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Title |
Study Factors for Student Performance Applying Data Mining Regression Model Approach |
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Author |
Shakir Khan |
| Citation |
Vol. 21 No. 2 pp. 188-192 |
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Abstract |
In this paper, we apply data mining techniques and machine learning algorithms using R software, which is used to predict, here we applied a regression model to test some factor on the dataset for which we assumed that it effects student performance. Model was built on an existing dataset which contains many factors and the final grades. The factors tested are the attention to higher education, absences, study time, parent’s education level, parent’s jobs, and the number of failures in the past. The result shows that only study time and absences can affect the students’ performance. Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a student’s data. |
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Keywords |
data mining technique; big data , student performance; Linear Regression Model |
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