Instructor Performance Analysis in Educational Contexts Based on Learner Evaluation Data: Integration of Clustering and Predictive Model - Lestari, Santi Dwi Desy; Margono, Hendro (Prodi Pendidikan Sejarah Fakultas Keguruan dan Ilmu Pendidikan Universitas Islam Sumatera, 2025)
This study aims to analyze instructor performance in educational contexts by classifying instructors based on learner evaluation data through the K-Means clustering algorithm and developing a predictive model to support effective and targeted instructor development programs. The data were derived from learners’ evaluations of instructors, covering aspects such as discipline and professionalism, mastery of subject matter, and pedagogical skills in delivering content. The results indicate that k=3 is the optimal cluster, producing three categories: Superior Instructor, Potential Instructor, and Developing Instructor. Furthermore, the predictive model demonstrates that the Naive Bayes algorithm outperforms XGBoost in performance prediction, achieving higher accuracy, recall, precision, and F1-scores. The integration of clustering and prediction proves effective in enabling faster, objective, and data-driven decisions for instructor development. These findings provide significant implications for educational institutions in establishing adaptive and sustainable systems of instructor evaluation and management.‎