Deep Learning and Collaborative Filtering-Based Methods for Students’ Performance Prediction and Course Recommendation

At the beginning of a new semester, due to the limited understanding of the new courses, it is difficult for students to make predictive choices about the courses of the current semester. In order to help students solve this problem, this paper proposed a hybrid prediction model based on deep learning and collaborative filtering. The proposed model can automatically generate personalized suggestions about courses in the next semester to assist students in course selection. The two important tasks of this study are course recommendation and student ranking prediction. First, we use a user-based collaborative filtering model to give a list of recommended courses by calculating the similarity between users. Then, for the courses in the list, we use a hybrid prediction model to predict the student’s performance in each course, that is, ranking prediction. Finally, we will give a list of courses that the student is good at or not good at according to the predicted ranking of the courses. Our method is evaluated on students’ data from two departments of our university. Through experiments, we compared the hybrid prediction model with other nonhybrid models and confirmed the good effect of our model. By using our model, students can refer to the different recommendation lists given and choose courses that they may be interested in and good at. The proposed method can be widely applied in Internet of Things and industrial vocational learning systems.