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

Year 2026, Volume: 18 Issue: 1, 126 - 142, 23.02.2026

Taybe Alabed , Sema Servi

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

https://izlik.org/JA82XH49BX

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

Teacher profiling , technology acceptance , digital competence , clustering , classification , TAM , TPACK

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.

Supporting Institution

Selcuk Unniversity

References

• Alabed, T., Servi, S., Multilayer analysis of nicotine-induced gene expression alterations in breast cancer cells using clustering and supervised learning methods, Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3)(2025), 1558–1573.
• Altman, N.S., An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 1992, 46(3), 175–185.
• Anwer, K.I., Servi, S., Clustering method based on artificial algae algorithm, International Journal of Intelligent Systems and Applications in Engineering, 9(4)(2021), 136–151.
• Antonietti, C., Cattaneo, A., Amenduni, F., Can teachers’ digital competence influence technology acceptance in vocational education?, Computers in Human Behavior, 132(2022), 107266.
• Arthur, D., Vassilvitskii, S., k-means++: The advantages of careful seeding, Stanford, 2006.
• Başer, B.Ö., Yangın, M., Sarıdaş, E.S., Makine öğrenmesi teknikleriyle diyabet hastalığının sınıflandırılması, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1)(2021), 112–120.
• Benavoli, A., Corani, G., Mangili, F., Should we really use post-hoc tests based on mean-ranks?, The Journal of Machine Learning Research, 17(1)(2016), 152–161.
• Bradley, A.P., The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7)(1997),1145–1159.
• Caruana, R., Niculescu-Mizil, A., An empirical comparison of supervised learning algorithms, In Proceedings of the 23rd International Conference on Machine Learning, 2006.
• Celik, I., Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education, Computers in Human Behavior, 138(2023), 107468.
• Celebi, M.E., Kingravi, H.A., Vela, P.A., A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Systems with Applications, 40(1)(2013), 200–210.
• Cortes, C., Vapnik, V., Support-vector networks. Machine Learning, 20(1995), 273–297.
• Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13(3)(1989), 319–340.
• Demˇsar, J., Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 7(Jan)(2006), 1–30.
• Dimitriadis, S., Liparas, D., How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer’s.
• Du, H., et al., Exploring the effects of AI literacy in teacher learning: An empirical study, Humanities and Social Sciences Communications, 11(1)(2024), 1–10.
• Faisal, M., et al., Information and communication technology competencies clustering for students for vocational high school students using K-Means clustering algorithm, International Journal of Engineering Science and Information Technology, 2(3)(2022), 111–120.
• Fawcett, T., An introduction to ROC analysis, Pattern Recognition Letters, 27(8)(2006), 861–874.
• Garcia, S., et al., Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences, 180(10)(2010), 2044–2064.
• Gulsoy, N., Kulluk, S., A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3)(2019), e1299.
• Hubert, L., Arabie, P., Comparing partitions, Journal of Classification, 2(1)(1985), 193–218.
• Hsu, C.-W., Chang, C.-C., Lin, C.-J., A Practical Guide to Support Vector Classification, Taipei, Taiwan, 2003.
• Islam, M.J., et al., Investigating the performance of naive-bayes classifiers and k-nearest neighbor classifiers, In 2007 International Conference on Convergence Information Technology (ICCIT 2007), IEEE, 2007.
• Jain, A.K., Data clustering: 50 years beyond K-means, Pattern Recognition Letters, 31(8)(2010), 651–666.
• Johnson, S.C., Hierarchical clustering schemes, Psychometrika, 32(3)(1967), 241–254.
• Mitchell, T.M., Mitchell, T.M., Machine Learning, Vol. 1. McGraw-Hill New York, 1997.
• Mishra, P., Koehler, M.J., Technological pedagogical content knowledge: A framework for teacher knowledge, Teachers College Record, 108(6)(2006), 1017–1054.
• Mukuka, A., Data on mathematics teacher educators’ proficiency and willingness to use technology: A structural equation modelling analysis, Data in Brief, 54(2024), 110307.
• Pedregosa, F. et al., Scikit-learn: Machine learning in Python, The Journal of Machine Learning Research, 12(2011), 2825–2830.
• Ray, S., A quick review of machine learning algorithms, In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), IEEE, 2019.
• Romero, C., Ventura, S., Educational data mining and learning analytics: An updated survey, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3)(2020), e1355.
• Sangodiah, A. et al., Machine learning clustering analysis towards educator’s readiness to adopt augmented reality as a teaching tool, MENDEL, 2023.
• Schölkopf, B., Smola, A.J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2002.
• Singh, A., Thakur, N., Sharma, A., A review of supervised machine learning algorithms, In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), IEEE, 2016.
• Sofwan, M., Habibi, A., Yaakob, M.F.M., TPACK’s role in predicting technology integration during teaching practicum: Structural equation modeling, Education Sciences, 13(5)(2023), 448.
• Van Deursen, A.J.A.M., Van Dijk, J.A.G.M., Internet skills and the digital divide, New Media & Society, 13(6)(2011), 893–911.
• Venkatesh, V., et al., User acceptance of information technology: Toward a unified view, MIS Quarterly, 27(3)(2003), 425–478.
• Vinh, N.X., Epps, J., Bailey, J., Information theoretic measures for clusterings comparison: Is a correction for chance necessary?, in Proceedings of the 26th Annual International Conference on Machine Learning, 2009.
• Ward Jr, J.H., Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, 58(301)(1963), 236–244.
• Wong, Z.-J., et al., Cluster Boost: Stacked machine learning model for customer behaviour analysis in e-commerce, in 2024 IEEE 12th Conference on Systems, Process & Control (ICSPC), IEEE, 2024.
• Zhang, C., et al., Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis, International Journal of Educational Technology in Higher Education, 20(1)(2023), 49.
• Zhang, C., et al., Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural model analysis, Education and Information Technologies, 2025.