their learning (self-regulated learning [SRL]) by appraising tasks, making plans, setting goals, and enacting and monitoring strategies. SRL researchers have relied on self-report and learner-mediated methods during academic tasks studied in laboratories and now collect digital event data when learners engage with technology-based tools in classrooms. Inferring SRL processes from digital events and testing their validity is challenging. We aligned digital and verbal SRL event data to validate digital events as traces of SRL and used them to predict achievement in lab and course settings. In Study 1, we sampled a learning task from a biology course into a laboratory setting. Enrolled students (N = 48) completed the lesson using digital resources (e.g., online textbook, course site) while thinking aloud weeks before it was taught in class. Analyses confirmed that 10 digital events reliably co-occurred ≥70% of the time with verbalized task definition and strategy use macroprocesses. Some digital events co-occurred with multiple verbalized SRL macroprocesses. Variance in occurrence of validated digital events was limited in lab sessions, and they explained statistically nonsignificant variance in learners’ performance on lesson quizzes. In Study 2, lesson-specific digital event data from learners (N = 307) enrolled in the course (but not in Study 1) predicted performance on lesson-specific exam items, final exams, and course grades. Validated digital events also predicted final exam and course grades in the next semester (N = 432). Digital events can be validated to reflect SRL processes and scaled to explain achievement in naturalistic undergraduate education settings. Educational Impact and Implications Statement Instructors often have difficulty identifying and helping struggling students in courses with hundreds of students. Digital trace data can be used to efficiently and effectively identify struggling students in these large courses, but such data are often difficult to interpret with confidence. In our study, we found that using verbal trace data to augment and validate our inferences about the meaning of digital trace data resulted in a powerful set of predictors of students’ achievement. These validated digital trace data can be used to not only identify students in need of support in large classes, but also to understand how to target interventions to the aspects of learning that are causing students the most difficulty. 

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4. Intelligent teacher dashboards that support students’ self-regulated learning, engagement, and teachers’ decision making

   https://doi.org/https://doi.org/10.3389/feduc.2021.570229  

   Wiedbusch, M. (April 2021, Frontiers in education)

   Michalsky, Tova; Moos, Daniel (Ed.)

   Teachers’ ability to self-regulate their own learning is closely related to their competency to enhance self-regulated learning (SRL) in their students. Accordingly, there is emerging research for the design of teacher dashboards that empower instructors by providing access to quantifiable evidence of student performance and SRL processes. Typically, they capture evidence of student learning and performance to be visualized through activity traces (e.g., bar charts showing correct and incorrect response rates, etc.) and SRL data (e.g., eye-tracking on content, log files capturing feature selection, etc.) in order to provide teachers with monitoring and instructional tools. Critics of the current research on dashboards used in conjunction with advanced learning technologies (ALTs) such as simulations, intelligent tutoring systems, and serious games, argue that the state of the field is immature and has 1) focused only on exploratory or proof-of-concept projects, 2) investigated data visualizations of performance metrics or simplistic learning behaviors, and 3) neglected most theoretical aspects of SRL including teachers’ general lack of understanding their’s students’ SRL. Additionally, the work is mostly anecdotal, lacks methodological rigor, and does not collect critical process data (e.g. frequency, duration, timing, or fluctuations of cognitive, affective, metacognitive, and motivational (CAMM) SRL processes) during learning with ALTs used in the classroom. No known research in the areas of learning analytics, teacher dashboards, or teachers’ perceptions of students’ SRL and CAMM engagement has systematically and simultaneously examined the deployment, temporal unfolding, regulation, and impact of all these key processes during complex learning. In this manuscript, we 1) review the current state of ALTs designed using SRL theoretical frameworks and the current state of teacher dashboard design and research, 2) report the important design features and elements within intelligent dashboards that provide teachers with real-time data visualizations of their students’ SRL processes and engagement while using ALTs in classrooms, as revealed from the analysis of surveys and focus groups with teachers, and 3) propose a conceptual system design for integrating reinforcement learning into a teacher dashboard to help guide the utilization of multimodal data collected on students’ and teachers’ CAMM SRL processes during complex learning. 

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5. 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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