EMPIRICAL EVALUATION OF COLLEGE PHYSICS INSTRUCTION BASED ON A MULTIDIMENSIONAL ITEM RESPONSE THEORY FRAMEWORK

As educational data mining (EDM) technology advances rapidly, educators can gain in-depth insights into and optimize the teaching process with advanced data analysis methods. This study combines Multidimensional Item Response Theory (MIRT) with educational data mining. It uses the MIRT package in R software to analyze college physics final exam data from three semesters, from the 2022—2023 spring semester to the 2023—2024 spring semester. The focus is on student academic performance in memory, conceptual understanding, and comprehensive application. By estimating students’ multidimensional abilities, it evaluates the teaching content and methods’ effectiveness. Results show a significant positive correlation between after class homework, classroom test scores and final exam performance across dimensions, particularly in comprehensive application ability, where homework design is key for improvement. Moreover, the study identifies underperforming classes in specific dimensions through analyzing anomalies in summative assessment data and suggests improvements. Based on cluster analysis, teaching classes are categorized into excellent, good, and needing-improvement groups, offering data-backed support for teaching improvement. Lastly, this study provides data-driven decision making support for teaching managers. It helps them identify teaching strengths and weaknesses, allocate resources better, and enhance teaching quality.