 New Indices for Refining Multiple Choice QuestionsMariano Amo-Salas, María del Mar Arroyo-Jimenez, David Bustos-Escribano, Eva Fairén-Jiménez and Jesús López-Fidalgo Journal of Probability and Statistics, 2014, vol. 2014, 1-8 Abstract: Multiple choice questions (MCQs) are one of the most popular tools to evaluate learning and knowledge in higher education. Nowadays, there are a few indices to measure reliability and validity of these questions, for instance, to check the difficulty of a particular question (item) or the ability to discriminate from less to more knowledge. In this work two new indices have been constructed: (i) the no answer index measures the relationship between the number of errors and the number of no answers; (ii) the homogeneity index measures homogeneity of the wrong responses (distractors). The indices are based on the lack-of-fit statistic, whose distribution is approximated by a chi-square distribution for a large number of errors. An algorithm combining several traditional and new indices has been developed to refine continuously a database of MCQs. The final objective of this work is the classification of MCQs from a large database of items in order to produce an automated-supervised system of generating tests with specific characteristics, such as more or less difficulty or capacity of discriminating knowledge of the topic. Date: 2014 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljps:240263 DOI: 10.1155/2014/240263 Access Statistics for this article More articles in Journal of Probability and Statistics from Hindawi |
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