A Case-Based Reasoning Approach to Automating the Construction of Multiple Choice Questions
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.
Keywordscase-based reasoning retrieval similarity multiple-choice questions natural language generation
Unable to display preview. Download preview PDF.
- 2.Harper, R.: Multiple-Choice Questions - A Reprieve. Bioscience Education E-journal, 2–6 (2003)Google Scholar
- 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
- 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
- 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