Exploring the Role of Response Time in Item Response Theory: Rethinking the <scp>PISA</scp> 2022 Creative Thinking Assessment
Bibliographic record
Abstract
ABSTRACT This article explores the potential of response time‐item response theory (RT‐IRT) models to enhance creative thinking (CT) measurement in Programme for International Student Assessment (PISA) 2022, which employs traditional IRT models (2PL and generalized partial credit) excluding response time (RT). Given traditional IRT's limitations in capturing cognitive processes, we explored how numerous advanced IRT models, particularly those that incorporate RT as valuable information, have been developed for model testing and comparison for large‐scale assessment. In addition, we discuss the critical role of RT as demonstrated in the research literature, linking it to key aspects of creativity. We also explore the possibilities of connecting two types of RTs (i.e., the total time spent on task completion and the time from start to first action spent on each task) to patterns in creative performance across domains and stages, using RT‐IRT models. Benefits and types of RT‐IRT models (e.g., joint, diffusion, mixture) are further examined as they integrate RT to model speed–accuracy trade‐offs, detect aberrant response behaviors, and enhance result interpretability by reflecting engagement and creative processes. Lastly, we propose RT‐IRT models to leverage PISA 2022's RT data and provide process‐oriented insights, improve ability estimates, and potentially prevent misclassification of spontaneously creative responses as careless ones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.193 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".