OLAP Mining with Educational Data Mart to Predict Students’ Performance

Abstract

Academic institutions always try to use a solid platform for supporting their short-to-long term decisions related to academic performance. These platforms utilize the historical data and turn them into strategic decisions. The hidden patterns in the data need tools and approaches to be discovered. This paper aims to present a short roadmap for implementing educational data mart based on a data set form Alexandria Private Elementary School, located in Basrah province of Iraq in the 2017-2018 academic year. The educational data mart is implemented, then the cube is constructed to perform OLAP operations and present OLAP reports. Next, OLAP mining is performed on the educational cube using nine algorithms, namely: decision tree with score method (entropy) and split method (complete)), decision tree with score method (entropy) and split method (complete)), decision tree with score method (entropy) and split method (both)), Logistic, Naïve Bayes, Neural Network, clustering with expectation maximization, clustering with K-means clustering, and Association rules mining. According to comparison of all algorithms, clustering with expectation maximization proved the highest accuracy with 96.76% for predicting the students’ performance and 96.12% for predicting students grades amongst all other algorithms