Instructor Performance Prediction Using Machine Learning
DOI : 10.46335/IJIES.2024.9.4.11
Authors : Mitali M. Shinde, Prof. Nilesh S. Vani
Abstract – This research explores the application of machine learning algorithms—Support Vector Machine (SVM), Random Forest, k-Nearest Neighbors (KNN), and Decision Tree—in the prediction of instructor performance within educational settings. Traditional methods of evaluating instructors often suffer from subjectivity, limited metrics, and an inability to comprehensively capture teaching effectiveness. This study aims to bridge this gap by leveraging advanced machine learning techniques to develop a robust predictive model. Through a comprehensive analysis of existing literature, the research identifies the inadequacies of current evaluation systems and proposes a novel approach that integrates diverse data sources and algorithmic models for accurate and unbiased instructor performance prediction. The findings of this study contribute to the refinement of educational assessment methodologies, fostering a more data-driven and objective means of gauging instructor effectiveness
