Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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Prediction of Student’s Academic Performance using k-Means Clustering and Multiple Linear Regressions
Oladele Tinuke Omolewa, Ogundokun Roseline Oluwaseun, Adegun Adekanmi Adeyinka and Aro Taye Oladele
Page: 8254-8260 | Received 21 Sep 2022, Published online: 21 Sep 2022
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Abstract
In today’s educational system, performances of students are mainly based on tests, assignments, attendance, quizzes and final examination. It is at the end of this exercise that a minimum mark is determined on which promotion will be based. There is need to identify factors that lead to a student’s success or failure. This will allow the teacher to provide appropriate counselling and focus more on such factors. Hence, a model for forecasting student’s performance academically is of a pronounced significance, therefore, data mining techniques in classifying and forecasting the academic performance of students was put into application in this research study. k-means clustering and Multiple Linear Regression (MLR) were used for assessing student’s performance. The results showed that student’s test scores, quiz and assignment were the major factors that could be used in predicting academic performance of students. Also, two clusters were derived with the use of elbow method to group all the students into clusters.
How to cite this article:
Oladele Tinuke Omolewa, Ogundokun Roseline Oluwaseun, Adegun Adekanmi Adeyinka and Aro Taye Oladele. Prediction of Student’s Academic Performance using k-Means Clustering and
Multiple Linear Regressions.
DOI: https://doi.org/10.36478/jeasci.2019.8254.8260
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2019.8254.8260