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Predicting the academic performance of college students through machine learning techniques

Author Affiliations

  • 1Department of Computer Science, Periyar University, Salem-636011, Tamilnadu, India
  • 2Department of Computer Science, Periyar University, Salem-636011, Tamilnadu, India

Res. J. Computer & IT Sci., Volume 6, Issue (6), Pages 1-10, October,20 (2018)

Abstract

Data Mining is one of the interdisciplinary subfield of Computer Science and by means of data analysis; it explains the past and predicts the future. Educational Data Mining (EDM) is one of the applications of Data Mining, Machine Learning and Statistics to generate the information from various educational settings such as universities and intelligent tutoring systems that has a vital impact on predicting students' academic performance. To predict and explore the factors affecting the performance of college students, many empirical researches are carried out. The main focus of this research is to identify the slow learners from the taken dataset which contains the students' profile details associated with their internal examination details. The student dataset is tested and applied on several classification models such as J48, Naïve Bayes and REPTree using an open source tool WEKA. The statistics are generated to predict the best accuracy based on classification algorithms and comparison of these classifiers is done to find the best performing classifier among others. This study explores the classifier models to predict the academic performance of students in the field of Educational Data Mining.

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