Analyzing undergraduate students' performance using educational data mining

doi:10.1016/j.compedu.2017.05.007

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Analyzing undergraduate students' performance using educational data mining

2017-10-01

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DOI：10.1016/j.compedu.2017.05.007

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Abstract

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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. (C) 2017 Elsevier Ltd. All rights reserved.

Keywords:

Data mining

Decision trees

Clustering

Performance prediction

Performance progression

Quality of educational processes

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