Abstract:
The paper attempts to predict the performance of the students, through experimental
analysis and predictive modeling. There is a lack of predictive studies and models used in
the Ethiopian context to accurately determine the influencing factors of the students’
performance in academics by categorizing student status into dropout/fail, poor, good,
excellent or average performer. Many educational institutions have still no strategic plan to
predict or determine the student’s performance in order to improve it, reduce dropouts
and help to implement the curriculum/academic policies based on student’s performance
and status. The study aims at conducting a comparative analysis and predictive modeling
for knowing the student’s performance status through data mining techniques. The study
uses the KDD process model and interpret patterns in repositories. Decision tree (J48 and
Random Forest), Bayes (NaiveBayes and BayesNet) and Rule-Based (JRip and PART)
algorithms are used for classification. The results reveal that the overall accuracy of the
tested classifiers is above 80%. In addition, classification accuracy for the different classes
reveals that the predictions are worst for fail class and fairly good for the average class. The
J48 and JRip classifiers relatively produce the highest classification accuracy for the average
performer/ status. Finally, the study suggests that data mining can be used as a significant
technique to figure out student’s performance based on salient factors affecting.
