Analyzing undergraduate students' performance using educational data mining. (October 2017)

• Record Type: Journal Article
• Authors: Asif, Raheela; Merceron, Agathe; Ali, Syed Abbas; Haider, Najmi Ghani
• Is Part Of: Computers & education. Volume 113(2017)
• Page Start: 177
• Page End: 194
• Publication Date: 2017-10
• Subjects: Data mining -- Decision trees -- Clustering -- Performance prediction -- Performance progression -- Quality of educational processes; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2017.05.007
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: The tremendous growth in electronic data of universities creates the need to have some meaningful information extracted from these large volumes of data. The advancement in the data mining field makes it possible to mine educational data in order to improve the quality of the educational processes. This study, thus, uses data mining methods to study the performance of undergraduate students. Two aspects of students' performance have been focused upon. First, predicting students' academic achievement at the end of a four-year study programme. Second, studying typical progressions and combining them with prediction results. Two important groups of students have been identified: the low and high achieving students. The results indicate that by focusing on a small number of courses that are indicators of particularly good or poor performance, it is possible to provide timely warning and support to low achieving students, and advice and opportunities to high performing students. Highlights: Data mining methods are used to study the performance of undergraduate students. Two aspects of students' performance have been focused upon. Firstly, predicting students' academic achievement at the end of a four-year study programme. Secondly, studying typical progressions throughout the four academic years. Combinations of the progression and prediction results are formulated.