A comparison of the utility of data mining algorithms in an open distance learning context - Fynn, A.; Adamiak, J. (South African Journal of Higher Education, 2020)
<span style="line-height: 150%; font-family: ’Arial’,sans-serif; font-size: 12pt; mso-ascii-theme-font: minor-bidi; mso-hansi-theme-font: minor-bidi; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-GB; mso-fareast-language: EN-ZA;" lang="EN-GB">The use of data mining within the higher education context has, increasingly, been gaining traction. A parallel examination of the accuracy, robustness and utility of the algorithms applied within data mining is argued as a necessary step toward entrenching the use of EDM. This paper provides a comparative analysis of various classification algorithms within an Open Distance Learning institution in South Africa. The study compares the performance of the <span style="line-height: 150%; font-family: ’Arial’,sans-serif; font-size: 12pt; mso-ascii-theme-font: minor-bidi; mso-hansi-theme-font: minor-bidi; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-GB; mso-fareast-language: EN-ZA; mso-fareast-font-family: ’Times New Roman’;" lang="EN-GB">ZeroR, OneR, Naïve Bayes, IBk, Simple Logistic Regression and the J48 in classifying students within a cohort over an 8 year timespan. The initial results appear to show that, given the data quality and structure of the institution under study, the J48 most consistently performed with the highest levels of accuracy.