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

Shuoyan Xu

doi:10.3991/ijet.v17i03.29427

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.

Highlights

In the education field, the application of fragmented learning based on mobile devices in various disciplines has been gradually valued by people [1,2,3,4,5]
The experiments had proved that applying the constructed model to the resource recommendation scenarios of personalized fragmented online learning based on mobile devices was feasible and effective
This paper studied the resource recommendation for personalized fragmented online learning based on mobile devices

Summary

Introduction

The application of fragmented learning based on mobile devices in various disciplines has been gradually valued by people [1,2,3,4,5]. How to help learners acquire personalized learning resources during their fragmented learning in a timely manner to meet their respective learning preferences and demands has brought a new challenge to the resource recommendation function of existing e-learning platforms operating based on mobile devices. Regarding this challenge, scholars Cheng and Wang [17] designed a five-dimensional model of learner features and a three-dimensional model of English reading resource features; to make the learning resources fit for the temporal and spatial characteristics of fragmented learning, they rationally designed and subdivided the reading resources of CET-4 (College English Test - Band Four) to meet learners’ demands for fragmented learning. The fourth chapter verified the effectiveness of the constructed model using experimental results

Modeling of learners and fragmented learning resources

Fragmented learning resource model

Evaluation features Difficulty

Experimental results and analysis

Conclusion

Author