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Learners Thrive When Using Multifaceted Open Social Learner Models

Shi, Lei; Cristea, Alexandra I.

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Authors

Lei Shi



Abstract

This article explores open social learner modeling (OSLM)-a social extension of open learner modeling (OLM). A specific implementation of this approach is presented by which learners' self-direction and self-determination in a social e-learning context could be potentially promoted. Unlike previous work, the proposed approach, multifaceted OSLM, lets the system seamlessly and adaptively embed visualization of both a learner's own model and other learning peers' models into different parts of the learning content, for multiple axes of context, at any time during the learning process. It also demonstrates the advantages of visualizing both learners' performance and their contribution to a learning community. An experimental study shows that, contrary to previous research, the richness and complexity of this new approach positively affected the learning experience in terms of perceived effectiveness, efficiency, and satisfaction. This article is part of special issue on social media for learning.

Citation

Shi, L., & Cristea, A. I. (2015). Learners Thrive When Using Multifaceted Open Social Learner Models. IEEE MultiMedia, 23(1), 36-47. https://doi.org/10.1109/mmul.2015.93

Journal Article Type Article
Acceptance Date Oct 6, 2015
Online Publication Date Nov 11, 2015
Publication Date Nov 11, 2015
Deposit Date Jul 11, 2018
Publicly Available Date Jul 31, 2018
Journal IEEE MultiMedia
Print ISSN 1070-986X
Electronic ISSN 1941-0166
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 1
Pages 36-47
DOI https://doi.org/10.1109/mmul.2015.93
Related Public URLs http://wrap.warwick.ac.uk/75813/

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© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.





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