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City University of New York (CUNY)
Ensemble Classifiers; Student Academic Prediction; Data Mining; Engineering Education
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Full-Text Articles in Physical Sciences and Mathematics
Data Mining Using Ensemble Classifiers For Improved Prediction Of Student Academic Performance, Ashwin Satyanarayana, Mariusz Nuckowski
Data Mining Using Ensemble Classifiers For Improved Prediction Of Student Academic Performance, Ashwin Satyanarayana, Mariusz Nuckowski
Publications and Research
In the last decade Data mining (DM) has been applied in the field of education, and is an emerging interdisciplinary research field also known as Educational Data Mining (EDM). One of the goals of EDM is to better understand how to predict student academic performance given personal, socio-economic, psychological and other environmental attributes. Another goal is to identify factors and rules that influence educational academic outcomes. In this paper, we use multiple classifiers (Decision Trees-J48, Naïve Bayes and Random Forest) to improve the quality of student data by eliminating noisy instances, and hence improving predictive accuracy. We also identify association …