Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2023)

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KNN Classification of Kolb Learning Styles: A Comparative Study on Balanced and Unbalanced Datasets

Authors
Waladi Chaimae1, *, Lamarti Sefian Mohammed1, Khaldi Maha2, Khaldi Mohamed2, Boudra Said3
1Applied Mathematics and Computer Sciences, Normal School of TETOUAN, ABDEL MALEK ESSAIDI University, Tétouan, Morocco
2Laboratory of Applied Sciences and Didactics, Normal School of TETOUAN, ABDEL MALEK ESSAIDI University, Tétouan, Morocco
3Laboratory Of Applied Chemistry And Biology And Biotechnology, Normal School of TETOUAN, ABDEL MALEK ESSAIDI University, Tétouan, Morocco
*Corresponding author. Email: waladichaimaa@gmail.com
Corresponding Author
Waladi Chaimae
Available Online 5 February 2024.
DOI
10.2991/978-94-6463-360-3_6How to use a DOI?
Keywords
e-learning; machine learning; KNN; SMOTE; F1 score; precision; recall; accuracy; Kolb learning style
Abstract

In this work, the K-Nearest Neighbors (KNN) algorithm’s performance was compared across two datasets with various class distributions and sizes. The goal variable, Kolb learning style, and three features—total reading time, total problem-solving time, and total technical demonstration time—were the identical across both datasets. The first dataset had 150 samples with equal class distributions for learning styles that converge, diverge, and assimilate. 306 samples made up the second dataset, which had unbalanced class distributions. The accuracy for the first and second datasets for the KNN algorithm was 86.67% and 98.36%, respectively. The results demonstrated that the KNN algorithm performed well on both datasets. According to the results, the KNN technique can be applied successfully to both balanced and imbalanced datasets, however the class distribution can affect how well the algorithm performs. Consequently, while using the KNN method to their datasets, researchers should carefully analyze the class distribution.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2023)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
5 February 2024
ISBN
978-94-6463-360-3
ISSN
2667-128X
DOI
10.2991/978-94-6463-360-3_6How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Waladi Chaimae
AU  - Lamarti Sefian Mohammed
AU  - Khaldi Maha
AU  - Khaldi Mohamed
AU  - Boudra Said
PY  - 2024
DA  - 2024/02/05
TI  - KNN Classification of Kolb Learning Styles: A Comparative Study on Balanced and Unbalanced Datasets
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2023)
PB  - Atlantis Press
SP  - 43
EP  - 49
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-360-3_6
DO  - 10.2991/978-94-6463-360-3_6
ID  - Chaimae2024
ER  -