Applying Unsupervised Possibilistic Fuzzy C-Means to Cluster Teachers' Online Practices - Guldana A. Begimbetova; Heri Retnawati; Bruri Moch. Triyono; Haryanto Haryanto; Gulzhaina K. Kassymova; Slamet Suyanto (Yayasan Keluarga Guru Mandiri, 2025)
This research examines the digital literacy skills of teachers in Finnish education using a dataset of 4,988 teachers from 2017 to 2019. The dataset includes variables such as gender, age, level of urbanization, self-efficacy, professional development, digital engagement, and information skills. Teachers were categorized based on their digital competence. The study uses Unsupervised C-Mediums (UPFCM) clustering to analyze teachers' online experiences and identify distinct groups. Descriptive statistics and correlation analyses reveal relationships between key variables, suggesting that self-efficacy, teaching relevance, and digital engagement significantly influence teachers' digital literacy. The results indicate different digital competence profiles, providing insights into professional development needs. The study highlights the importance of personalized learning programs to enhance digital competence and bridge skill gaps. Using an advanced clustering approach, this study contributes to understanding teachers' digital skills. Provides a foundation for data-driven educational advancements aimed at increasing digital literacy in the teaching profession.