Research on the Hierarchical Structure of User Feature Factors Affecting the Accurate Recommendation of Digital Educational Resources

Abstract

In order to analyze the user characteristic factors that affect the accurate recommendation of digital educational resources, optimize the user model and improve the accuracy of resource recommendation. In this paper, the interpretive structure Model (ISM) technology is used to clarify the logical relations among 13 key feature factors and establish the interpretive structure hierarchy model. Then the MICMAC model is used to analyze the dependence and driving force of each characteristic factor to determine its influence degree. The results show that user resource needs, resource preference and learning style are the direct factors affecting digital educational resource recommendation service. Individual attribute, learning motivation, social attribute, interactive preference, learning emotion and learning attitude are the core factors that affect the recommendation of digital educational resources. Information literacy, knowledge level, cognitive structure and learning input are the source factors that affect the recommendation of digital educational resources. Combining ISM analysis and MICMAC classification, the stability and driving force of the model level are gradually enhanced from the surface layer to the deep layer, and the dependence is gradually weakened.