A Social Approach to High-level Context Generation for Supporting Context-aware M-Learning

作者：Pan Xu Wei^*; Ding Ling; Zhu Xi Yong; Yang Zhao Xiang

来源：Eurasia Journal of Mathematics, Science and Technology Education, 2017, 13(7): 3675-3686.

DOI：10.12973/eurasia.2017.00752a

• mobile learning
• context-aware
• personalized recommendation
• social computing
• social tagging

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.

• 出版日期2017-7
• 单位浙江理工大学