Assessment and Evaluation of University Faculty Teaching Methods and Techniques for Ensuring Educational Quality

The purpose of this study is to present a data-driven framework for assessing and evaluating the teaching methods and techniques of university faculty members, with a focus on the role of such evaluations in ensuring educational quality. Unlike most previous studies that primarily relied on descriptive analyses or expert opinions, this research utilizes real data obtained from student evaluations of teaching and corresponding academic performance scores. The statistical population includes course data from Qom University and Bozorgmehr University of Qaenat over the period of 2016 to 2025. First, the evaluation criteria were extracted based on the official guidelines of the Ministry of Science, Research, and Technology. Then, using data mining algorithms—particularly clustering—courses were grouped based on similar evaluation patterns, and the criteria associated with clusters exhibiting the highest academic performance were identified. The relative weight and importance of each criterion were subsequently calculated and normalized in a data-oriented manner. The analysis revealed that the criterion “Reasonable and logical response to student suggestions, criticisms, and viewpoints” had the highest impact on student performance, with a weight of 0.07803. This was followed by “Mastery of the subject” (weight: 0.07774) and “Having a proper lesson plan with coherence and comprehensive content delivery” (weight: 0.07746). In the final phase, targeted strategies were proposed for the high-priority criteria and ranked using multi-criteria decision-making (MCDM) methods, including the Analytic Hierarchy Process (AHP). The findings indicate that the integrated approach of data mining and decision analysis offers a precise and effective tool for enhancing the academic evaluation system and improving teaching quality in higher education.