AIRCC's International Journal of Computer Science and Information Technology

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The ability to recognize students' weakness and solving any problem may confront them in timely fashion is always a target of all educational institutions. This study was designed to explore how can predictive and statistical analysis support the academic advisor's work mainly in analysis students' progress. The sample consisted of a total of 249 undergraduate students; 46% of them were Female and 54% Male. A one-way analysis of variance (ANOVA) and t-test were conducted to analysis if there was different behaviour in registering courses. Predictive data mining is used for support advisor in decision making. Several classification techniques with 10-fold Cross-validation were applied. Among of them, C4.5 constitutes the best agreement among the finding results.

Keywords

Academic Advisory, Predictive and Statistical Analyses, Data Mining, C4.5, K-Nearest Neighbour, Naive Bayes Classifier.
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