Student Performance Prediction in Higher Education: A Comprehensive Review

Tjandra, Ellysa and Kusumawardani, Sri Suning and Ferdiana, Ridi (2022) Student Performance Prediction in Higher Education: A Comprehensive Review. In: AIP Conference Proceedings 2470, 3rd BIANNUAL INTERNATIONAL CONFERENCE ON INFORMATICS, TECHNOLOGY, AND ENGINEERING 2021 (InCITE 2021): Leveraging Smart Engineering, 25–26 August 2021, Surabaya, Indonesia (Online).

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Student dropout still becomes a critical problem in education. Educational Data Mining (EDM) can bring potential impact to support academic institution’s goals in making academic decisions, such as regulation renewal, rule enforcement, or academic process improvement. The sooner at-risk students can be identified, the earlier institution members can provide necessary treatments, thus prevent them from dropout and increase the student retention rate. This study performs a comprehensive literature review of student performance prediction using EDM techniques, including various research from 2002 to 2021. Our study is aimed to provide a comprehensive review of recent studies based on student performance prediction tasks, predictor variables, methods, accuracy, and tools used in previous works of student performance prediction. Performing student performance prediction in an academic institution can be helpful to provide the student performance mitigation mechanism because it can be managed earlier by the management to decrease the student dropout rate.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: student performance, prediction, student dropout, Educational Data Mining, EDM review
Subjects: L Education > L Education (General)
T Technology > T Technology (General)
Divisions: Faculty of Engineering > Department of Informatic
Depositing User: Ellysa Tjandra 61144
Date Deposited: 27 May 2022 07:46
Last Modified: 27 May 2022 07:46

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