CLUSTERING TEACHER EXPECTATIONS IN DIGITAL EDUCATION PROGRAMS USING FUZZY C-MEANS ANALYSIS

Abstract

As digital tools become increasingly embedded in education, understanding teachers' expectations for digital pedagogy training is essential for designing effective professional development programs. This study applies Fuzzy C-Means clustering analysis to a dataset of 1,865 educators to uncover distinct expectation patterns in digital training. Using a 37-item questionnaire based on the DigCompEdu framework, we examined how teachers prioritize key digital competencies, ranging from technological integration and active learning strategies to ethical considerations and digital assessment methods. The instrument demonstrated high reliability (Cronbach’s α = 0.992), and clustering solutions were optimized using Bayesian Information Criterion (BIC) and Silhouette scores in JASP. Findings reveal multiple expectation clusters, reflecting differences in digital readiness, pedagogical adaptation, and confidence in technology use. Some teachers sought advanced training on digital instructional strategies, while others emphasized foundational skills and ethical concerns. These insights highlight the need for flexible, personalized training programs that align with teachers’ diverse professional development needs. By recognizing and addressing these varying expectations, this study contributes to the optimization of digital pedagogy training, ensuring that educators are not just equipped with digital tools but also empowered to use them effectively in diverse learning environments.