Abdi, A., Shamsuddin, S.M., Hasan, S., Piran, J. (2019). Deep learning-based sentiment classification of evaluative text based on multi-feature fusion. Information Processing & Management, 56(4), 1245–1259.
Article
Google Scholar
Agarwal, M. (2012). Cloze and open cloze question generation systems and their evaluation guidelines. Master’s thesis. International Institute of Information Technology, (IIIT), Hyderabad, India.
Agarwal, M., & Mannem, P. (2011). Automatic gap-fill question generation from text books. In Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, Portland, (pp. 56–64).
Google Scholar
Aldabe, I., & Maritxalar, M. (2010). Automatic distractor generation for domain specific texts. In Proceedings of the 7th International Conference on Advances in Natural Language Processing. Springer-Verlag, Berlin, (pp. 27–38).
Chapter
Google Scholar
Alruwais, N., Wills, G., Wald, M. (2018). Advantages and challenges of using e-assessment. International Journal of Information and Education Technology, 8(1), 34–37.
Article
Google Scholar
Amidei, J., Piwek, P., Willis, A. (2018). Evaluation methodologies in automatic question generation 2013-2018. In Proceedings of The 11th International Natural Language Generation Conference. Association for Computational Linguistics, Tilburg University, (pp. 307–317).
Chapter
Google Scholar
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., Parikh, D. (2015). Vqa: Visual question answering. In Proceedings of the IEEE International Conference on Computer Vision, (pp. 2425–2433).
Bhatia, A.S., Kirti, M., Saha, S.K. (2013). Automatic generation of multiple choice questions using wikipedia. In Proceedings of the Pattern Recognition and Machine Intelligence. Springer-Verlag, Berlin, (pp. 733–738).
Chapter
Google Scholar
Bilker, W.B., Hansen, J.A., Brensinger, C.M., Richard, J., Gur, R.E., Gur, R.C. (2012). Development of abbreviated nine-item forms of the Raven’s standard progressive matrices test. Assessment, 19(3), 354–369.
Article
Google Scholar
Bin, L., Jun, L., Jian-Min, Y., Qiao-Ming, Z. (2008). Automated essay scoring using the KNN algorithm. In 2008 International Conference on Computer Science and Software Engineering, (Vol. 1. IEEE, Washington, DC, pp. 735–738).
Chapter
Google Scholar
Boyd, R.T. (1988). Improving your test-taking skills. Practical Assessment, Research & Evaluation, 1(2), 3.
Google Scholar
Brown, J.C., Frishkoff, G.A., Eskenazi, M. (2005). Automatic question generation for vocabulary assessment. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Vancouver, (pp. 819–826).
Google Scholar
Burrows, S., Gurevych, I., Stein, B. (2015). The eras and trends of automatic short answer grading. International Journal of Artificial Intelligence in Education, 25(1), 60–117.
Article
Google Scholar
Carmichael, M., Reid, A., Karpicke, J.D. (2018). Assessing the impact of educational video on student engagement, critical thinking and learning: The Current State of Play, (pp. 1–21): A SAGE Whitepaper, Sage Publishing.
Ch, D.R., & Saha, S.K. (2018). Automatic multiple choice question generation from text: A survey. IEEE Transactions on Learning Technologies, 13(1), 14–25. https://doi.org/10.1109/TLT.2018.2889100.
Article
Google Scholar
Chen, C.-Y., Liou, H.-C., Chang, J.S. (2006). Fast–an automatic generation system for grammar tests. In Proceedings of the COLING/ACL on Interactive Presentation Sessions. Association for Computational Linguistics, Sydney, (pp. 1–4).
Google Scholar
Chen, G., Yang, J., Hauff, C., Houben, G.-J. (2018). Learningq: A large-scale dataset for educational question generation. In Twelfth International AAAI Conference on Web and Social Media, (pp. 481–490).
Clay, B. (2001). A short guide to writing effective test questions. Lawrence: Kansas Curriculum Center, University of Kansas. https://www.k-state.edu/ksde/alp/resources/Handout-Module6.pdf.
Coniam, D. (1997). A preliminary inquiry into using corpus word frequency data in the automatic generation of English language cloze tests. Calico Journal, 14(2-4), 15–33.
Article
Google Scholar
Correia, R., Baptista, J., Eskenazi, M., Mamede, N. (2012). Automatic generation of cloze question stems. In Computational Processing of the Portuguese Language. Springer-Verlag, Berlin, (pp. 168–178).
Chapter
Google Scholar
Das, B., & Majumder, M. (2017). Factual open cloze question generation for assessment of learner’s knowledge. International Journal of Educational Technology in Higher Education, 14(1), 1–12.
Article
Google Scholar
Das, B., Majumder, M., Phadikar, S., Sekh, A.A. (2019). Automatic generation of fill-in-the-blank question with corpus-based distractors for e-assessment to enhance learning. Computer Applications in Engineering Education, 27(6), 1485–1495.
Article
Google Scholar
Deena, G., Raja, K., PK, N.B., Kannan, K. (2020). Developing the assessment questions automatically to determine the cognitive level of the E-learner using NLP techniques. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 11(2), 95–110.
Google Scholar
Dhokrat, A., Gite, H., Mahender, C.N. (2012). Assessment of answers: Online subjective examination. In Proceedings of the Workshop on Question Answering for Complex Domains, (pp. 47–56).
Divate, M., & Salgaonkar, A. (2017). Automatic question generation approaches and evaluation techniques. Current Science, 113(9), 1683–1691.
Article
Google Scholar
Du, X., Shao, J., Cardie, C. (2017). Learning to Ask: Neural Question Generation for Reading Comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, (pp. 1342–1352).
Chapter
Google Scholar
Flanagan, B., Yin, C., Hirokawa, S., Hashimoto, K., Tabata, Y. (2013). An automated method to generate e-learning quizzes from online language learner writing. International Journal of Distance Education Technologies (IJDET), 11(4), 63–80.
Article
Google Scholar
Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., Xu, W. (2015). Are you talking to a machine? Dataset and methods for multilingual image question. In Advances in Neural Information Processing Systems, (pp. 2296–2304).
Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., Farhadi, A. (2018). Iqa: Visual question answering in interactive environments. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (pp. 4089–4098).
Hasanah, U., Permanasari, A.E., Kusumawardani, S.S., Pribadi, F.S. (2016). A review of an information extraction technique approach for automatic short answer grading. In 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, Yogyakarta, (pp. 192–196).
Chapter
Google Scholar
Heaton, J.B. (1990). Classroom testing.
Hoshino, A., & Nakagawa, H. (2007). Assisting cloze test making with a web application. In Society for Information Technology & Teacher Education International Conference. Association for the Advancement of Computing in Education (AACE), Waynesville, NC USA, (pp. 2807–2814).
Islam, M.M., & Hoque, A.L. (2010). Automated essay scoring using generalized latent semantic analysis. In 2010 13th International Conference on Computer and Information Technology (ICCIT). IEEE, Dhaka, (pp. 358–363).
Chapter
Google Scholar
Jain, U., Zhang, Z., Schwing, A.G. (2017). Creativity: Generating diverse questions using variational autoencoders. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (pp. 6485–6494).
Jauhar, S.K., Turney, P., Hovy, E. (2015). TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions. https://www.microsoft.com/en-us/research/publication/tabmcq-a-dataset-of-general-knowledge-tables-and-multiple-choice-questions/.
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., Girshick, R. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (pp. 2901–2910).
Johnson, J., Karpathy, A., Fei-Fei, L. (2016). Densecap: Fully convolutional localization networks for dense captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (pp. 4565–4574).
Joshi, M., Choi, E., Weld, D.S., Zettlemoyer, L. (2017). TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, (pp. 1601–1611).
Chapter
Google Scholar
Kakkonen, T., Myller, N., Sutinen, E., Timonen, J. (2008). Comparison of dimension reduction methods for automated essay grading. Journal of Educational Technology & Society, 11(3), 275–288.
Google Scholar
Klein, T., & Nabi, M. (2019). Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds. ArXiv, abs/1911.02365.
Kočiskỳ, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K.M., Melis, G., Grefenstette, E. (2018). The narrativeqa reading comprehension challenge. Transactions of the Association for Computational Linguistics, 6, 317–328.
Article
Google Scholar
Kurdi, G., Leo, J., Parsia, B., Sattler, U., Al-Emari, S. (2020). A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 30(1), 121–204.
Article
Google Scholar
Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E. (2017). RACE: Large-scale ReAding Comprehension Dataset From Examinations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, (pp. 785–794).
Google Scholar
Le, N.-T., Kojiri, T., Pinkwart, N. (2014). Automatic question generation for educational applications–the state of art. In Advanced Computational Methods for Knowledge Engineering, (pp. 325–338).
Leacock, C., & Chodorow, M. (2003). C-rater: Automated scoring of short-answer questions. Computers and the Humanities, 37(4), 389–405.
Article
Google Scholar
Liang, C., Yang, X., Dave, N., Wham, D., Pursel, B., Giles, C.L. (2018). Distractor generation for multiple choice questions using learning to rank. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, (pp. 284–290).
Majumder, M., & Saha, S.K. (2014). Automatic selection of informative sentences: The sentences that can generate multiple choice questions. Knowledge Management and E-Learning: An International Journal, 6(4), 377–391.
Google Scholar
Majumder, M., & Saha, S.K. (2015). A system for generating multiple choice questions: With a novel approach for sentence selection. In Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications. Association for Computational Linguistics, Beijing, (pp. 64–72).
Chapter
Google Scholar
Miller, G.A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41.
Article
Google Scholar
Mitkov, R., LE An, H., Karamanis, N. (2006). A computer-aided environment for generating multiple-choice test items. Natural Language Engineering, 12(2), 177–194.
Article
Google Scholar
Mora, I.M., de la Puente, S.P., Nieto, X.G. (2016). Towards automatic generation of question answer pairs from images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, (pp. 1–2).
Mostafazadeh, N., Misra, I., Devlin, J., Mitchell, M., He, X., Vanderwende, L. (2016). Generating Natural Questions About an Image. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, (Volume 1: Long Papers), Berlin, Germany, (pp. 1802–1813).
Narendra, A., Agarwal, M., Shah, R. (2013). Automatic cloze-questions generation. In Proceedings of Recent Advances in Natural Language Processing. INCOMA Ltd. Shoumen, BULGARIA (ACL 2013), Hissar, (pp. 511–515).
Google Scholar
Nassif, A.B., Elnagar, A., Shahin, I., Henno, S. (2020). Deep learning for arabic subjective sentiment analysis: Challenges and research opportunities. Applied Soft Computing, 106836.
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., Rosenberg, M., Song, X., Stoica, A., Tiwary, S., Wang, T. (2016). MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. arXiv, arXiv:1611.09268. https://ui.adsabs.harvard.edu/abs/2016arXiv161109268B.
Nicol, D. (2007). E-assessment by design: Using multiple-choice tests to good effect. Journal of Further and higher Education, 31(1), 53–64.
Article
Google Scholar
Noorbehbahani, F., & Kardan, A.A. (2011). The automatic assessment of free text answers using a modified BLEU algorithm. Computers & Education, 56(2), 337–345.
Article
Google Scholar
Papasalouros, A., Kanaris, K., Kotis, K. (2008). Automatic generation of multiple choice questions from domain ontologies. In Proceedings of the e-Learning, (pp. 427–434).
Pino, J., & Eskenazi, M. (2009). Measuring hint level in open cloze questions. In Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference(FLAIRS). The AAAI Press, Florida, (pp. 460–465).
Google Scholar
Pino, J., Heilman, M., Eskenazi, M. (2008). A selection strategy to improve cloze question quality. In Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains, 9th International Conference on Intelligent Tutoring Systems. Springer, Montreal, (pp. 22–34).
Google Scholar
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Austin, (pp. 2383–2392).
Chapter
Google Scholar
Ramachandran, L., Cheng, J., Foltz, P. (2015). Identifying patterns for short answer scoring using graph-based lexico-semantic text matching. In Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, (pp. 97–106).
Ren, M., Kiros, R., Zemel, R. (2015). Exploring models and data for image question answering. In Advances in Neural Information Processing Systems, (pp. 2953–2961).
Roy, S., Narahari, Y., Deshmukh, O.D. (2015). A perspective on computer assisted assessment techniques for short free-text answers. In International Computer Assisted Assessment Conference. Springer, Zeist, (pp. 96–109).
Chapter
Google Scholar
Rozali, D.S., Hassan, M.F., Zamin, N. (2010). A survey on adaptive qualitative assessment and dynamic questions generation approaches. In 2010 International Symposium on Information Technology, (Vol. 3. IEEE, Kuala Lumpur, pp. 1479–1484).
Chapter
Google Scholar
Rus, V., Wyse, B., Piwek, P., Lintean, M., Stoyanchev, S., Moldovan, C. (2012). A detailed account of the first question generation shared task evaluation challenge. Dialogue & Discourse, 3(2), 177–204.
Article
Google Scholar
Sakaguchi, K., Heilman, M., Madnani, N. (2015). Effective feature integration for automated short answer scoring. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (pp. 1049–1054).
Santoro, A., Hill, F., Barrett, D., Morcos, A., Lillicrap, T. (2018). Measuring abstract reasoning in neural networks. In International Conference on Machine Learning, (pp. 4477–4486).
Serban, I.V., García-Durán, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., Bengio, Y. (2016). Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, (pp. 588–598).
Chapter
Google Scholar
Shaban, A.-M.S. (2014). A comparison between objective and subjective tests. Journal of the College of Languages, 30, 44–52.
Google Scholar
Shermis, M.D., & Burstein, J. (2013). Handbook of automated essay evaluation: Current applications and new directions.
Simoncelli, E.P., & Olshausen, B.A. (2001). Natural image statistics and neural representation. Annual Review of Neuroscience, 24(1), 1193–1216.
Article
Google Scholar
Suhr, A., Zhou, S., Zhang, A., Zhang, I., Bai, H., Artzi, Y. (2019). A Corpus for Reasoning about Natural Language Grounded in Photographs. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, (pp. 6418–6428).
Chapter
Google Scholar
Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., Suleman, K. (2017). NewsQA: A Machine Comprehension Dataset. In Proceedings of the 2nd Workshop on Representation Learning for NLP. Association for Computational Linguistics, Vancouver, (pp. 191–200).
Chapter
Google Scholar
Welbl, J., Liu, N.F., Gardner, M. (2017). Crowdsourcing multiple choice science questions. In Proceedings of the 3rd Workshop on Noisy User-generated Text, (pp. 94–106).
Yu, L., Park, E., Berg, A.C., Berg, T.L. (2015). Visual madlibs: Fill in the blank description generation and question answering. In Proceedings of the IEEE International Conference on Computer Vision, (pp. 2461–2469).
Zhang, S., Qu, L., You, S., Yang, Z., Zhang, J. (2017). Automatic generation of grounded visual questions. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. The AAAI Press, Melbourne, (pp. 4235–4243).
Google Scholar
Zhu, Y., Groth, O., Bernstein, M., Fei-Fei, L. (2016). Visual7w: Grounded question answering in images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (pp. 4995–5004).