Supported by the Ministry of Science (NRW, GER) as part of the project KI:edu.nrw.
This project applies Curriculum Analytics (CA) to examine variation in course difficulty across student groups and over time. Using Item Response Theory (IRT) and additie grade point models, we quantify and adjust for confounding factors such as student achievement, workload, and temporal shifts. We introduce Differential Course Functioning (DCF) to identify disparities in how different student populations experience course difficulty, even when controlling for overall achievement. In addition, we examine time-varying patterns of difficulty and show that conventional metrics such as pass rates may be biased without such adjustments. Our analyses reveal structural and temporal biases in course design and assessment, with implications for fairness, advising, and curriculum development. This work supports the creation of more equitable and consistent learning experiences by informing data-driven interventions and institutional decision-making.
Publications
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*Best Paper Nominee* Gaining Insights into Course Difficulty Variations Using Item Response Theory