A Computational Approach to Grammatical Coding of English Words
Journal of the ACM (JACM)
Computer-aided generation of multiple-choice tests
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
Automatic question generation for vocabulary assessment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
FAST: an automatic generation system for grammar tests
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
ACM Transactions on Information Systems (TOIS)
Semantic similarity of distractors in multiple-choice tests: extrinsic evaluation
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Automatic distractor generation for domain specific texts
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
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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.