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Related Experiment Video

Updated: May 13, 2026

Enabling High Grayscale Resolution Displays and Accurate Response Time Measurements on Conventional Computers

Enabling High Grayscale Resolution Displays and Accurate Response Time Measurements on Conventional Computers

Published on: February 29, 2012

The Value of Individual Screen Response Time in Predicting Student Test Performance: Evidence from TIMSS 2019 Problem

Bin Tan¹, Okan Bulut²

¹Measurement, Evaluation, and Data Science, Faculty of Education, University of Alberta, Edmonton, AB T6G 2G5, Canada.

Journal of Intelligence

|July 25, 2025

Summary

This summary is machine-generated.

Student response time variability across test items, not just average time, significantly predicts academic performance. Analyzing fine-grained timing data offers deeper insights into student test-taking processes and improves assessment design.

Keywords:

individual differences process data profile analysis response time within-person variability

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Last Updated: May 13, 2026

Enabling High Grayscale Resolution Displays and Accurate Response Time Measurements on Conventional Computers

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Published on: February 29, 2012

The 5-Choice Serial Reaction Time Task: A Task of Attention and Impulse Control for Rodents

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Educational Measurement
Cognitive Psychology
Psychometrics

Background:

Student response time (RT) varies significantly across test items, impacting performance prediction.
Traditional analysis using average RT may obscure crucial within-person variability.
Understanding within-person variability in RT is key to unlocking deeper insights into student performance.

Purpose of the Study:

To demonstrate the predictive and explanatory value of within-person variability in RT for student test scores.
To decompose the predictive power of RT into pattern effect (variability) and level effect (average).

Main Methods:

Utilized profile analysis on response time data from 13,829 fourth-grade students in the TIMSS 2019 mathematics assessment.
Analyzed screen-level RT as a proxy for item-level timing, capturing within-person variability.
Decomposed RT predictive power into pattern and level effects; conducted cross-validation.

Main Results:

Within-person variability in standardized RT (pattern effect) significantly outweighed average RT (level effect) in predicting performance.
Individual screen RTs demonstrated unique predictive power, with varying strength and direction.
Results were consistent across different achievement groups and validated through cross-analysis.

Conclusions:

Within-person variability in response time is a critical, often overlooked, predictor of student test performance.
Fine-grained RT data offers richer insights into student cognitive processes during assessments.
Recommendations for future educational assessments include collecting and analyzing detailed RT data.