The Value of Individual Screen Response Time in Predicting Student Test Performance: Evidence from TIMSS 2019 Problem Solving and Inquiry Tasks
Bibliographic record
Abstract
The time students spend on answering a test item (i.e., response time) and its relationship to performance can vary significantly from one item to another. Thus, using total or average response time across all items to predict overall test performance may lead to a loss of information, particularly with respect to within-person variability, which refers to fluctuations in a student's standardized response times across different items. This study aims to demonstrate the predictive and explanatory value of including within-person variability in predicting and explaining students' test scores. The data came from 13,829 fourth-grade students who completed the mathematics portion of Problem Solving and Inquiry (PSI) tasks in the 2019 Trends in International Mathematics and Science Study (TIMSS). In this assessment, students navigated through a sequence of interactive screens, each containing one or more related items, while response time was recorded at the screen level. This study used a profile analysis approach to show that students' standardized response times-used as a practical approximation of item-level timing-varied substantially across screens, indicating within-person variability. We further decompose the predictive power of response time for overall test performance into pattern effect (the predictive power of within-person variability in response time) and level effect (the predictive power of the average response time). Results show that the pattern effect significantly outweighed the level effect, indicating that most of the predictive power of response time comes from within-person variability. Additionally, each screen response time had unique predictive power for performance, with the relationship varying in strength and direction. This finding suggests that fine-grained response time data can provide more information to infer the response processes of students in the test. Cross-validation and analyses across different achievement groups confirmed the consistency of results regarding the predictive and explanatory value of within-person variability. These findings offer implications for the design and administration of future educational assessments, highlighting the potential benefits of collecting and analyzing more fine-grained response time data as a predictor of test performance.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".