Using educational data mining techniques to increase the prediction accuracy of student academic performance. Issue 7 (16th August 2019)

• Record Type: Journal Article
• Authors: Ramaswami, Gomathy; Susnjak, Teo; Mathrani, Anuradha; Lim, James; Garcia, Pablo
• Is Part Of: Information and learning sciences. Volume 120:Issue 7/8(2019)
• Page Start: 451
• Page End: 467
• Publication Date: 2019-08-16
• Subjects: Classification -- Model evaluation -- Predictions -- Educational data mining -- Process mining -- Data mining technique; Information science -- Periodicals; Library science -- Periodicals; Information theory in education -- Periodicals; Libraries and education -- Periodicals; 020
• DOI: 10.1108/ILS-03-2019-0017
• Languages: English
• ISSNs: 2398-5348

Abstract

Abstract : Purpose: This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation. Design/methodology/approach: Classification data mining techniques have been adapted to predict students' academic performance. Four algorithms, Naïve Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions. Findings: The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students' final-year academic performance for the subsequent year. Research limitations/implications: The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students' performance. Future work will address some of these limitations. Originality/value: The model developed from this research can provide value to institutions in making process- and data-driven predictions on students' academic performances.