A New Methodology of Knowledge Point Sequence Generation and Learning Path Recommendation by Knowledge Reasoning

Abstract

Accurate knowledge point sequences and clear learning paths are the compass for learners to travel in the sea of massive knowledge, they can point out the way for learners and improve their learning experience. Generating knowledge point sequences and learning paths based on knowledge reasoning is conductive to optimizing the allocation of educational resources and improving the quality of higher education, and this has a profound influence on the reform of the entire educational field. In view of this, this study explored the generation of knowledge point sequences and the recommendation of learning paths based on knowledge reasoning. At first, the learning behavior of learners was subjected to collaborative analysis based on three aspects of knowledge points: learning frequency, learning duration, and pause/ skip frequency, and the specific method of generating subject knowledge point sequences based on the metrics of difficulty differences was given. Then, a sequence sampling method that matches the features of Entity-Relationship (ER) diagram was proposed, which enables the system to dynamically adjust the recommended knowledge points and learning paths according to learners’ learning progress with the help of biased random walks, thereby giving personalized and dynamic learning recommendations. At last, the validity of the proposed method was verified by experimental results.