Open AccessEducational data mining for students’ academic performance analysis in selected Ethiopian universities
Author(s) -
Alemu K. Tegegne,
Tamir Anteneh Alemu
Publication year - 2018
Publication title -
information impact
Language(s) - English
Resource type - Journals
ISSN - 2360-994X
DOI - 10.4314/iijikm.v9i2.1
Subject(s) - educational data mining , decision tree , computer science , decision tree learning , set (abstract data type) , data science , data set , data mining , association rule learning , tree (set theory) , machine learning , artificial intelligence , mathematics , mathematical analysis , programming language
Universities are working in a very dynamic and powerfully viable environment today. Due to the advent of information technology, they gather large volumes of data related to their students in electronic form in various formats like records, files, documents, images, sound, videos, scientific data and many new data formats. This study focuses on predicting performance of student at an early stage of the degree program, in order to help the university not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. The aim of this paper is to use data mining methodologies to design and develop a Data Mining model to predict academic performance of students at the end of first year degree program in selected Ethiopian higher educational institutions (universities).The data of different undergraduate students has been mined with decision tree classifiers. A model is built using C4.5 Decision tree learning algorithm – generates five classification rule set classifiers (predictors) in an experiment. The experiment using a test data set produces 81.4% accuracy.Keywords: Educational Data, Educational Data mining, Decision tree, Classification rule, C4.5