Applying an Evolutionary Approach for Learning Path Optimization in the Next-Generation E-Learning Systems

Learning analytics is targeted to better understand and optimize the process of learning and its environments through the measurement, collection and analysis of learners' data and contexts. To advise people's learning in a specific subject, most intelligent e-learning systems would require course instructors to explicitly input some prior knowledge about the subject such as all the pre-requisite requirements between course modules. Yet human experts may sometimes have conflicting views leading to less desirable learning outcomes. In a previous study, we proposed a complete system framework of learning analytics to perform an explicit semantic analysis on the course materials, followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures, and lastly employing a simple yet efficient evolutionary approach to return the optimal learning sequence. In this paper, we carefully consider to enhance the original evolutionary optimizer with the hill-climbing heuristic, and also critically evaluate the impacts of various experts' recommended learning sequences possibly with conflicting views to optimize the learning paths for the next-generation e-learning systems. More importantly, the integration of heuristics can make our proposed framework more self-adaptive to less structured knowledge domains with conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework for learning analytics. Our empirical evaluation clearly revealed many possible advantages of our proposal with interesting directions for future investigation.