Personalized recommendations for learning activities in online environments: a modular rule-based approach

Abstract Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework.