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Personalized Learning Path Recommendation Based on Context Awareness

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DOI: 10.38007/Proceedings.0000239

Author(s)

Rongqing Zhuo, Yang Chen

Corresponding Author

Rongqing Zhuo

Abstract

With the rapid integration of computer technology and sensing technology. The rapid development of the mobile Internet, Internet of Things and smart mobile terminals has been promoted. In order to promote the popularization of educational information, the construction of lifelong learning platform provides technical support and guarantee. Although the emergence of search engines helps learners find learning resources quickly and efficiently, it still cannot obtain personalized dynamic learning services. Therefore, in order to adapt to people's individual needs, at the same time reduce the user's search costs. Personalized recommendations have also become one of the research trends in referral services. The semantic matching algorithm calculates the similarity between the context ontology and the ontology of the subject domain, and calculates the knowledge information required by the learner, so as to accurately recommend the personalized learning path. This paper is based on context-aware data inference technology, and infers implicit context information according to context inference rules and constraints, and establishes an adaptive learning path. Finally, the accuracy of the model prediction method for adding scene-aware data proposed in this paper is greatly improved. Collaborative filtering algorithms are caused by data sparsity. The accuracy of mitigating prediction scores has been greatly improved.

Keywords

Situational Awareness; Personalized Learning; Collaborative Filtering; Learning Path