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Mitigating Biases using an Additive Grade Point Model: Towards Trustworthy Curriculum Analytics Measures
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Datum
2023
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Gesellschaft für Informatik e.V.
Zusammenfassung
Curriculum Analytics (CA) tries to improve degree program quality and learning experience by studying curriculum structure and student data. In particular, descriptive data measures (e.g., correlation-based curriculum graphs) are essential to monitor whether the learning process proceeds as intended. Therefore, identifying confounders and resulting biases and mitigating them should be critical to ensure reliable and fair results. Still, CA approaches often use raw student data without considering the influence of possible confounders such as student performance, course difficulty, workload, and time, which can lead to biased results. In this paper, we use an additive grade model to estimate these confounders and verify the validity and reliability of the estimates. Further, we mitigate the estimated confounders and investigate their impact on the CA measures course-to-course correlation and order benefit. Using data from 574 Computer Science Bachelor students, we show that these measures are significantly confounded and mislead to biased interpretations.