International Journal of Advances in Electrical Engineering
2021, Vol. 2, Issue 1, Part A
Performance evaluation of student academic results using clustering
Author(s): Dr. Adenowo Adetokunbo O, Keshinro Kazeem Kolawole, Green Oluwole A and Balogun Wasiu Adebayo
Abstract: Student’s academic performance is an essential object in the institutions of higher learning for predicting systems of modification that could enhance better academic performance in an institution. Several performance evaluation algorithms have been proposed at Lagos State Polytechnic, South-Western Nigeria, but there is a need to find the best prediction techniques for students’ academic progress and performance. This is done by engaging the use of statistical analysis in MINITAB. The students’ academic results are examined on a semester-by-semester basis rather than using the overall result or cumulative grade point average (CGPA). This creates clusters in the linear regression graph, which would be used to summarize the performance level at the end of a semester. The clustering of each point to the line of best fit indicates the model is best described by the “linear model” used in this research work for the data analysis This research work provides a superlative student performance evaluation technique in data mining, as well as the most important student attributes for performance prediction. It further provides student performance enhancement skills with evaluation benefits to students, faculty, and the school management.
Pages: 36-40 | Views: 198 | Downloads: 98
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How to cite this article:
Dr. Adenowo Adetokunbo O, Keshinro Kazeem Kolawole, Green Oluwole A, Balogun Wasiu Adebayo. Performance evaluation of student academic results using clustering. Int J Adv Electr Eng 2021;2(1):36-40.