A proposed course recommender model based on collaborative filtering for course registration

Students face issues and challenges in making decisions for course registration. Traditionally, students rely on suggestions from academic advisers prior to course registration. Therefore, students spend a considerable amount of time waiting for advisers to help them register for the right subjects. However, the number of students rises yearly, thereby increasing the responsibilities of lecturers. Moreover, academic advisers experience constraints in analysing data during consultations for course registration. Therefore, this study proposes a course recommender model based on collaborative filtering. Collaborative filtering is adopted because it provides recommendations based on students' performance in previous subjects. A dataset from the Information & Communication Technology Centre (ICT) of the University Malaysia Pahang is used to evaluate the proposed model. The evaluation is conducted based on two experiments. The first experiment is performed by calculating the difference between actual and predicted scores to verify prediction accuracy. Results show that the average of the mean absolute error of the proposed model is 0.319, which is highly accurate. The second experiment is conducted by comparing the recommendations of the proposed model with those of experts to validate the course recommendation accuracy of the proposed model. Results of the second experiment show that the proposed model has a 91.06% accuracy rate with an error rate of 8.94%. In addition, average precision is 0.68 and recall is 0.724, which are considered accurate. Therefore, the proposed model can play a vital role in assisting students and academic advisers to recommend the right courses during registration, thereby overcoming the limitations of academic advising.