Computer-Aided Generation of Multiple-Choice Tests

By R. Mitkov, L. Ha. International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003, 2003

313 citations recorded by the source corpus.

Summary form only given. The paper describes a novel automatic procedure for the generation of multiple-choice tests from electronic documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the system makes use of language resources such as corpora and ontologies. The system operates in a fully automatic mode and also a semiautomatic environment where the user is offered the option to post-edit the generated test items. The results from the conducted evaluation suggest that the new procedure is very effective saving time and labour considerably and that the test items produced with the help of the program are not of inferior quality to those produced manually.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/956c1680fd3c600bd5358898571b927ea78e841d

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In Artificial Intelligence

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Citations in the corpus

Learning to Ask: Neural Question Generation for Reading Comprehension (2017, background): “For the most part, question generation has been tackled in the past via rule-based approaches ( e.g. , Mitkov and Ha (2003); Rus et al. (2010).”
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (2018, methodology): “Most early work in QG employed rule-based approaches to transform input text into questions, usually requiring the application of a sequence of well-designed general rules or templates (Mitkov and Ha, 2003; Labutov et al., 2015).”
Deep Questions without Deep Understanding (2015, methodology): “As such, the problem of automatic question generation has long been of interest to the online education community (Mitkov and Ha, 2003; Schwartz, 2004), both as a means of providing self-assessments directly to students and as a tool to help teachers with question authoring.”
Automatic Gap-fill Question Generation from Text Books (2011, background): “Since in current system any kind of post editing or manual work is avoided, comparison of efficiency in manual and automatic generation is not needed unlike Mitkov and Ha et al. (2003).”
Neural Models for Key Phrase Extraction and Question Generation (2018, methodology): “Various NLP techniques have been adopted in these systems to improve generation quality, including parsing (Heilman and Smith, 2010a; Mitkov and Ha, 2003), semantic role labeling (Lindberg et al., 2013), and the use of lexicographic resources like WordNet (Miller, 1995; Mitkov and Ha, 2003).”
Generating Natural Questions About an Image (2016, background): “Previous work on question generation from textual input has focused on two aspects: the gram-maticality (Wolfe, 1976; Mitkov and Ha, 2003; Heilman and Smith, 2010) and the content focus of question generation, i.e., “what to ask about”.”
Developing, Analyzing, and Using Distractors for Multiple-Choice Tests in Education: A Comprehensive Review (2017, background): “…to create distractors that are similar in content and structure relative to the correct option (Ascalon, Meyers, Davis, & Smits, 2007; Case & Swanson, 2001; Guttman, Schlesinger, & Schlesinger, 1967; Hoshino, 2013; Lai et al., 2016; Mitkov & Ha, 2003; Owens, Hanna, & Coppedge, 1970; Towns, 2014).”
Crowdsourcing Multiple Choice Science Questions (2017, methodology): “For example, if the correct answer a ∗ is heart , then a plausible distractor a (cid:48) like liver would share with a ∗ the hy-ponymy relation to organ in WordNet.”