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Automated Item Generation with Recurrent Neural Networks

Published online by Cambridge University Press:  01 January 2025

Matthias von Davier Open the ORCID record for Matthias von Davier [Opens in a new window]
Matthias von Davier*
Affiliation:
National Board of Medical Examiners
*
Correspondence should be made to Matthias von Davier, National Board of Medical Examiners, 3750 Market Street, Philadelphia, PA 19104-3102, USA. Email: mvondavier@nbme.org
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Abstract

Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven’s progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.

Keywords

deep learningneural networksautomatic item generationmachine learning

Information

Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 4 , December 2018 , pp. 847 - 857
Copyright
Copyright © 2018 The Psychometric Society

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