Abstract
This paper presents a novel Automatic Question Generation (AQG) approach that generates trigger questions as a form of support for students’ learning through writing. The approach first automatically extracts citations from students’ compositions together with key content elements. Next, the citations are classified using a rule-based approach and questions are generated based on a set of templates and the content elements. A pilot study using the Bystander Turing Test investigated differences in writers’ perception between questions generated by our AQG system and humans (Human Tutor, Lecturer, or Generic Question). It is found that the human evaluators have moderate difficulties distinguishing questions generated by the proposed system from those produced by human (F-score=0.43). Moreover, further results show that our system significantly outscores Generic Question on overall quality measures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Graesser, A.C., Person, N.K.: Question asking during tutoring. American Educational Research Journal 31, 104–137 (1994)
Reynolds, T., Bonk, C.: Computerized prompting partners and keystroke recording devices: Two macro driven writing tools. Educational Technology Research and Development 44(3), 83–97 (1996)
Wolfe, J.H.: Automatic question generation from text - an aid to independent study. SIGCUE Outlook 10(SI), 104–112 (1976)
Heilman, M., Noah, S.A.: Question generation via overgenerating transformations and ranking. In: The 14th Annual Conference on Artificial Intelligence in Education Workshop on Question Generation (2009)
Wang, W.M., Hao, T.Y., Liu, W.Y.: Automatic question generation for learning evaluation in medicine. In: Leung, H., Li, F., Lau, R., Li, Q. (eds.) ICWL 2007. LNCS, vol. 4823, pp. 242–251. Springer, Heidelberg (2008)
Britt, M.A., Wiemer-Hastings, P., Larson, A.A., Perfetti, C.A.: Using intelligent feedback to improve sourcing and integration in students’ essays. Int. J. Artif. Intell. Ed. 14(3,4), 359–374 (2004)
Villalon, J., Kearney, P., Calvo, R.A., Reimann, P.: Glosser: Enhanced feedback for student writing tasks. In: Proc. Eighth IEEE International Conference on Advanced Learning Technologies ICALT 2008, July 1-5, pp. 454–458 (2008)
Mitkov, R., Ha, L.A.: Computer-aided generation of multiple-choice tests. In: Proceedings of the HLT-NAACL 2003 workshop on Building educational applications using natural language processing, Association for Computational Linguistics, Morristown, NJ, USA, pp. 17–22 (2003)
Rus, V., Cai, Z.Q., Graesser, A.C.: Experiments on generating questions about facts. In: Gelbukh, A. (ed.) CICLing 2007. LNCS, vol. 4394, pp. 444–455. Springer, Heidelberg (2007)
Kunichika, H., Katayama, T., Hirashima, T., Takeuchi, A.: Automated question generation methods for intelligent english learning systems and its evaluation. In: Proc. of ICCE 2001 (2001)
Powley, B., Dale, R.: Evidence-based information extraction for high-accuracy citation extraction and author name recognition. In: Proceedings of the 8th RIAO International Conference, Pittsburgh, PA (2007)
Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: CoNLL 2009 (2009)
Lehnert, W., Cardie, C., Riloff, E.: Analyzing research papers using citation sentences. In: Proceedings of the Twelfth Annual Conference of the Cognitive Science Society, pp. 511–518 (1990)
University of Glasgow: Writing an assignment: reporting verbs. Technical report, University of Glasgow (2009)
Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 2006), pp. 417–422 (2006)
Hyland, K.: Academic attribution: citation and the construction of disciplinary knowledge. Applied Linguistics 20, 341–367 (1994)
Person, N.K., Graesser, A.C.: Human or computer? autotutor in a bystander turing test. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 821–830. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, M., Calvo, R.A., Rus, V. (2010). Automatic Question Generation for Literature Review Writing Support. In: Aleven, V., Kay, J., Mostow, J. (eds) Intelligent Tutoring Systems. ITS 2010. Lecture Notes in Computer Science, vol 6094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13388-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-13388-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13387-9
Online ISBN: 978-3-642-13388-6
eBook Packages: Computer ScienceComputer Science (R0)