ROBIN SCHMUCKER

Curriculum analytics (CA) studies curriculum structure and student data to ensure the quality of educational programs. To gain statistical robustness, most existing CA techniques rely on the assumption of time-invariant course difficulty, preventing them from capturing variations that might occur over time. However, ensuring low temporal variation in course difficulty is crucial to warrant fairness in treating individual student cohorts and consistency in degree outcomes. We introduce item response theory (IRT) as a CA methodology that enables us to address the open problem of monitoring course difficulty variations over time. We use statistical criteria to quantify the degree to which course performance data meets IRT's theoretical assumptions and verify validity and reliability of IRT-based course difficulty estimates. Using data from 664 Computer Science and 1,355 Mechanical Engineering undergraduate students, we show how IRT can yield valuable CA insights: First, by revealing..

Primary location: Pittsburgh United States