Our submission for the BEA 2024 Shared Task on Predicting Item Difficulty and Item Response Time.
If you use this code for scientific purposes, please cite:
Gombert, S., Menzel, L., Di Mitri, D., & Drachsler, H. (2024, June). Predicting Item Difficulty and Item Response Time with Scalar-mixed Transformer Encoder Models and Rational Network Regression Heads. Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024). Mexico City, Mexico: Association for Computational Linguistics.