A survey on educational data mining methods used for predicting students' performance. Issue 5 (13th December 2021)

• Record Type: Journal Article
• Authors: Xiao, Wen; Ji, Ping; Hu, Juan
• Is Part Of: Engineering reports. Volume 4:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-13
• Subjects: educational data mining -- machine learning -- predicting students' performance -- survey; Engineering -- Periodicals; Computer science -- Periodicals; 620.005
• DOI: 10.1002/eng2.12482
• Languages: English
• ISSNs: 2577-8196

Abstract

Abstract: Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016–2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research. Abstract : We reviewed the important researches on predicting students' performance using educational data mining methods in recent 5 years, synthesized the procedure of building prediction model of students' performance, compared and discussed the EDM methods used in different studies. We also analyzed the main challenges in this field and gave some suggestions for future work.