Interleaving Retrieval Practice Promotes Science Learning
Bibliographic record
Abstract
Can interleaved retrieval practice enhance learning in classrooms? Across a four-week period, students (N = 155) took a weekly quiz in their science courses testing half of the concepts taught in that week. Questions on each quiz were either blocked by concept or interleaved with different concepts. A month after the final quiz, students were tested on the concepts covered in the four-week period. Replicating the retrieval practice effect, participants performed better on concepts that had been on blocked quizzes (M = 54%, SD = 28%) than on concepts not been quizzed (M = 47%, SD = 20%, d = .30). Interleaved quizzes led to even greater benefits, revealing an interleaving benefit: participants performed better on concepts that had been on interleaved quizzes (M = 63%, SD = 26%), than concepts that had been on blocked quizzes (d = .35). These results demonstrate a cost-effective strategy to promote classroom learning.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".