Recommender System for Low Achievers in Higher Education

Abstract

Digital education platforms like learning management systems (LMS) have made the virtual teaching-learning process very much handy. The LMS must include additional features to track and review the learner’s behavior in the teaching-learning process. This study aims to identify the low achievers with the assessment marks which let the course instructors understand the learner’s cognitive level and enables the facilitators to recognize the student’s perspective of the course based on their reviews collected from the questionnaire. In the outcome, recommender systems are incorporated with the learning analytics by using the K-Means clustering algorithm. This algorithm has helped the facilitators to segregate and identify the set of low achievers based on their assessment scores and also to predict the appropriate reason behind such slow performance. Apart from this, the results of this study have also suggested that facilitators incorporate the use of various emerging pedagogical methods in the teaching-learning process to maximize the learner’s performance and accentuate the level of virtual classrooms.