Path-Based Recommender System for Learning Activities Using Knowledge Graphs

Recommender systems can offer a fertile ground in e-learning software, since they assist users by presenting them with learning material which be more interested, based on their preferences. To this end, paper, we present new method for knowledge-graph-based, path-based recommender system activities. The suggested approach makes better activity recommendations using connections between people and/or products. By pre-defining meta-paths or automatically mining connective patterns, our uses the student-learning graph to find path-level commonalities provide an explanation result as well. Our methodology is used intelligent tutoring Java programming domain being taught. keeps track of user behavior and recommend activities students knowledge-graph-based system. Numerous metadata, such kind, complexity, number questions, are describe each activity. has been evaluated promising results that highlight effectiveness activities, while preserving pedagogical affordance.