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    Developing classifier for the prediction of students’ performance using data mining classification techniques
    (Altınbaş Üniversitesi, 2020) Rimi, Abubakar Auwa; Ibarihim, Abdullahi Abdu; Bayat, Oğuz
    Data mining is used in academic institutions to predict the performance of students using classification techniques. These techniques are applied on students’ features in order to find reasonable patterns that can be used as basis for the prediction. The availability of students’ data in digital form and increase in processing power of computer systems makes this whole process a reality. There are numerous researches done in this direction in order to prevent massive failure of students. However, these researches are focused mainly on the prediction of students from other countries. Although there are efforts by few indigenous researchers to perform research in this direction, they have not explored the most widely used features. The main aim of this research is to develop a classifier using locally generated students’ features for accurate performance prediction. The students’ features that are collected from different sources underwent preprocessing, which later were introduced into the weka for feature selection and eventually for learning and testing. The naïve Bayes classifier which emerged as the most accurate classifier was selected and implemented in our performance predictor tool. The tool was tested using another set of features and the evaluation result shows that the tool can predict the performance of students in their future examinations.