Regression Model for Predicting Engineering Students Academic Performance
R.R. Rajalaxmi1, P. Natesan2, N. Krishnamoorthy3, S. Ponni4
1R.R. Rajalaxmi, Professor, Department of Computer Science & Engineering, Kongu Engineering College, Erode (Tamil Nadu), India.
2P. Natesan, Associate Professor, Department of Computer Science & Engineering, Kongu Engineering College, Erode (Tamil Nadu), India.
3N. Krishnamoorthy, Assistant Professor (Sr.Gr), Department of Computer Science & Engineering, Kongu Engineering College, Erode (Tamil Nadu), India.
4S. Ponni, Research Assistant, Department of Computer Science & Engineering, Kongu Engineering College, Erode (Tamil Nadu), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 07 May 2019 | PP: 71-75 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1015376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction of academic performance of the students helps the educator to develop the good understanding of student’s community and take the healthy measures to make their learning comfortable and understandable. Many data mining techniques can be used to predict the performance of the students in academics. But the main objective of the paper is to use linear regression techniques to build a model which predicts the performance of the students in Engineering Discipline. The predictor or independent variables of the model contain how many hours spent on the internet in some activities based on the data collected. The output or dependent variable is the prediction of end semester examination grades i.e. CGPA (Cumulative Grade Points). Multiple measures are used to calculate and corroborate the models that were predicted along with the percentage of good predictions. The results show that the predicted model gives the better accuracy in prediction.
Keywords: Data Mining; Engineering Students; Internet Use; Regression.
Scope of the Article: Regression and Prediction