Mining Educational Data to Predict Learners’ Performance Using Decision Tree Algorithm

DOI: 10.5176/2251-2195_CSEIT18.140

Authors: Ean Teng Khor

Abstract: Data mining is gaining increasing traction in the field of education as its applications in the education sector has increased over the past few years. Different data mining methods can be used to gain insights into educational data, including the uncovering of hidden patterns and prediction of output. The methods include classification analysis, association rule learning, anomaly or outlier detection, clustering analysis, and regression analysis. In this study, the classification analysis is used with decision tree algorithms to predict learners' performance. The findings reveal that the algorithm can be used to build a predictive model with good performance measure based on accuracy level, true positive (TP) rate, and false positive (FP) rate.

Keywords:Educational Data Mining, Classification, Decision Tree Algorithm, Predictive Modeling, Learning Management System, Learners’ Performance