DEVELOPING PREDICTIVE DATA MINING MODEL FOR GRADE 11TH STUDENT PERFORMANCE: IN-CASE OF ELU WOREDA SECONDARY SCHOOL

Abstract

Education is a key factor for achieving continuing economic progress. Many students are getting less in their result for many reasons. More academic institutes now store massive student educational and related data. A large number of students will enter every year. So, the demanding growth of data in education sectors continues. Handling and analyzing such a high amount of raw data manually for performance evaluation creates dissatisfaction, boring and unsuccessful. Manually handling and evaluating such a big amount of data for performance evaluation leads to discontent, boredom, and failure. To put it another way, traditional methodologies are overly complex and difficult to analyze and evaluate. To solve such kind’s issues, we use a data mining technique, which is a set of machine learning algorithms, to examine the data .In other words automated discovery of previously unknown, valid, novel, useful, and understandable patterns in school databases. The study's major goal is to use data mining to create a predictive model for student academic performance in Oromia National Region State's South West Shoa zone Elu woreda Teji Senior Secondary School and Asgori secondary schools. This can greatly was support policymakers, planners, and education providers working on the control of student performance. The methodology used for this research will a hybrid six-step CRISP Knowledge Discovery Process. The essential data would be gathered from a school data ware house created specifically for student result purposes, would be collected from 2011 to 2016 E.C. The researcher used three popular data mining algorithms (J48 Decision Trees, Random forest and Naive Bayes Classifier) to develop the predictive model using a larger dataset (4000 cases). The researcher used 10-fold cross-validation and percentage split test mode for data mining algorithms of the three predictive models for performance comparison purposes. The results indicated that the J48 Decision tree with10-foldcross-validation mode and Random Forest with 85% split testis the better predictor due to the nature of data which is categorical for which this two algorithm is better. Both algorithm (the J48 Decision tree with10-foldcross-validation mode and Random Forest with 85% split test) has 100% accuracy on the given school dataset and Naïve Bayes came out to be the second with an accuracy of 97.5%