research-article

Cyberlearners and learning resources

Authors:

Leyla Zhuhadar,

Rong YangAuthors Info & Claims

LAK '12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge

Pages 65 - 68

https://doi.org/10.1145/2330601.2330621

Published: 29 April 2012 Publication History

Abstract

The discovery of community structure in real world networks has transformed the way we explore large systems. We propose a visual method to extract communities of cyberlearners in a large interconnected network consisting of cyberlearners and learning resources. The method used is heuristic and is based on visual clustering and a modularity measure. Each cluster of users is considered as a subset of the community of learners sharing a similar domain of interest. Accordingly, a recommender system is proposed to predict and recommend learning resources to cyberlearners within the same community. Experiments on real, dynamic data reveal the structure of community in the network. Our approach used the optimal discovered structure based on the modularity value to design a recommender system.

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Cited By

Yassine SKadry SSicilia M(2021)Detecting communities using social network analysis in online learning environments: Systematic literature reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.143112:1Online publication date: 25-Sep-2021
https://doi.org/10.1002/widm.1431
Alabi H(2016)A Language and a SpaceDeveloping Effective Educational Experiences through Learning Analytics10.4018/978-1-4666-9983-0.ch001(1-41)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9983-0.ch001

Cyberlearners and learning resources
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

LAK '12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge

April 2012

282 pages

ISBN:9781450311113

DOI:10.1145/2330601

Conference Chairs:
Shane Dawson
University of British Columbia, Canada
,
Caroline Haythornthwaite
University of British Columbia, Canada
,
Editors:
Simon Buckingham Shum,
Dragan Gasevic,
Rebecca Ferguson

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
TEKRI: Technology-Enhanced Knowledge Research Institute
Desire2Learn: Desire2Learn Inc.
EDUCAUSE: EDUCAUSE
University of British Columbia: University of British Columbia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 April 2012

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LAK 2012

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SIGWEB
TEKRI
Desire2Learn
EDUCAUSE
University of British Columbia

LAK 2012: Second International Conference on Learning Analytics and Knowledge

April 29 - May 2, 2012

British Columbia, Vancouver, Canada

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Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Yassine SKadry SSicilia M(2021)Detecting communities using social network analysis in online learning environments: Systematic literature reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.143112:1Online publication date: 25-Sep-2021
https://doi.org/10.1002/widm.1431
Alabi H(2016)A Language and a SpaceDeveloping Effective Educational Experiences through Learning Analytics10.4018/978-1-4666-9983-0.ch001(1-41)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9983-0.ch001

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