Training data generation for context-dependent rubric-based short answer grading
Every four years, the PISA test is administered by the OECD to test the knowledge of teenage students worldwide and allow for comparisons of educational systems. However, having to avoid language differences and annotator bias makes the grading of student answers challenging. For these reasons, it would be interesting to consider methods of automatic student answer grading. To train some of these methods, which require machine learning, or to compute parameters or select hyperparameters for those that do not, a large amount of domain-specific data is needed. In this work, we explore a small number of methods for creating a large-scale training dataset using only a relatively small confide...
| Authors | Pavel Šindelář, Dávid Slivka, Christopher Bouma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 March 2026 |
| Last updated at source | 31 March 2026 |
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