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