Recommendation of Learning Resources for MOOCs Based on Historical Sequential Behaviours

Abstract

Learning path recommendation is crucial for guiding learners through a series of courses in a logical sequence based on their previous learning experiences. This is particularly important for improving learning outcomes in massive open online courses (MOOCs) for diverse learners. Because both the historical learning courses and recommended learning paths can be represented as sequential patterns (SPs); it is reasonable to approach this problem through SP mining (SPM). In addition to support, we incorporate three factors, that is, course learning days, grades and engagement, to model frequent high-utility SPs (FHUSPs). When recommending a learning path, FHUSPs that align with the target user's learning history and are common among successful learners, while rare among less successful ones, are prioritised. If there are insufficient matching FHUSPs, we address this by recommending additional courses based on the joint competency and complementarity of learners similar to the target learner. Experimental results on a real-world dataset demonstrate that our method provides highly accurate and relevant recommendations.