The use of the recommended learning path in the personalized adaptive e-learning system

This paper promotes the idea of the learning process management in the e-learning system. A personalized adaptive e-learning system is used in this research that comprises three developed topic acquisition sequences: teacher, learner or optimal topic sequences. The learner has the ability to switch between the aforementioned topic sequences. The system stores data about the course acquisition process. The analysis of the stored data demonstrated that a bit more than half of the students used the teacher topic sequence; higher grades in topics got those students who chose the learner or optimal topic sequence; the grades of the half of the students who used the optimal and teacher topic sequences were in the same level. The obtained results were used as the justification for the improvement of the existing optimal topic sequence development method. As a result, an algorithm for the recommended learning path development is proposed in this paper. The topics of the course and links in between are described using a weighted directed graph. The weight of every edge and vertex of the graph is calculated based on the parameter values describing the topic. Afterwards, the recommended learning path is assumed to be the path with the lowest weight that is found in the weighted oriented graph using a search.