Optimizing Learning Efficiency: Balancing Spacing and Repetition Under Time Constraints
Bibliographic record
Abstract
Spaced retrieval practice has been repeatedly demonstrated to improve learning, but its implementation is often constrained by real-world time limitations. This study investigated whether, under fixed study durations, learners should prioritize spacing or repetition. Across two experiments (total N = 1589), participants practiced Indonesian vocabulary under four conditions that varied in spacing and repetition. Item difficulty was also manipulated. Results showed that increasing repetitions at the cost of spacing enhanced immediate test performance, particularly for harder items. These findings suggest that spaced retrieval practice is effective only when learners have sufficient prior repetitions to retrieve information successfully. This study highlights the trade-offs between spacing and repetition under time constraints and offers practice insights for optimizing learning strategies.
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How this classification was reachedexpand
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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; both teacher heads 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".