Reinforcement learning for automatic detection of effective strategies for self-regulated learning

Self-regulated learning (SRL) is an essential skill for achieving one's learning goals, particularly in Digital Learning Environments (DLEs) where system support is often limited compared to traditional classroom settings. However, research has found that learners often struggle to adapt their behaviour to the self-regulatory demands of DLEs. Furthermore, existing SRL analysis tools have limited utility for real-time or individualized prescriptive support of a learner's SRL strategy during a study session. In response to these challenges, we propose a novel approach using reinforcement learning as a framework to optimize the sequence of SRL processes for a learning task. This framework allows us to model and optimize the SRL strategy as a sequential decision-making problem, where each decision corresponds to an SRL process. The goal is to find an optimal sequence of decisions that maximizes performance in learning outcomes such as assessment score or learning gains. We compare the performance of our reinforcement learning framework with other sequential machine learning tools, such as Long Short-Term Memory (LSTM) neural networks and Genetic Algorithms (GA). The results of our study show that our reinforcement learning model outperforms GA and LSTM models in optimizing SRL strategy. The contributions of this work can facilitate the development of a tool which can detect sub-optimal SRL strategy in real-time and enable individualized SRL focused scaffolding.