webinar register page

Computational Psychometrics for Test Development: Combining Language and Psychometric Modeling
Along with the advances in communication and platform technology it became apparent that (quality) content development is at the core of many industries, including the education industry. The development of learning and assessment content has been a craft that has required a high level of expertise, often of the type that was built over the years on the job. In the fast-paced digital education this is difficult to sustain.

Moreover, in traditional assessments, a further complicating factor in the development and maintenance of the quality of the test is that items are pretested with human subjects, which is an expensive and time consuming activity.

This webinar presents an alternative approach to the development of an assessment based on a) creating a large item bank using language-model-based automatic item generation techniques, b) estimating their preliminary difficulties using natural language processing (NLP) models, c) piloting the items in the context of an adaptive test and a framework for updating item parameters. This framework involves a form of concurrent calibration that accounts for the fact that different people take different items. We have investigated several methodologies for this purpose that account for the selection bias inherent in adaptive testing. We also use a Bayesian approach for incorporating the uncertainty of the estimates of the item parameters.

In summary, this webinar illustrates how a psychometric framework combined with ML algorithms can support quality assessments for the 21st century. We will illustrate this approach with the Duolingo English Test.

Sep 16, 2021 11:00 AM in Eastern Time (US and Canada)

Webinar is over, you cannot register now. If you have any questions, please contact Webinar host: Psychometric Society.