Distributed practice and time pressure interact to affect learning and retention of arithmetic facts
Bibliographic record
Abstract
Arithmetic is commonly taught through timed practice and drill, yet little research exists to guide optimal practice structure. This study investigated the effects of distributed practice and time pressure on the acquisition and retention of arithmetic facts. Following a pretest, adult participants (n = 211) were randomly assigned to learn unfamiliar times tables (17 and 19) in one of ten conditions in a 5 (spacing: daily, every other day, weekly, every 10 days, every other week) x 2 (time pressure: timed or untimed) factorial design. After the learning phase, retention tests were given to measure both accuracy and response time immediately, after a ten-day delay, and at the end of semester. Time pressure during learning elevated participants’ perceived stress. It also led to faster response times during testing when learning was spaced daily and every other day, but slower response times for all other spacings. These patterns were reversed in the absence of time pressure during learning. While timed and untimed practice during learning led to similar forgetting of practiced facts over time, untimed practice allowed participants to gradually improve on unpracticed facts and conceptually related facts across test phases. Ultimately, distributed practice and time pressure may interact in complex ways to affect the learning and retention of arithmetic facts, and the effects shown in previous studies using verbal material (e.g., narrative texts, word lists) may not generalize to arithmetic.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".