A Case-Based Reasoning Approach to Automating the Construction of Multiple Choice Questions

  • David McSherry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6176)


Automating the construction of multiple-choice questions (MCQs) is a challenge that has attracted the interest of artificial intelligence researchers for many years. We present a case-based reasoning (CBR) approach to this problem in which MCQs are automatically generated from cases describing events or experiences of interest (e.g., historical events, movie releases, sports events) in a given domain. Measures of interestingness and similarity are used in our approach to guide the retrieval of cases and case features from which questions, distractors, and hints for the user are generated in natural language. We also highlight a potential problem that may occur when similarity is used to select distractors for the correct answer in certain types of MCQ. Finally, we demonstrate and evaluate our approach in an intelligent system for automating the design of MCQ quizzes called AutoMCQ.


case-based reasoning retrieval similarity multiple-choice questions natural language generation 


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  1. 1.
    Collins, J.: Education Techniques for Lifelong Learning: Writing Multiple-Choice Questions for Continuing Medical Education Activities and Self-Assessment Modules. RadioGraphics 26, 543–551 (2006)CrossRefGoogle Scholar
  2. 2.
    Harper, R.: Multiple-Choice Questions - A Reprieve. Bioscience Education E-journal, 2–6 (2003)Google Scholar
  3. 3.
    Moss, E.: Multiple Choice Questions: Their Value as an Assessment Tool. Current Opinion in Anaesthesiology 14, 661–666 (2001)CrossRefGoogle Scholar
  4. 4.
    Tarrant, M., Ware, J., Mohammed, A.M.: An Assessment of Functioning and Non-Functioning Distractors in Multiple-Choice Questions: a Descriptive Analysis. BMC Medical Education 9, 40 (2009)CrossRefGoogle Scholar
  5. 5.
    Díaz-Agudo, B., Pablo Gervás, P., Federico Peinado, F.: A Case Based Reasoning Approach to Story Plot Generation. In: González-Calero, P.A., Funk, P. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 142–156. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Fan, Y., Kendall, E.: A Case-Based Reasoning Approach for Speech Corpus Generation. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 993–1003. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Francisco, V., Hervás, R., Pablo Gervás, P.: Dependency Analysis and CBR to Bridge the Generation Gap in Template-Based NLG. In: Gelbukh, A. (ed.) CICLing 2007. LNCS, vol. 4394, pp. 432–443. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Carbonell, J.: AI in CAI: An Artificial Intelligence Approach to Computer-Assisted Instruction. IEEE Transactions on Man-Machine Systems MMS 11, 190–202 (1970)CrossRefGoogle Scholar
  9. 9.
    Mitkov, R., Ha, L.A., Karamanis, N.: A Computer-Aided Environment for Generating Multiple-Choice Test Items. Natural Language Engineering 12, 177–194 (2006)CrossRefGoogle Scholar
  10. 10.
    Brown, J.C., Frishkoff, G.A., Eskenazi, M.: Automatic Question Generation for Vocabulary Assessment. In: Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 819–826. Association for Computational Linguistics, Morristown (2005)CrossRefGoogle Scholar
  11. 11.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)MATHGoogle Scholar
  12. 12.
    Papasalouros, A., Kanaris, K., Kotis, K.: Automatic Generation of Multiple Choice Questions from Domain Ontologies. In: IADIS International Conference e-Learning, pp. 427–434. IADIS Press (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David McSherry
    • 1
  1. 1.School of Computing and Information EngineeringUniversity of UlsterColeraineNorthern Ireland

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