Quantitative Analytics in Exploring Self-Regulated Learning Behaviors: Effects of Study Persistence and Regularity

This Research Full Paper investigates potential quantitative self-regulated learning (SRL) analytics of problem-solving behaviors on a multiple-choice question (MCQ) platform. Conventionally, SRL skills are assessed by qualitative and self-report instruments which can provide insightful details of SRL behaviors. Nevertheless, such kind of tools might be under criticism of reproducibility and lack scalability. Additionally, it is difficult to provide real-time feedback based on such measurements since they require active participation and longer data collection processes. These factors thereby deter educational practitioners who wish to utilize SRL-based pedagogy in larger classrooms. To make SRL research more accessible to educational practitioners, we propose to use quantitative analysis based on trace data to measure potential SRL behaviors from two aspects: study persistence and study regularity, which we assume are implicit measurement of SRL. We conducted the experiment in an undergraduate computer science course by a homegrown self-assessment platform, QuizIt and successfully identified four types of study persistence. The study regularity is measured as scores and associated in a linear regression model. The evaluation based on students' formal exam scores in the course shows that high persistence and regular behaviors are positively correlated to exam performance. Although the examination of linkage from persistence and regularity to conventional self-report SRL measures is left for the future study, our approach has implied a potential for scalable educational tools that can avoid subjectivity of self-report SRL measurement.