Alsubait, T., Parsia, B., Sattler, U. (2014). Generating multiple choice questions from ontologies: Lessons learnt. In OWLED (pp. 73–84). Citeseer. http://ceur-ws.org/Vol-1265/owled2014_submission_11.pdf
Google Scholar American Psychiatric Association . (2013). Diagnostic and statistical manual of mental disorders, 5th edition (DSM-5™). https://doi.org/10.1176/appi.books.9780890425596
Google Scholar Beel, J., Gipp, B., Langer, S., Breitinger, C. (2016). Paper recommender systems: A literature survey. International Journal on Digital Libraries, 17(4), 305–338.
Google Scholar | Crossref Bejar, I. I., Lawless, R., Morley, M. E., Wagner, M. E., Bennett, R. E., Revuelta, J. (2003). A feasibility study of on-the-fly item generation in adaptive testing. Journal of Technology, Learning, and Assessment, 2(3) 1–29.
Google Scholar Bigi, B., Brun, A., Haton, J. P., Smaïli, K., Zitouni, I. (2001, November). A comparative study of topic identification on newspaper and e-mail. In Proceedings Eighth Symposium on String Processing and Information Retrieval (pp. 238–241). IEEE. https://proceedings.neurips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf
Google Scholar | Crossref Briggs, D. C., Alonzo, A. C., Schwab, C., Wilson, M. (2006). Diagnostic assessment with ordered multiple-choice items. Educational Assessment, 11, 33–63. https://doi.org/10.1207/s15326977ea1101_2
Google Scholar de la Torre, J. (2009). A cognitive diagnosis model for cognitively based multiple-choice options. Applied Psychological Measurement, 33, 163–183. https://doi.org/10.1177/0146621608320523
Google Scholar Fremer, J., Anastasio, E. J. (1969). Computer assisted item writing (spelling items). Journal of Educational Measurement, 6, 69–74.
Google Scholar | Crossref | ISI Gierl, M. J., Bulut, O., Guo, Q., Zhang, X. (2017). Developing, analyzing, and using distractors for multiple-choice tests in education: A comprehensive review. Review of Educational Research, 87(6), 1082–1116.
Google Scholar | SAGE Journals | ISI Gierl, M. J., Lai, H. (2013a). Evaluating the quality of medical multiple-choice items created with automated processes. Medical Education, 47, 726–733.
Google Scholar | Crossref | Medline Gierl, M. J., Lai, H. (2013b). Using automated processes to generate test items. Educational Measurement: Issues and Practice, 32, 36–50.
Google Scholar | Crossref | ISI Gierl, M. J., Lai, H., Hogan, J. B., Matovinovic, D. (2015). A method for generating educational test items that are aligned to the common core state standards. Journal of Applied Testing Technology, 16(1), 1–18.
Google Scholar Gierl, M. J., Lai, H., Pugh, D., Touchie, C., Boulais, A-P., De Champlain, A. (2016). Evaluating the psychometric characteristics of generated multiple-choice test items. Applied Measurement in Education, 29, 196–210.
Google Scholar | Crossref Ha, L. A., Yaneva, V. (2018, June). Automatic distractor suggestion for multiple-choice tests using concept embeddings and information retrieval. In Proceedings of the thirteenth workshop on innovative use of NLP for building educational applications (pp. 389–398). https://doi.org/10.1145/3192714.3192819
Google Scholar | Crossref Haladyna, T. M., Rodriguez, M. C., Stevens, C. (2019). Are multiple-choice items too fat? Applied Measurement in Education, 32(4), 350–364.
Google Scholar | Crossref Hoffman, J. I. (2015). Biostatistics for medical and biomedical practitioners. Academic press.
Google Scholar Jurafsky, D., Martin, J. H. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (2nd ed.). Prentice-Hall.
Google Scholar Lai, H., Gierl, M. J., Touchie, C., Pugh, D., Boulais, A-P., De Champlain, A. (2016). Using automatic item generation to improve the quality of MCQ distractors. Teaching and Learning in Medicine, 28, 166–173.
Google Scholar | Crossref | Medline | ISI Leo, J., Kurdi, G., Matentzoglu, N., Parsia, B., Sattler, U., Forge, S., Donato, G., Dowling, W. (2019). Ontology-based generation of medical, multi-term MCQs. International Journal of Artificial Intelligence in Education, 29(2), 145–188 https://doi.org/10.1007/s40593-018-00172-w
Google Scholar Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J. (2013, December 5–10). Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International conference on neural information processing systems—Volume 2 (pp. 3111–3119). https://proceedings.neurips.cc/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf.
Google Scholar Mitkov, R., Ha, L. A. (2003). Computer-aided generation of multiple-choice tests. In Proceedings of the HLT-NAACL 03 workshop on building educational applications using natural language processing (Vol. 2 , pp. 17–22). Association for Computational Linguistics. https://aclanthology.org/W03-0203.pdf
Google Scholar | Crossref Mitkov, R., Ha, L. A., Karamanis, N. (2006). A computer-aided environment for generating multiple-choice test items. Natural Language Engineering, 12, 177–194.
Google Scholar | Crossref Pugh, D., De Champlain, A., Gierl, M., Lai, H., Touchie, C. (2020). Can automated item generation be used to develop high quality MCQs that assess application of knowledge? Research and Practice in Technology Enhanced Learning, 15, 12. https://doi.org/10.1186/s41039-020-00134-8
Google Scholar Richards, J. M. (1967). Can computers write college admissions tests? Journal of Applied Psychology, 51, 211–215.
Google Scholar | Crossref | Medline Roelleke, T. (2013). Information retrieval models. Foundations and relationships. Morgan & Claypool. https://www.morganclaypool.com/doi/abs/10.2200/S00494ED1V01Y201304ICR027
Google Scholar | Crossref von Davier, M. (2018). Automated item generation with recurrent neural networks. Psychometrika, 83, 847–857.
Google Scholar | Crossref | Medline von Davier, M. (2019). Training Optimus Prime, M.D.: Generating medical certification items by fine-tuning OpenAI’s gpt2 transformer model. arXiv:1908.08594.
Google Scholar