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

Source 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.