Adaptive Social Learning Based on Crowdsourcing

Autor/inn/en	Karataev, Evgeny; Zadorozhny, Vladimir
Titel	Adaptive Social Learning Based on Crowdsourcing
Quelle	In: IEEE Transactions on Learning Technologies, 10 (2017) 2, S.128-139 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Karataev, Evgeny)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2016.2515097
Schlagwörter	Socialization; Social Networks; Electronic Publishing; Collaborative Writing; Web Sites; Epistemology; Group Experience; Hypermedia; Electronic Learning; Programming; Units of Study; Student Developed Materials; Systems Approach; Cooperative Learning; Program Evaluation; Network Analysis; Learning Processes + Suchen Sie Ihr Suchwort? Socialisation; Sozialisation; Social network; Soziales Netzwerk; Elektronisches Publizieren; Web-Design; Erkenntnistheorie; Programmierung; Lerneinheit; Systemischer Ansatz; Kooperatives Lernen; Programme evaluation; Programmevaluation; Netzplantechnik; Learning process; Lernprozess
Abstract	Many techniques have been developed to enhance learning experience with computer technology. A particularly great influence of technology on learning came with the emergence of the web and adaptive educational hypermedia systems. While the web enables users to interact and collaborate with each other to create, organize, and share knowledge via user-generated content, majority of e-learning systems do not utilize the power of their users to create high quality educational content and provide data for adaptive algorithms. In this paper, we introduce a novel social learning framework that allows anybody to author educational content in a form of mini-lessons, learn lessons by following adaptive learning pathways as well as interact with their peers as in any social network. The proposed approach combines concepts of crowdsourcing, online social networks, and complex adaptive systems to engage users in efficient learning through teaching process. We first describe the main idea behind the framework and how users interact with it, and then we describe SALT system that implements the framework. We also performed evaluation of the SALT system via several classroom studies. Our results show that collective learning experiences can be efficiently utilized in adaptive social learning. We found that students tend to form stable clusters that survive very high similarity threshold. Meanwhile, our learning pathway analysis showed that almost all students have their own unique best pathway. Experiments with various recommendation algorithms showed that most algorithms obtain very small penalty in all classes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01