A Knowledge Diffusion Model in Autonomous Learning Under Multiple Networks for Personalized Educational Resource Allocation

Autor/inn/en	Wan, Pengfei; Wang, Xiaoming; Lin, Yaguang; Pang, Guangyao
Titel	A Knowledge Diffusion Model in Autonomous Learning Under Multiple Networks for Personalized Educational Resource Allocation
Quelle	In: IEEE Transactions on Learning Technologies, 14 (2021) 4, S.430-444 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wan, Pengfei) ORCID (Wang, Xiaoming) ORCID (Lin, Yaguang)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2021.3103006
Schlagwörter	Independent Study; Personal Autonomy; Learning Processes; Electronic Learning; Conventional Instruction; Models; Networks; Blended Learning; Student Needs; Mathematics; Efficiency; Educational Resources + Suchen Sie Ihr Suchwort? Selbststudium; Individuelle Autonomie; Learning process; Lernprozess; Analogiemodell; Mathematik; Effectiveness; Effektivität; Wirkungsgrad; Bildungsmittel
Abstract	Learners' autonomous learning is at the heart of modern education, and the convenient network brings new opportunities for it. We notice that learners mainly use the combination of online and offline learning methods to complete the entire autonomous learning process, but most of the existing models cannot effectively describe the complex process of knowledge diffusion under the multinetworks framework. By analyzing the relationship between online learning and offline learning in the autonomous learning, we develop a novel formal model to characterize the dynamic process of knowledge diffusion in the autonomous learning under multiple networks. To guide learners to learn independently and effectively expand the scope of knowledge through hybrid online learning, we then introduce the personalized needs of learners and the guidance of educators and further propose an effective algorithm (ERAA) to allocate educational resources. Through the experiments, we verify the effectiveness of the model and analyze the efficiency of the proposed algorithm. Then, we compare the proposed algorithm with the existing four algorithms and the results show that the proposed algorithm improves performance by 38%. This article provides highly realistic significance for education administrators to analyze the practice, develop autonomous learning strategy and put online educational resources. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01