Subtopic-specific heterogeneity in computer-based learning behaviors

• Published in: International journal of STEM education Vol. 11; no. 1; pp. 61 - 31
• Main Authors: Lee, HaeJin, Bosch, Nigel
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Adaptation; Algorithms; CAI; Coherence analysis; College students; Colleges & universities; Computer assisted instruction; Computer science; Computers; Context; Data collection; Data mining; Education; Educational Environment; Educational Technology; Heterogeneity; Learning; Learning analytics; Learning Processes; Mathematics Education; Metacognition; Outcomes of Education; Pattern analysis; Prior Learning; Problem solving; Science Education; Self-regulated learning; Skills; Statistical analysis; STEM education; Students; Technology education; United States > US; Illinois; Self-regulated learning; Heterogeneity; Learning analytics
• ISSN: 2196-7822; 2196-7822
• DOI: 10.1186/s40594-024-00519-x

Background Self-regulated learning (SRL) strategies can be domain specific. However, it remains unclear whether this specificity extends to different subtopics within a single subject domain. In this study, we collected data from 210 college students engaged in a computer-based learning environment to examine the heterogeneous manifestations of learning behaviors across four distinct subtopics in introductory statistics. Further, we explore how the time spent engaging in metacognitive strategies correlated with learning gain in those subtopics. Results By employing two different analytical approaches that combine data-driven learning analytics (i.e., sequential pattern mining in this case), and theory-informed methods (i.e., coherence analysis), we discovered significant variability in the frequency of learning patterns that are potentially associated with SRL-relevant strategies across four subtopics. In a subtopic related to calculations, engagement in coherent quizzes (i.e., a type of metacognitive strategy) was found to be significantly less related to learning gains compared to other subtopics. Additionally, we found that students with different levels of prior knowledge and learning gains demonstrated varying degrees of engagement in learning patterns in an SRL context. Conclusion The findings imply that the use—and the effectiveness—of learning patterns that are potentially associated with SRL-relevant strategies varies not only across contexts and domains, but even across different subtopics within a single subject. This underscores the importance of personalized, context-aware SRL training interventions in computer-based learning environments, which could significantly enhance learning outcomes by addressing the heterogeneous relationships between SRL activities and outcomes. Further, we suggest theoretical implications of subtopic-specific heterogeneity within the context of various SRL models. Understanding SRL heterogeneity enhances these theories, offering more nuanced insights into learners’ metacognitive strategies across different subtopics.