Incorporating Test‐Taking Engagement into Multistage Adaptive Testing Design for Large‐Scale Assessments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The use of multistage adaptive testing (MST) has gradually increased in large‐scale testing programs as MST achieves a balanced compromise between linear test design and item‐level adaptive testing. MST works on the premise that each examinee gives their best effort when attempting the items, and their responses truly reflect what they know or can do. However, research shows that large‐scale assessments may suffer from a lack of test‐taking engagement, especially if they are low stakes. Examinees with low test‐taking engagement are likely to show noneffortful responding (e.g., answering the items very rapidly without reading the item stem or response options). To alleviate the impact of noneffortful responses on the measurement accuracy of MST, test‐taking engagement can be operationalized as a latent trait based on response times and incorporated into the on‐the‐fly module assembly procedure. To demonstrate the proposed approach, a Monte‐Carlo simulation study was conducted based on item parameters from an international large‐scale assessment. The results indicated that the on‐the‐fly module assembly considering both ability and test‐taking engagement could minimize the impact of noneffortful responses, yielding more accurate ability estimates and classifications. Implications for practice and directions for future research were discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.350 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it