Recommendation of Online Learning Resources for Personalized Fragmented Learning Based on Mobile Devices

Abstract

Fragmented learning aims to fully utilize fragmented time slices to learn and accumulate fragmented knowledge. The current mobile online learning apps fail to fully consider the preferences, demands, and adaptability of users. The content and difficulty of the recommended resources are not in match with user features. Therefore, this paper explored the issue of the recommendation of personalized online learning resources for fragmented learning based on mobile devices. Firstly, the authors developed an architecture for the adaptive recommendation model of online learning resources, modeled the learners and fragmented learning resources. Next, the recommendation model was constructed for personalized online learning resources, the flow of the recommendation engine was detailed, and the degrees of resource recommendation and matching were calculated. The proposed model was proved valid through experiments.