A Modular Approach to Cyclical Self-Regulated Learning Modeling with Machine Learning

Self-regulated learning (SRL) is essential for academic success, encompassing cog-nitive, metacognitive, motivational, and emotional processes that enable studentsto plan, perform, and reflect on their learning. This paper reviews foundationalSRL theory, highlighting models such as Zimmerman’s cyclical framework, whichorganizes activities into forethought, performance, and self-reflection phases,alongside contributions from Boekaerts, Winne and Hadwin, Pintrich, Efklides,and Hadwin et al. It examines interventions, which have proven effective in tra-ditional settings for enhancing SRL but are resource-intensive and challengingto scale. Measurement approaches are discussed, including offline methods, likesurveys, and online techniques using trace and multimodal data, though the lat-ter often provide biased, incomplete representations of SRL by focusing solelyon digital interactions and neglecting offline activities or theoretical mappings.In this paper, we explore methods to use machine learning to model cyclicalSRL, review challenges of applying modern techniques to represent SRL the-ory models, test SRL-inspired feature engineering from trace data, and proposea modular machine learning approach that partitions data by macro-phases tocapture causal insights and cyclical reinforcement otherwise lost in monolithicmodels (a single unified approach), enabling scalable intervention simulation forpersonalized e-learning.