Using linguistic features to predict the response process complexity associated with answering clinical MCQs
MetadataShow full item record
AbstractThis study examines the relationship between the linguistic characteristics of a test item and the complexity of the response process required to answer it correctly. Using data from a large-scale medical licensing exam, clustering methods identified items that were similar with respect to their relative difficulty and relative response-time intensiveness to create low response process complexity and high response process complexity item classes. Interpretable models were used to investigate the linguistic features that best differentiated between these classes from a descriptive and predictive framework. Results suggest that nuanced features such as the number of ambiguous medical terms help explain response process complexity beyond superficial item characteristics such as word count. Yet, although linguistic features carry signal relevant to response process complexity, the classification of individual items remains challenging.
CitationYaneva, V., Jurich, D., Ha, L.A. and Baldwin, P. (2021) Using linguistic features to predict the response process complexity associated with answering clinical MCQs. Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 223–232 April 20, 2021.
Description© 2021 The Authors. Published by Association for Computational Linguistics . This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://aclanthology.org/2021.bea-1.23
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/