Social Context-Aware Recommendation for Personalized Online Learning

The integration of ICT in teaching and learning enables the paradigm shift for education system by creating a possibility for learner to learn anywhere and anytime through variety of communication system. To enhance effective learning for a large number of learners, online learning requires effective personalized learning method. For decades, recommendation system is responsible for providing personalized learning to the learners by considering several related learners information such as individual characteristic, learning style, and knowledge background. With context aware computing perspective, this paper thus proposes the context-aware recommendation system to promote effective personalized online learning for each learner individually. Instead of employing ordinary individual context, this paper focuses also on the social context which is the interaction between learning objects and the learners. The gathered social context is classified with K-nearest neighbor and decision tree for classifying appropriate types of learners. Consequently, the appropriate learning paths are recommended by using association rule. The empirical study is conducted with the learners having scientific and non-scientific backgrounds studying in two different content modules of basic computer skill course. The results show that the proposed social context-aware recommendation system is able to provide acceptable classification accuracies from both classifiers. Additionally, the proposed system is potentially able to recommend appropriate learning path to different group of learners.