Location-Matching Adaptive Testing for Polytomous Technology-Enhanced Items

Abstract

The article presents adaptive testing strategies for polytomously scored technology-enhanced innovative items. We investigate item selection methods that match examinee’s ability levels in location and explore ways to leverage test-taking speeds during item selection. Existing approaches to selecting polytomous items are mostly based on information measures and tend to experience an item pool usage problem. In this study, we introduce location indices for polytomous items and show that location-matched item selection significantly improves the usage problem and achieves more diverse item sampling. We also contemplate matching items’ time intensities so that testing times can be regulated across the examinees. Numerical experiment from Monte Carlo simulation suggests that location-matched item selection achieves significantly better and more balanced item pool usage. Leveraging working speed in item selection distinctly reduced the average testing times as well as variation across the examinees. Both the procedures incurred marginal measurement cost (e.g., precision and efficiency) and yet showed significant improvement in the administrative outcomes. The experiment in two test settings also suggested that the procedures can lead to different administrative gains depending on the test design.