PERKC: Personalized kNN With CPT for Course Recommendations in Higher Education

E-learning is increasingly being used by students in the higher education level for their university credit purpose and some for improving their knowledge. E-learning is also used for skill enhancement purpose by organizations. Due to the availability of wide-ranging options, recommender systems that provide personalized suggestions are much needed. The proposed methodology takes advantage of compact prediction tree (CPT), a popular sequence prediction algorithm. In this article, a new prediction model based on applying CPT over similar students which is found in a novel manner is proposed. The aim of the work is to recommend courses to students at university level. The methodology was evaluated in terms of accuracy and results show the proposed work performs better than applying only CPT, when applying fuzzy C-means with CPT, and when applying k nearest neighbors with CPT.