Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis - Zhang, Yupei; Yun, Yue; An, Rui; Cui, Jiaqi; Dai, Huan; Shang, Xuequn (Frontiers in Psychology, 2021)
Student performance prediction (SPP) aims to evaluate the grade that a student will reach before enrolling in a course or an exam. This prediction problem is a primary task towards personalized education and has been increasingly paid attention to in artificial intelligence and educational data mining. This paper provides a systematic review of the SPP study from machine learning and data mining. This review mainly includes the following five stages: data collection, problem formalization, model, prediction, and result application. We conducted experiments using all involved methods on a data set from our institute and a public data set. Our educational data set, composed of 1325 students and 832 courses, was collected by the information system for higher education. Based on experimental results, discussions on current shortcomings and several interesting future works are finally summarized, from data collections to practices. This work shows developments and challenges in SPP researches and facilitates the progress of personalized education.