Machine learning algorithm, scaling technique and the accuracy: an application to educational data.

Wickramasinghe, Indika; Kalutarage, Harsha

Abstract

Machine learning (ML) applications in educational data mining have become an increasingly popular research area. Literature indicates a lack of research investigating the impact of data scaling techniques, ML algorithms, and the nature of data on the classification's accuracy. This study aims to fulfill the above. In that direction, we use three linear and three non-linear ML classifiers and six scaling techniques to evaluate the impact of the data scaling technique and the ML algorithm on four selected educational datasets. According to the experimental outcomes for data set #1, classification accuracy was significantly influenced (p-value < 0.01) by the nature of the data. All the performance indicators except detection rate and prevalence were highly influenced by the type of ML technique used for the classification. Furthermore, there was a significant (p-value < 0.05) interaction impact of two-way interactions of the nature of the data and the type of ML technique for F1 value and balanced accuracy. Further analysis indicates that the classification accuracy varies with the level of the class variable.

Citation

WICKRAMASINGHE, I. and KALUTARAGE, H. 2024. Machine learning algorithm, scaling technique and the accuracy: an application to educational data. In Proceedings of the 12th International conference on information and education technology 2024 (ICIET 2024) 18-20 March 2024, Yamaguchi, Japan. Piscataway: IEEE [online], pages 6-12. Available from: https://doi.org/10.1109/iciet60671.2024.10542714

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 12th International conference on information and education technology 2024 (ICIET 2024)
• Start Date: Mar 18, 2024
• End Date: Mar 20, 2024
• Acceptance Date: Feb 5, 2024
• Online Publication Date: Mar 18, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jun 6, 2024
• Publicly Available Date: Jun 6, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 6-12
• DOI: https://doi.org/10.1109/ICIET60671.2024.10542714
• Keywords: Machine learning; Educational data mining; Educational data analysis; Data normalizing; Scaling techniques
• Public URL: https://rgu-repository.worktribe.com/output/2368201