Evidence of the Spacing Effect and Influences on Perceptions of Learning and Science Curricula
Bibliographic record
Abstract
The conventional science curricula generally favour educational practices that yield high scores on immediate examination, though it may not accurately predict students' long-term academic achievement. In view of the pre-exam cramming phenomenon, this article shows the evidence of spacing effect in science education and probes into its theoretical mechanisms, effectiveness in experimental settings, and current applications in science learning. In brief, spacing works by repeatedly presenting the learning material across various temporal intervals. This paper suggests that spacing could significantly result in greater memory strength by alleviating multiple neurocognitive and behavioural properties of learning that are hampered by cramming. Together with the analysis of its relevance in science education, the spacing effect may further provide leverages for promoting long-term conceptual understanding and reflective skill development. However, there are many reasons that students and teachers may not be aware of or fully appreciate its benefits. Finally, this article discusses systemic barriers to why spaced repetition is underutilized in science curricula.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".