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Recent years witnessed an increase in the amount of research on the task of Question Difficulty Estimation from Text QDET with Natural Language Processing (NLP) techniques, with the goal of targeting the limitations of traditional approaches to question calibration.
Dale, E., Chall, J.S.: A formula for predicting readability: Instructions. Educational research bulletin pp. 37–54 (1948)
1948
Earlier work this paper cites.
Flesch, R.: A new readability yardstick. Journal of applied psychology 32
1948
Earlier work this paper cites.
Gunning, R., et al.: Technique of clear writing (1952)
1952
Earlier work this paper cites.
Coleman, E.B.: On understanding prose: some determiners of its complexity. NSF Final Report GB-2604. Washington, DC: National Science Foundation (1965)
1965
Earlier work this paper cites.
Senter, R., Smith, E.A.: Automated readability index. Tech. rep., Cincinnati Univ OH (1967)
1967
Earlier work this paper cites.
Klare, G.R.: Assessing readability. Reading research quarterly pp. 62–102 (1974)
1974
Earlier work this paper cites.
Kincaid, J.P., Fishburne Jr, R.P., Rogers, R.L., Chissom, B.S.: Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. Tech. rep., Naval Technical Training Command Millington TN Research Branch (1975)
1975
Earlier work this paper cites.
Manning, C.D.: Introduction to information retrieval. Syngress Publishing, (2008)
2008
Earlier work this paper cites.
Feng, M., Heffernan, N., Koedinger, K.: Addressing the assessment challenge with an online system that tutors as it assesses. User Modeling and User-Adapted Interaction 19
2009
Earlier work this paper cites.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26
2013
Earlier work this paper cites.
Beinborn, L., Zesch, T., Gurevych, I.: Candidate evaluation strategies for improved difficulty prediction of language tests. In: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications. pp. 1–11 (2015)
2015
Earlier work this paper cites.
Culligan, B.: A comparison of three test formats to assess word difficulty. Language Testing 32
2015
Earlier work this paper cites.
El Masri, Y.H., Ferrara, S., Foltz, P.W., Baird, J.A.: Predicting item difficulty of science national curriculum tests: the case of key stage 2 assessments. The Curriculum Journal 28
2017
Earlier work this paper cites.
Huang, Z., Liu, Q., Chen, E., Zhao, H., Gao, M., Wei, S., Su, Y., Hu, G.: Question difficulty prediction for reading problems in standard tests. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
2017
Earlier work this paper cites.
Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E.: Race: Large-scale reading comprehension dataset from examinations. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 785–794 (2017)
2017
Earlier work this paper cites.
Trace, J., Brown, J.D., Janssen, G., Kozhevnikova, L.: Determining cloze item difficulty from item and passage characteristics across different learner backgrounds. Language Testing 34
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: NIPS (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Ehara, Y.: Building an english vocabulary knowledge dataset of japanese english-as-a-second-language learners using crowdsourcing. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (2018)
2018
Cited alongside, same era.
Pandarova, I., Schmidt, T., Hartig, J., Boubekki, A., Jones, R.D., Brefeld, U.: Predicting the difficulty of exercise items for dynamic difficulty adaptation in adaptive language tutoring. International Journal of Artificial Intelligence in Education 29
2019
Later among the works it cites.
Qiu, Z., Wu, X., Fan, W.: Question difficulty prediction for multiple choice problems in medical exams. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. pp. 139–148 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Yaneva, V., Baldwin, P., Mee, J., et al.: Predicting the difficulty of multiple choice questions in a high-stakes medical exam. In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. pp. 11–20 (2019)
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Hsu, F.Y., Lee, H.M., Chang, T.H., Sung, Y.T.: Automated estimation of item difficulty for multiple-choice tests: An application of word embedding techniques. Information Processing & Management 54
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Yang, H., Suyong, E.: Feature analysis on english word difficulty by gaussian mixture model. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC). pp. 191–194. IEEE (2018)
2018
Cited alongside, same era.
Cheng, S., Liu, Q., Chen, E., Huang, Z., Huang, Z., Chen, Y., Ma, H., Hu, G.: Dirt: Deep learning enhanced item response theory for cognitive diagnosis. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. pp. 2397–2400 (2019)
2019
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186 (2019)
2019
Cited alongside, same era.
Fang, J., Zhao, W., Jia, D.: Exercise difficulty prediction in online education systems. In: 2019 International Conference on Data Mining Workshops (ICDMW). pp. 311–317. IEEE (2019)
2019
Cited alongside, same era.
Hou, J., Maximilian, K., Quecedo, J.M.H., Stoyanova, N., Yangarber, R.: Modeling language learning using specialized elo rating. In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. pp. 494–506 (2019)
2019
Cited alongside, same era.
Lee, J.U., Schwan, E., Meyer, C.M.: Manipulating the difficulty of c-tests. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 360–370 (2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Benedetto, L., Cappelli, A., Turrin, R., Cremonesi, P.: Introducing a framework to assess newly created questions with natural language processing. In: International Conference on Artificial Intelligence in Education. pp. 43–54. Springer (2020)
2020
Later among the works it cites.
Benedetto, L., Cappelli, A., Turrin, R., Cremonesi, P.: R2de: a nlp approach to estimating irt parameters of newly generated questions. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. pp. 412–421 (2020)
2020
Later among the works it cites.
Settles, B., T. LaFlair, G., Hagiwara, M.: Machine learning–driven language assessment. Transactions of the Association for computational Linguistics 8
2020
Later among the works it cites.
Tong, H., Zhou, Y., Wang, Z.: Exercise hierarchical feature enhanced knowledge tracing. In: International Conference on Artificial Intelligence in Education. pp. 324–328. Springer (2020)
2020
Later among the works it cites.
Yaneva, V., Baldwin, P., Mee, J., et al.: Predicting item survival for multiple choice questions in a high-stakes medical exam. In: Proceedings of The 12th Language Resources and Evaluation Conference. pp. 6812–6818 (2020)
2020
Later among the works it cites.
Zhou, Y., Tao, C.: Multi-task bert for problem difficulty prediction. In: 2020 International Conference on Communications, Information System and Computer Engineering (CISCE). pp. 213–216. IEEE (2020)
2020
Later among the works it cites.
AlKhuzaey, S., Grasso, F., Payne, T.R., Tamma, V.: A systematic review of data-driven approaches to item difficulty prediction. In: International Conference on Artificial Intelligence in Education. pp. 29–41. Springer (2021)
2021
Later among the works it cites.
Benedetto, L., Aradelli, G., Cremonesi, P., Cappelli, A., Giussani, A., Turrin, R.: On the application of transformers for estimating the difficulty of multiple-choice questions from text. In: Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications. pp. 147–157 (2021)
2021
Later among the works it cites.
Bi, S., Cheng, X., Li, Y.F., Qu, L., Shen, S., Qi, G., Pan, L., Jiang, Y.: Simple or complex? complexity-controllable question generation with soft templates and deep mixture of experts model. In: Findings of the Association for Computational Linguistics: EMNLP 2021. pp. 4645–4654 (2021)
2021
Later among the works it cites.
Benedetto, L., Cremonesi, P., Caines, A., Buttery, P., Cappelli, A., Giussani, A., Turrin, R.: A survey on recent approaches to question difficulty estimation from text. ACM Computing Surveys (CSUR) (2022)
2022
Later among the works it cites.