A Social Approach to High-level Context Generation for Supporting Context-aware M-Learning
Xu-Wei Pan 1 * , Ling Ding 1, Xi-Yong Zhu 1, Zhao-Xiang Yang 1
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1 Zhejiang Sci-Tech University, Hangzhou, CHINA
* Corresponding Author

Abstract

In m-learning environments, context-awareness is for wide use where learners’ situations are varied, dynamic and unpredictable. We are facing the challenge of requirements of both generality and depth in generating and processing high-level context. In this paper, we present a social approach which exploits social dynamics and social computing for generating high-level context. It is a novel and generic paradigm where the crowds of learners in m-learning environments directly engage in creating contents about high-level context and interactions by social tagging, and these contents and interactions are further explored to discover more implicit and complex high-level contextual information. We present the concept model, the context representation, the context matrix, and the context retrieval method. We evaluate our approach by a social simulation based experiment. The experimental results demonstrate that the context retrieval performance is improved in both the accuracy and the diversity, and validate that the proposed social approach is effective for generating high-level context.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.12973/eurasia.2017.00752a

EURASIA J Math Sci Tech Ed, 2017 - Volume 13 Issue 7, pp. 3675-3686

Publication date: 19 Jun 2017

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