Enhancing Learning Object Recommendations for Teachers Using Adaptive Neighbor Selection

Recommender Systems (RS) have been implemented in the Technology enhanced Learning (TeL) field for facilitating, among others, Learning Object (LO) selection by teachers to support their daily teaching practice. In particular, memory-based collaborative filtering (CF) approaches have demonstrated promising results for real-life implementations of web-based Learning Object Repositories (LOR). Building on this, the contribution of this paper is an enhancement to existing memory-based CF RS methods, by adaptively selecting the teacher neighbors based on their co-rated LOs and the attribute similarity of the latter to the LO to be recommended. The evaluation results show a significant increase in the predictive accuracy of the adaptive RS approaches compared to their "traditional" benchmarks, signifying the proposed approach's capacity to enhance the accuracy of existing memory-based CF approaches.