Leveraging Personality Information to Improve Community Recommendation in E-Learning Platforms

Autor/inn/en	Sun, Jianshan; Geng, Jie; Cheng, Xusen; Zhu, Mingyue; Xu, Qiyu; Liu, Yunli
Titel	Leveraging Personality Information to Improve Community Recommendation in E-Learning Platforms
Quelle	In: British Journal of Educational Technology, 51 (2020) 5, S.1711-1733 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13011
Schlagwörter	Personality; Electronic Learning; Integrated Learning Systems; Computer Mediated Communication; Discussion Groups; Mathematics; Social Theories; Personality Measures + Suchen Sie Ihr Suchwort? Personalität; Computerkonferenz; Mathematik; Gesellschaftstheorie
Abstract	E-learning platforms are becoming more and more important and they are gradually changing people's learning ways. In the e-learning platforms, users actively create and join their favorite communities to share their questions and ideas. With the increase of users of e-learning platforms, the number of communities is increasing dramatically. In this context, it has become difficult for users to find learning communities that match their interests and preferences. Therefore, how to effectively recommend the learning community for users has become an urgent need. However, compared to learning item recommendation, there is relatively limited work on learning community recommendation, and the existing research on community recommendation often ignores the personality information. Personality is considered one of the primary factors that influence human behavior and social relationships, as it affects how people react and interact with others. Several studies have demonstrated that people with similar personality tend to have similar interests. Furthermore, homophily theory also states that social interactions between similar individuals occur at a higher rate than among dissimilar ones. Since interests and interactions are important driving forces for users to join the learning communities, personality has an important impact on users' choices of communities. Therefore, this paper aims at shedding some light on the impact of personality information on the accuracy of community recommendations. Particularly, we propose three enhanced matrix factorization models based on the Big Five personality framework. To evaluate the effectiveness of our proposed models, we conducted extensive experiments on myPersonality datasets. The results prove that the personality information can improve the performance of the learning community recommendation model and alleviate the data sparsity problem. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01