ALP-CRS: enhancing course recommendation in online education through data mining and graph analysis

Recommender systems (RSs) are an increasingly common type of intelligent software that predicts the preferences of users. Online learning has become a ubiquitous and necessary means of lifelong learning, which provides flexibility in time and place. This paper introduces a course recommendation system (ALP-CRS) which is founded on APRIORi and Link Prediction. It employs link prediction and graph clustering techniques to enhance the accuracy of the recommendations, and employs association rule mining to detect common trends in the course selection. Three stages make up the methodology: outlining the guidelines for choosing courses, comparing student characteristics, and making suggestions. In the first stage, features are extracted from the course selection data and course selection rules are derived. In order to improve recommendations using link prediction, the next step comprises creating student graphs, grouping them, and filtering comparable students. During the last stage, the system offers recommendations on the filtered information, which gives students a more targeted and personalized experience of learning. According to the findings, the suggested approach was capable of providing suggestions with an 82% precision rate and a 24% recall rate, meaning that it outperformed the compared approaches by at least 4% in precision and 10% in recall.