Personalized Online Learning Resource Recommendation Based on Artificial Intelligence and Educational Psychology - Wei, Xin; Sun, Shiyun; Wu, Dan; Zhou, Liang (Frontiers in Psychology, 2021)
In remote education era, online learning has become an important way for replacing traditional face-to-face learning within classroom, especially during the 2020 COVID-19 pandemic. How to conveniently and precisely choose the suitable learning resources in online learning scenarios is an annoying problem for students. Recommendation is an effective tool for providing students learning resources, which has been used in current online learing platform. However, traditional online learning resource recommendation schemes mainly focus on students' interests or characteristics of resources, result in effects far from satisfactory. Different from existing schemes, in this paper, we design a personalized online learning resource recommendation scheme aiming at matching students’ ability and inspiring their potentials. Specifically, according to educational psychology, students’ learning ability can be first obtained by analyzing their learning behaviors and their identities can be classified. Then, features of learning resources such as difficulty degree are extracted and a LinUCB-based recommendation algorithm is proposed. In this algorithm, an exploration coefficient is carefully constructed according to student’s ability. It can adaptively adjust the ratio of exploration and exploitation during recommendation. Finally, experiments are conducted for evaluating its performances on precision, adaptivity, and personalization. From both experimental results and analysis based on educational psychology, the proposed personalized online learning resource recommendation scheme can not only control the difficulty degree of learning resources within the student’s ability, but also encourage their potential by providing the suitable learning resources.