Predicting Student Success in Courses via Collaborative Filtering

Abstract

Based on their skills and interests, students' success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that they are likely to pass with good grades. Besides, instructors may have an idea about the expected success of students in a class, and may restructure the course organization accordingly. In this paper, we propose a collaborative filtering-based method to estimate the future course grades of students. Besides, we further enhance the standard collaborative filtering by incorporating automated outlier elimination and GPA-based similarity filtering. We evaluate the proposed technique on a real dataset of course grades. The results indicate that we can estimate the student course grades with an average error rate of 0.26, and the proposed enhancements improve the error value by 16%.

Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis

“…All involved studies have the final goal that is to improve the student learning performance (Liu et al, 2018 , 2019 ; Anand, 2019 ; Wang et al, 2020 ), as well as other additional goals like reducing educational costs (Gronberg et al, 2004 ). As a result, in the past decades, various researches were concentrated on student performance prediction, referred to as SPP in this paper, (Sweeney et al, 2015 ; Polyzou and Karypis, 2016 ; Thanh-Nhan et al, 2016 ; Cakmak, 2017 ; Hu et al, 2017 ; Morsy and Karypis, 2017 ) or were evaluated by the student's final grades (Al-Radaideh et al, 2006 ; Shovon et al, 2012 ; Ahmed and Elaraby, 2014 ; Meier et al, 2015 ; Al-Barrak and Al-Razgan, 2016 ). While several review papers have summarized previous EDM research studies (Shahiri and Husain, 2015 ; Saa, 2016 ), this paper provides a more completed survey on the problem of SPP from the perspective of machine learning and data mining.…”

“…The clustering formulation is to group the into multi-clusters, where each cluster contains the instances with high similarities. Many studies partition X into different clusters based on students and/or courses in SPP (Cakmak, 2017 ). The classification formulation aims to predict the discrete grade using a machine-learning classifier, such as logic regression (Elbadrawy et al, 2014 ) and support vector machine (SVM) (Xu and Yang, 2016 ).…”

An Automated Recommender System for Educational Institute in India

“…Al-Badarenah and Alsakran [9] presented a collaborative filtering RS using the technique of clustering for the generation of elective courses by making use of rules of association to suggest courses based on liking parameters. Cakmak [10] designed an RS technique that is used for the estimation of student course grades. They also increase the quality of result generation by implementing automated outlier removal.…”

A Review of Recommender Systems for Choosing Elective Courses

“…Students chose by a student elective courses based on their interests. Predicting student grades in the courses, they will enroll for is useful for guiding students and allowing them to make informed choices regarding compulsory, and elective courses [3].…”

“…The ability to predict student enrolment patterns for courses provides an opportunity for HEIs to be effective in allocating resources and providing a high-quality learning experience [6]. Predicting student grades in future courses before they take them is an essential tool that can be used to assist students with choosing elective courses [3].…”