Conference paper Open Access
Simulating Policy Changes in Prerequisite-Free Curricula: A Supervised Data-Driven Approach
Frederik Baucks; Laurenz Wiskott
Editor(s)
Mitrovic, Antonija; Bosch, NigelCurriculum research is an important tool for understanding complex processes within a degree program. In particular, stochastic graphical models and simulations on related curriculum graphs have been used to make predictions about dropout rates, grades and degree completion time. There exists, however, little research on changes in the curriculum and the evaluation of their impact. The available evaluation methods of curriculum changes assume pre-existing strict curriculum graphs in the form of directed acyclic graphs. These allow for a straightforward model-oriented probabilistic or graph topological investigation of curricula. But the existence of such graphs cannot generally be assumed. We present a novel generalizing approach in which a curriculum graph is constructed based on data, using measurable student flow. By applying a discrete event simulation, we investigate the impact of policy changes on the curriculum and evaluate our approach on a sample data set from a German university. Our method is able to create a comparably effective and individually verifiable simulation without requiring a curriculum graph. It can thus be extended to prerequisite-free curricula, making it feasible to evaluate changes to flexible curricula.
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Indexed in
- Publication date:
- July 18, 2022
- DOI:
-
- ISBN:
- 978-1-7336736-3-1
- Published in:
- Proceedings of the 15th International Conference on Educational Data Mining, International Educational Data Mining Society, pp. 470--476 (978-1-7336736-3-1).
- Meeting:
- Proceedings of the 15th International Conference on Educational Data Mining, Durham, United Kingdom, July-2022
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- Cited by
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- License (for files):
- Creative Commons Attribution 4.0 International