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2019 | OriginalPaper | Buchkapitel

Towards an Automatic Q&A Generation for Online Courses - A Pipeline Based Approach

verfasst von: Sylvio Rüdian, Niels Pinkwart

Erschienen in: Artificial Intelligence in Education

Verlag: Springer International Publishing

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Abstract

Personalization of online courses is one of the challenges of the 21st century. Although different methodologies for personalization in educational contexts are already existing, there is a bottleneck: personalization by context is always limited to existing learning material; creation of those is a time-consuming task. In this paper we introduce a pipeline to generate questions and valid answers based on educational texts, limited to factual questions for given sentences. We combined NLP technologies with an efficient methodology that is normally used in bioinformatics and adjusted it to generate Q&A-pairs. Instructors can suggest corrections in natural language. Our system generates questions and corresponding answers based on sentences of which 70% make sense.
Literatur
1.
Zurück zum Zitat National Academy of Sciences: Advance personalized learning NAE Grand Challenges for Engineering, Updated 2017, pp. 45–47 (2008) National Academy of Sciences: Advance personalized learning NAE Grand Challenges for Engineering, Updated 2017, pp. 45–47 (2008)
2.
Zurück zum Zitat Bloom, B.: Bloom’s Taxonomy of Educational Objectives, Vol. 1: Cognitive Domain, New York, McKay (1965) Bloom, B.: Bloom’s Taxonomy of Educational Objectives, Vol. 1: Cognitive Domain, New York, McKay (1965)
3.
Zurück zum Zitat Heilman, M., Smith, N.A.: Question generation via overgenerating transformations and ranking. Pittsburgh, Language Technologies Institute (2009) Heilman, M., Smith, N.A.: Question generation via overgenerating transformations and ranking. Pittsburgh, Language Technologies Institute (2009)
4.
Zurück zum Zitat Heilman, M., Smith, N.A.: Good question! Statistical ranking for question generation. In: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, HLT 2010, pp. 609–617 (2010) Heilman, M., Smith, N.A.: Good question! Statistical ranking for question generation. In: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, HLT 2010, pp. 609–617 (2010)
7.
Zurück zum Zitat Zhao, S., Wang, H., Li, C., Liu, T., Guan, Y.: Automatically generating questions from queries for community-based question answering. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, Chiang Mai, Thailand, AFNLP, pp. 929–937 (2011) Zhao, S., Wang, H., Li, C., Liu, T., Guan, Y.: Automatically generating questions from queries for community-based question answering. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, Chiang Mai, Thailand, AFNLP, pp. 929–937 (2011)
8.
Zurück zum Zitat Rodrigues, H.P., Coheur, L., Nyberg, E.: QGASP: a framework for question generation based on different levels of linguistic information. In: Proceedings of the 9th International Natural Language Generation conference, Edinburgh, UK, pp. 242–243. Association for Computational Linguistic (2016) Rodrigues, H.P., Coheur, L., Nyberg, E.: QGASP: a framework for question generation based on different levels of linguistic information. In: Proceedings of the 9th International Natural Language Generation conference, Edinburgh, UK, pp. 242–243. Association for Computational Linguistic (2016)
9.
Zurück zum Zitat Rajpurkar, P., Jian Zhang, K.L., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: Conference on Empirical Methods in Natural Language Processing (2016) Rajpurkar, P., Jian Zhang, K.L., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: Conference on Empirical Methods in Natural Language Processing (2016)
10.
Zurück zum Zitat Altschul, S.F., Erickson, B.W.: Optimal sequence alignment using affine gap costs. Bull. Math. Biol. 48, 603–616 (1986) MathSciNetCrossRef Altschul, S.F., Erickson, B.W.: Optimal sequence alignment using affine gap costs. Bull. Math. Biol. 48, 603–616 (1986) MathSciNetCrossRef
11.
Zurück zum Zitat LM Digital Media: General Knowledge Quiz for kids to Intrigue their Senses, 11 October 2017. kidsworldfun.​com. Accessed 01 Feb 2018 LM Digital Media: General Knowledge Quiz for kids to Intrigue their Senses, 11 October 2017. kidsworldfun.​com. Accessed 01 Feb 2018
12.
Zurück zum Zitat Fellbaum, C.: WordNet. In: Chapelle, C. (ed.) The Encyclopedia of Applied Linguistics. Blackwell Publishing Ltd., Chichester (2012) Fellbaum, C.: WordNet. In: Chapelle, C. (ed.) The Encyclopedia of Applied Linguistics. Blackwell Publishing Ltd., Chichester (2012)
13.
Zurück zum Zitat Karamanis, N., Ha, L.A., Mitkov, R.: Generating multiple-choice test items from medical text: a pilot study. In: Proceedings of the Fourth International Natural Language Generation Conference, Sydney, pp. 111–113. Association for Computational Linguistics (2006) Karamanis, N., Ha, L.A., Mitkov, R.: Generating multiple-choice test items from medical text: a pilot study. In: Proceedings of the Fourth International Natural Language Generation Conference, Sydney, pp. 111–113. Association for Computational Linguistics (2006)
Metadaten
Titel
Towards an Automatic Q&A Generation for Online Courses - A Pipeline Based Approach
verfasst von
Sylvio Rüdian
Niels Pinkwart
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-23207-8_44

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