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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 7

Published on: July 2026

PREDICTING STUDENT ACADEMIC PERFORMANCE OF LEARNING IN EDUCATIONAL DATA MINING PARADIGM USING KNN-GA

Anand Tiwari

Dr.Dayashankar Pandey

Dept.of Information Technology , RKDF IST SRK UNIVERSITY, BHOPAL
DOI:https://doi.org/10.55041/ijcope.v2i7.214

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Abstract

The performance of students in higher education is a most challenge task day by day in academic as well as in other curricular activities. Today's everybody can find the huge amount of data stored in the web technology, but they don't relies that what data is suitable? As they all know that internet technology is growing as much as faster, but the learning approach of students are not up to the mark. The emerging research community which helps to find the solution to the said problem is Educational Data Mining. In present scenario, the huge students' data is stored in educational database. That type of database contains widely open or secret information to improve student performance. In our proposed work, we have tested it on reputed dataset, which has been downloaded from a well known organization UCI repository and dataset name is student-mat.csv. This work has investigated the process of classification of plethora of students' data. The work has been divided into two parts. The first part covers the entropy based feature selection, after that classification process has been performed. For the classification, we have used the KNN-GA method and we observed that the accuracy of proposed method is too good when compared with some previous methods. The accuracy of the proposed method was found to be more than 91% whereas the existing method has approx 81% accuracy.

 

Keywords : Educational data mining , Student Learning Outcome (SLO), Decision tree, SVM, K-Nearest Neighbor, Genetic Algorithm. Moodle; learning management system (LMS); educational data mining (EDM); synchronous and asynchronous learning; student engagement; personalized learning; higher education; machine learning algorithms; online learning platforms

How to Cite this Paper

Tiwari, A. (2026). Predicting Student Academic Performance of learning in Educational Data Mining Paradigm using KNN-GA. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(7), 1-9. https://doi.org/10.55041/ijcope.v2i7.214

Tiwari, Anand. "Predicting Student Academic Performance of learning in Educational Data Mining Paradigm using KNN-GA." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 7, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i7.214.

Tiwari, Anand. "Predicting Student Academic Performance of learning in Educational Data Mining Paradigm using KNN-GA." International Journal of Creative and Open Research in Engineering and Management 02, no. 7 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i7.214.

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References


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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Jul 25 2026
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