Online Course Learning Resource Recommendation Based on Difficulty Matching

Abstract

The current methods for online course learning resource recommendation tend to continuously push similar items to students. To make students more satisfied with the overall recommendation service, it is necessary to explore the recommendation methods for online course learning resources based on difficulty matching. Therefore, this paper devises an online course learning resource recommendation method based on difficulty matching. Firstly, an online course learning resource recommendation approach was developed based on the autoencoder, in the light of difficulty matching. The flowchart of learning resource recommendation was presented, and the proposed algorithm was described in details. Then, the authors depicted the flow of the online course learning resource recommendation model, which considers difficulty matching, and proposed to capture the hierarchy of resources by assigning difficulty labels to texts, such that the proposed recommendation model outputs interpretable recommendation results. Finally, the authors designed a model that can discover the influence of the difficulty of learned resources on the difficulty of unlearned learning resources, and recommended online course learning resources considering difficulty matching. The effectiveness of the proposed model is verified through experiments.