What insights can response times provide for education research?
Bibliographic record
Abstract
The main objective of this methodological article is to discuss the contribution of response times as a tool in education research. The use of response times in research is largely a legacy of the work carried out in cognitive psychology, which has made it possible to describe the cognitive processes involved in information processing. In education, research that incorporates response times into its methodological design often has two main objectives: 1) evaluate the automation of basic learning and 2) infer the cognitive processes involved in academic learning, such as working memory and inhibitory control. This article addresses and discusses specific research designs that use response times across various academic disciplines and instructional levels, offering a comprehensive overview of the potential applications of response times in education research. In light of this overview, utilizing response times in education research not only emerges as relevant but also serves to complement existing methodological tools. For instance, such designs can aid in identifying the relative difficulty of different types of learning, understanding the underlying reasons for such variations, and offering valuable insights for developing learning sequences and pedagogical interventions that are more consistent with learning processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.243 | 0.668 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".