Abstract
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
Highlights
The advances in the data mining field make it possible to mine the educational data and find information that allow for innovative ways of supporting teachers, students, and decision makers
The main functions of data mining are applying various methods and algorithms in different applications such as biological data analysis, financial data analysis, transportation, and forecasting/prediction (Mannilla, 1996). Such data mining and knowledge discovery applications have got a great attention due to its significance in decision making and it has become an essential component in various organizations including universities where educational data is mostly available
Knowledge extraction has got an additional opportunity since data mining techniques have been introduced into new fields of Statistics, Databases, Machine Learning, Pattern Reorganization, Artificial Intelligence and Computation capabilities, etc
Summary
Introduction The advances in the data mining field make it possible to mine the educational data and find information that allow for innovative ways of supporting teachers, students, and decision makers. This study will explore the ability to mine available data using data mining algorithms, and the information acquired be able to associate what factors are more predictive for the success or failure of students, how those critical factors can be fine-tuned for effecting better performance of students, and looking for optimal rule (model) in the predication of the overall students’ academic performance.
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