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Refined Distractor Generation with LSA and Stylometry for Automated Multiple Choice Question Generation
Josef Robert Moser1,3, Christian Gütl1,2, and Wei Liu3
1Institute for Information Systems and Computer Media, Graz University of Technology, Austria
josef.robert.moser@gmail.com
Christian.Guetl@iicm.tugraz.at
2Business School, Curtin University, Australia
3School of Computer Science and Software Engineering, The University of Western Australia, Australia
wei@csse.uwa.edu.au
Abstract. As lifelong learning becomes increasingly important in our society, mechanisms allowing students to evaluate their progress must be provided. A commonly used and widely accepted feedback mechanism is the multiple-choice test. Manual creation of multiple choice questions is often a time consuming process involving many iterations of trail and error. Using text processing and natural language processing techniques, automated multiple choice question generation, in recent years, is getting much closer to reality than ever. However, one of the most difficult tasks in both manual creation and automated generation of this kind of tests is the creation of distractors, because unsuitable distractors allow students to easily guess the correct answer, which counteracts the goal of these questions. In this paper, we investigated the desired properties of distractors and identified relevant text processing algorithms, specifically, latent semantic analysis and stylometry, for distractor selection. The refined distrators are compared with baseline distrators generated by our existing Automated Question Creator (AQC). Our preliminary evaluation shows that this novel combined approach produces distractors with a higher quality than those of the baseline AQC system.
Keywords: Automated Multiple-Choice Question Generation, Distractors, Text Processing, Latent Semantic Analysis, Stylometry
LNAI 7691, p. 95 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012