PREDICTING HIGHER EDUCATION STUDENTS’ PERFORMANCE USING DATA MINING TECHNIQUES: THE CASE OF ETHIOPIAN POLICE UNIVERSITY

Predictive modelling for students’ performance is an innovative methodology that can be utilized by higher education institutions. Accurate and reliable student performance forecasts in higher education are crucial in order to minimize inefficient utilization of resources and funds at universities. The objective of this research is to develop a predictive model for higher education students’ performance using data mining techniques. The study followed the six-step hybrid methodology of the Knowledge Discovery Process model, such as understanding of the problem domain, understanding of the data, preparation of the data, data mining, and evaluation of the discovered knowledge and use of the discovered knowledge to achieve the goal. The study tries to understand factors affecting higher education student performance based on the data collected from Ethiopian Police University from the years 2008 up to 2012 E.C. After data preparation using data cleaning, classification algorithms such as J48 Decision Tree, PART Rule induction, Nave Bayes, Logistic regression, Support Vector Machines, and Multilayer Perception Neural Network were used for all experiments due to their popularity in recent related works. The study used a dataset containing 5254 instances, 18 attributes, and one outcome variable to run the experiments. The WEKA 3.9.5 open source software was used as a data mining tool to implement the experiments. The study also used a 10-fold cross validation and 66% split test modes for splitting the data into training and test datasets. The result of the study showed that the J48 decision tree algorithm has registered the best classification accuracy of 98.4%. The results obtained in this study are interesting and encourage the design of a model that predicts higher institution students' performance. The major factors affecting the students' performance were identified as previous study GPA, previous study field, program type, students’ performance, batch, financial source, and students’ status. In this study, an attempt was made to show the use of knowledge extracted by data mining. In the future, we recommend the automatic integration of data mining with a knowledge system so as to design an intelligent system.