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Building Socially-Aware E-Learning Systems Through Knowledge Management

Building Socially-Aware E-Learning Systems Through Knowledge Management

Richa Sharma, Hema Banati, Punam Bedi
Copyright: © 2012 |Volume: 8 |Issue: 3 |Pages: 26
ISSN: 1548-0666|EISSN: 1548-0658|EISBN13: 9781466613270|DOI: 10.4018/jkm.2012070101
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MLA

Sharma, Richa, et al. "Building Socially-Aware E-Learning Systems Through Knowledge Management." IJKM vol.8, no.3 2012: pp.1-26. http://doi.org/10.4018/jkm.2012070101

APA

Sharma, R., Banati, H., & Bedi, P. (2012). Building Socially-Aware E-Learning Systems Through Knowledge Management. International Journal of Knowledge Management (IJKM), 8(3), 1-26. http://doi.org/10.4018/jkm.2012070101

Chicago

Sharma, Richa, Hema Banati, and Punam Bedi. "Building Socially-Aware E-Learning Systems Through Knowledge Management," International Journal of Knowledge Management (IJKM) 8, no.3: 1-26. http://doi.org/10.4018/jkm.2012070101

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Abstract

Conformance to social context while designing an e-learning course is crucial in enhancing acceptability of the course. Building socially aware e-learning courses requires elicitation of social opinion from various stakeholders associated with the system. Stakeholders are disparate in their perception towards the intricacies of the system, leading to generation of numerous assorted ideas. Knowledge Management (KM) assimilates these ideas to bring congruency into the system. This paper proposes i) a model KMeLS (Knowledge Management in e-Learning Systems) built upon the SECI (Socialization, Externalization, Combination and Internalization) framework, and ii) an algorithm PARSeL (Prioritizing Alternatives using Recommendations of Stakeholders in e-Learning) to incorporate KM into designing an e-learning course. PARSeL prioritizes the content using stakeholder recommendations using Analytic Hierarchy Process (AHP) and fuzzy modeling. A case study is also presented with a goal of prioritizing a set of programming languages for an online computing course. The proposed methodology can be promising in recommending appropriate content for the e-learners and can be implemented to benefit e-learning organizations in a wider spectrum.

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