Enhancing self-regulated learning in online environments: challenges and opportunities for recommender system algorithms

Abstract Although recommender systems have the potential to advance self-regulated learning (SRL) in online environments, implementation challenges impede their development. To systematically investigate the barriers to the effective use of recommender systems to enhance SRL, this study developed a three-tiered analytical framework that considers challenges in three areas: data and experiments, user experience/interface design, and technical factors. A systematic search of five bibliographic databases identified 33 empirical studies. The findings revealed the following key challenges in the three analytical levels: (a) data and experiments: scarcity, validity, and arrangement; (b) user experience and engagement: poor matching and inadequate scaffolding for SRL (e.g., weak feedback mechanisms) and suboptimal interface and function designs; and (c) technical factors: lack of stability and security of recommender systems and limitations in modeling complex learning behaviors and balancing personalization with pedagogical goals. These challenges interact dynamically to form a self-reinforcing recursive constraint, where poor user experiences lead to disengagement, resulting in sparse, low-quality data that further constrains algorithmic capabilities. Current algorithmic approaches typically address isolated problems, failing to break this cycle by overlooking these systemic interdependencies. This underscores the critical need to move beyond isolated fixes and develop comprehensive, pedagogically-aware algorithmic architectures. To address this, this study proposes a four-phase development roadmap for creating an SRL-Scaffolding Architecture. This roadmap integrates multi-modal data validation, hybrid algorithmic intelligence combining emotional and cognitive modeling, adaptive scaffolding interfaces, and a continuous human-in-the-loop feedback cycle. By mapping these interconnected challenges and proposing a structured architectural framework, this study proposes roadmap to address these barriers and develop recommender systems that effectively empower SRL in online environments.