Instructor Effectiveness Evaluation for Industry Training Using Rough Set Theory
Abstract
Instructor effectiveness plays a vital role in the success of industry training, directly influencing employee skill development and organizational performance. Accurate evaluation of instructors ensures better learning outcomes and a more effective return on investment in training. However, existing evaluation methods often rely on subjective feedback, statistical averages, or weighted scoring models, which struggle with redundancy, vagueness, and uncertainty in data. To address these limitations, this study proposes a Rough Set-Based Decision Rule Extraction (RSBDRE) framework for evaluating instructor effectiveness. The framework identifies the most influential attributes, eliminates redundancies, and generates interpretable IF-THEN rules for decision-making. This approach is applied to industry training scenarios, enabling managers to classify instructor effectiveness with higher accuracy and transparency. Findings indicate that communication clarity, engagement, and post-training performance are strong predictors of effectiveness, offering actionable insights for instructor development and training quality improvement.