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Springer Nature Switzerland
Reference28 articles.
1. Azevedo, J.M., Oliveira, E.P., Beites, P.D.: Using learning analytics to evaluate the quality of multiple-choice questions: A perspective with classical test theory and item response theory. Int. J. Inf. Learn. Technol. 36(4), 322–341 (2019)
2. Bhowmick, A.K., Jagmohan, A., Vempaty, A., Dey, P., Hall, L., Hartman, J., Kokku, R., Maheshwari, H.: Automating Question Generation From Educational Text. In: Artificial Intelligence XL. pp. 437–450 Springer Nature Switzerland, Cham (2023)
3. Bitew, S.K., Deleu, J., Develder, C., Demeester, T.: Distractor generation for multiple-choice questions with predictive prompting and large language models. In: RKDE2023, the 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education Side event at ECML-PKDD (2023)
4. Bulathwela, S., Muse, H., Yilmaz, E.: Scalable Educational Question Generation with Pre-trained Language Models. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., and Dimitrova, V. (eds.) Artificial Intelligence in Education. pp. 327–339 Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-36272-9_27
5. Costello, E., Holland, J.C., Kirwan, C.: Evaluation of MCQs from MOOCs for common item writing flaws. BMC Res. (2018). https://doi.org/10.1186/s13104-018-3959-4