Enhancing Learning Paths with Concept Clustering and Rule-Based Optimization

Finding a good learning path with respect to existing reference paths of closely related concepts is very challenging yet important for effective course teaching and especially adaptive e-learning systems. There are various approaches including ontology analysis to extract the key concepts which could then be correlated to one another using an implicit or explicit knowledge structure for relevant courses. With the available correlation information, an effective optimizer can ultimately return a good learning path according to its predefined objective function. In this paper, we propose to obtain more thorough correlation information through concept clustering, which will then be passed to our rule-based genetic algorithm to search for better learning path(s). To demonstrate the feasibility of our proposal, a prototype of our ontology analyser enhanced with concept clustering and rule-based optimizer was implemented. Its performance was thoroughly studied and compared favorably against the benchmarking shortest-path optimizer on actual courses. More importantly, our proposal can be easily integrated into existing e-learning systems, and has significant impacts for adaptive or personalized e-learning systems through enhanced ontology analysis.