QoE oriented intelligent online learning evaluation technology in B5G scenario

Abstract

Students' demand for online learning has exploded during the post-COVID-19 pandemic era. However, due to their poor learning experience, students' dropout rate and learning performance of online learning are not always satisfactory. The technical advantages of Beyond Fifth Generation (B5G) can guarantee a good multimedia Quality of Experience (QoE). As a special case of multimedia services, online learning takes into account both the usability of the service and the cognitive development of the users. Factors that affect the Quality of Online Learning Experience (OL-QoE) become more complicated. To get over this dilemma, we propose a systematic scheme by integrating big data, Machine Learning (ML) technologies, and educational psychology theory. Specifically, we first formulate a general definition of OL-QoE by data analysis and experimental verification. This formula considers both the subjective and objective factors (i.e., video watching ratio and test scores) that most affect OL-QoE. Then, we induce an extended layer to the classic Broad Learning System (BLS) to construct an Extended Broad Learning System (EBLS) for the students' OL-QoE prediction. Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS, the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity. Finally, we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values. Through timely interventions, their OL-QoE and learning performance can be improved. Experimental results verify the effectiveness of the proposed scheme.

Keywords

• B5G
• Online learning
• Quality of experience