In higher educational institutes, many students have to struggle hard to complete different courses since there is no dedicated support offered to students who need special attention in the registered courses. Machine learning techniques can be utilized for students' grades prediction in different courses. Such techniques would help students to improve their performance based on predicted grades and would enable instructors to identify such individuals who might need assistance in the courses. In this paper, we use Collaborative Filtering (CF), Matrix Factorization (MF), and Restricted Boltzmann Machines (RBM) techniques to systematically analyze a real-world data collected from Information Technology University (ITU), Lahore, Pakistan. We evaluate the academic performance of ITU students who got admission in the bachelor's degree program in ITU's Electrical Engineering department. The RBM technique is found to be better than the other techniques used in predicting the students' performance in the particular course.
Grade prediction accuracy using Matrix Factorization (MF) method degrades when dealing with small sample sizes. @cite_30 investigated different recommender system techniques to accurately predict the students' next term course grades as well as within the class assessment performance of George Mason University (GMU), University of Minnesota (UMN) and Stanford University (SU). Their study revealed that both Personalized Multi-Linear Regression models (PLMR) and advance Matrix Factorization (MF) techniques could predict next term grades with lower error rate than traditional methods. PLMR was also useful for predicting grades on assessments within a traditional class or online course by incorporating features captured through students' interaction with LMS and MOOC server logs.
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To help solve the ongoing problem of student retention, new expected performance-prediction techniques are needed to facilitate degree planning and determine who might be at risk of failing or dropping a class. Personalized multiregression and matrix factorization approaches based on recommender systems, initially developed for e-commerce applications, accurately forecast students' grades in future courses as well as on in-class assessments.