Predicting student performance in higher education using multi-regression models

• Published in: Telkomnika Vol. 18; no. 3; pp. 1354 - 1360
• Main Authors: Santoso, Leo Willyanto, Yulia, Yulia
• Format: Journal Article
• Language: English
• Published: Yogyakarta Ahmad Dahlan University 01.06.2020
• Subjects: Algorithms; At risk students; Big Data; Colleges & universities; Data mining; Data warehouses; Datasets; Design; Developing countries; Distance learning; Education; Higher education; Information systems; LDCs; Learning; Learning management systems; Performance evaluation; Performance prediction; Quality of education; Recommender systems; Regression analysis; Regression models; Students
• ISSN: 1693-6930; 2302-9293
• DOI: 10.12928/telkomnika.v18i3.14802

By implementing data mining model in IIS, this feature could precisely predict the student grade for their enrolled subjects. [...]it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. [...]data mining and data warehousing technique have been increasingly implemented in the academic information system to analyze the vast amounts of student data [8, 9]. [...]section 4 concludes this research. 2.RESEARCH METHOD Identifying at-risk students for taking appropriate actions can be addressed through evaluating collected students academic performance data. Learning analytic be able to support instructional material designers to better measure the quality of a course design and understand what works and what does not work [21, 22]. [...]learning analytic can increase evaluation of student performance by investigating various indicators such as student activities and grades on assignments.