Profiling Teachers' Technology Acceptance and Digital Competence Using Machine Learning Techniques

Taybe Alabed; Sema Servi

doi:10.47000/tjmcs.1778991

Profiling Teachers' Technology Acceptance and Digital Competence Using Machine Learning Techniques

Abstract

Profiling educators based on their attitudes toward technology and digital competence provides valuable guidance for developing targeted professional development strategies. This study grouped mathematics teacher educators according to their levels of perceived usefulness, perceived ease of use, intention to use technology, and technological proficiency. An open-access dataset was analyzed using K-Means, K-Means++, and Hierarchical Clustering algorithms, resulting in three distinct participant profiles. K-Means was specifically employed to enhance the stability and convergence of initial centroids in the clustering process. These profiles were then used as target labels in supervised classification tasks using five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors (KNN), and Naive Bayes. Among these algorithms, the SVM model achieved the highest accuracy of 92%. To assess the performance of the classification models, additional evaluation metrics such as precision, recall, F1-score, AUC, and the Friedman test were employed. The Friedman test showed that SVM consistently outperformed other models, confirming its superior classification capability. The findings underline the value of data-driven approaches in educational technology research and contribute to the optimization of teacher education programs by providing insights into teacher profiling based on technology acceptance and digital competence.

Keywords

Supporting Institution

Selcuk Unniversity

Ethical Statement

This study was conducted using an open-access dataset that is publicly available. No new data were collected directly from human participants, and no personal or identifiable information was used in the analysis. Therefore, ethical approval was not required for this research. The authors declare that they have followed principles of research integrity and good scientific practice throughout the study.

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Details

Primary Language

English

Subjects

Machine Learning (Other)

Journal Section

Research Article

Authors

Taybe Alabed
0009-0009-3722-2911
Türkiye

Sema Servi ^*
0000-0003-2069-9085
Türkiye

Publication Date

February 23, 2026

Submission Date

September 6, 2025

Acceptance Date

October 29, 2025

Published in Issue

Year 2026 Volume: 18 Number: 1

DOI

https://doi.org/10.47000/tjmcs.1778991

IZ

https://izlik.org/JA82XH49BX

Cite

RIS / Bibtex

APA

Alabed, T., & Servi, S. (2026). Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques. Turkish Journal of Mathematics and Computer Science, 18(1), 126-142. https://doi.org/10.47000/tjmcs.1778991

AMA

1.Alabed T, Servi S. Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques. TJMCS. 2026;18(1):126-142. doi:10.47000/tjmcs.1778991

Chicago

Alabed, Taybe, and Sema Servi. 2026. “Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques”. Turkish Journal of Mathematics and Computer Science 18 (1): 126-42. https://doi.org/10.47000/tjmcs.1778991.

EndNote

Alabed T, Servi S (February 1, 2026) Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques. Turkish Journal of Mathematics and Computer Science 18 1 126–142.

IEEE

[1]T. Alabed and S. Servi, “Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques”, TJMCS, vol. 18, no. 1, pp. 126–142, Feb. 2026, doi: 10.47000/tjmcs.1778991.

ISNAD

Alabed, Taybe - Servi, Sema. “Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques”. Turkish Journal of Mathematics and Computer Science 18/1 (February 1, 2026): 126-142. https://doi.org/10.47000/tjmcs.1778991.

JAMA

1.Alabed T, Servi S. Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques. TJMCS. 2026;18:126–142.

MLA

Alabed, Taybe, and Sema Servi. “Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques”. Turkish Journal of Mathematics and Computer Science, vol. 18, no. 1, Feb. 2026, pp. 126-42, doi:10.47000/tjmcs.1778991.

Vancouver

1.Taybe Alabed, Sema Servi. Profiling Teachers’ Technology Acceptance and Digital Competence Using Machine Learning Techniques. TJMCS. 2026 Feb. 1;18(1):126-42. doi:10.47000/tjmcs.1778991