An Explainable Clustering Methodology for the Categorization of Teachers in Digital Learning Platforms Based on Their Performance

Categorizing teachers using artificial intelligence (AI) on digital platforms opens up new ways to optimize teacher performance. By applying unsupervised clustering techniques together with supervised methods such as Random Forest and decision trees, significant patterns in teacher behavior and performance can be identified. These patterns are crucial for designing personalized educational interventions and improving the allocation of training resources, which significantly increases the effectiveness of teacher training programs. The results of these analyses highlight the potential of AI to provide valuable insights into teacher effectiveness, thus promoting more adaptive and equitable education. This methodological approach makes it possible to develop a deeper understanding of educational needs and to adjust pedagogical strategies effectively. The implementation of these technologies not only enriches the educational experience for teachers but also establishes a robust foundation for the expansion of AI use in a variety of educational settings. Furthermore, the study underlines the importance of maintaining a balance between technological innovation and fundamental pedagogical needs. Ensuring that the adoption of advanced technologies in education is beneficially inclusive and accessible to all stakeholders is essential to fostering a positive and lasting impact on the educational field.