A Highly Adaptive Testing Design for PISA

The highly adaptive testing (HAT) design is introduced as an alternative test design for the Programme for International Student Assessment (PISA). The principle of HAT is to be as adaptive as possible when selecting items while accounting for PISA's nonstatistical constraints and addressing issues concerning PISA such as item position effects. HAT combines established methods from the field of computerized adaptive testing. It is implemented in R and code is provided. HAT was compared to the PISA 2018 multistage design (MST) in a simulation study based on a factorial design with the independent variables response probability (RP; .50, .62), item pool optimality (PISA 2018, optimal), and ability level (low, medium, high). PISA-specific conditions regarding sample size, missing responses, and nonstatistical constraints were implemented. HAT clearly outperformed MST regarding test information, RMSE, and constraint management across ability groups but it showed slightly weaker item exposure. Raising RP to .62 did not decrease test information much and is therefore a viable option to foster students' test-taking experience with HAT. Test information for HAT was up to three times higher than for MST when using a hypothetical optimal item pool. Summarizing, HAT proved to be a promising and applicable test design for PISA.

Automated summary

The highly adaptive testing (HAT) design is introduced as an alternative test design for PISA. HAT combines established methods from computerized adaptive testing and outperforms the PISA 2018 multistage design (MST) in terms of test information, RMSE, and constraint management. However, it shows slightly weaker item exposure. Increasing the response probability to .62 is a viable option to enhance students' test-taking experience with HAT.