Author : R. kaviyarasi , t. balasubramanian
Keyword : Data mining, edm, learners, j48, naïve bayes, reptree
Subject : Educational technology
Article Type : Original article (research)
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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’ performance of college students, many empirical 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 o 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 p classifier models to predict the academic performance of students in the field of Educational Data Mining.
Article by : KAVIYARASI R
Article add date : 2020-07-12
How to cite : R. kaviyarasi , t. balasubramanian. (2020-July-12). Predicting the academic performance of college students through machine learning techniques. retrieved from https://www.openacessjournal.com/abstract/28