Analytical review and study on various course recommendation systems

In the educational system, online courses are significant in developing the knowledge of users. The selection of courses is important for college students because of large unknown optional courses. The course recommendation systems are provided with suggestions and improve course selection during the pre-registration stage. This survey presents the analysis of 50 research papers for course recommendation. The course recommendation systems are grouped under three categories, namely machine learning-based techniques, collaborative-based and data mining-based techniques. Besides, the classification of techniques, utilised tools, implemented software tools and performance metrics are considered for analysis. Moreover, the research gaps identified in the existing course recommendation system are discussed. The machine learning-based approach is mostly used for course recommendation among several approaches. Most existing course recommendation techniques use Java as the implementation tool and the Moodle log database. Also, F-measure, MAE, accuracy and RMS have been commonly used as performance metrics.