The Development of a Predication Model for Academic Achievement of Students During a Learning Process by Using Data Mining Techniques
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Abstract
The purpose of this research was to develop a prediction model for educational achievement of students during the study by using mining techniques. The data were obtained from academic transcripts of graduate students in the Information Technology Program and Computer Science Program. The data included the grade levels of all courses and the Grade Point Average (GPA.). The data were divided into 7 sub-series based on the semesters. The model was developed with the use of four data mining techniques, including the multiple linear regression, simple linear regression, multilayer perceptron, and support vector machine for regression. The results showed that the prediction model of both programs had more accuracy to predict the study’s result when the students studied in higher year levels. The RMSE of the study’s result when the students graduated were 0.09 to 0.28, and the RAE were at 18.40 - 60.20 levels.
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