A Fair Clustering Approach to Self-Regulated Learning Behaviors in a Virtual Learning Environment

Abstract

While virtual learning environments (VLEs) are widely used in K-12 education for classroom instruction and self-study, young students’ success in VLEs highly depends on their self-regulated learning (SRL) skills. Therefore, it is important to provide personalized support for SRL. One important precursor of designing personalized SRL support is to understand students’ SRL behavioral patterns. Extensive studies have clustered SRL behaviors and prescribed personalized support for each cluster. However, limited attention has been paid to the algorithm bias and fairness of clustering results. In this study, we “fairly” clustered the behavioral patterns of SRL using fair-capacitated clustering (FCC), an algorithm that incorporates constraints to ensure fairness in the assignment of data points. We used data from 14,251 secondary school learners in a virtual math learning environment. The results of FCC showed that it could capture six clusters of SRL behaviors in a fair way; three clusters belonging to high-performing (i.e., H-1. Help-provider, H-2) Active SRL learner, H-3) Active onlooker), and three clusters in low-performing groups (i.e., L-1) Quiz-taker, L-2) Dormant learner, and L-3) Inactive onlooker). The findings provide a better understanding of SRL patterns in online learning and can potentially guide the design of personalized support for SRL.