Does the interleaving effect extend to unrelated concepts? Learners’ beliefs versus empirical evidence.
Bibliographic record
Abstract
When learning new information, should students focus on studying 1 concept at a time or should they alternate studying between different concepts? Recent research shows that students should mix up or interleave the study of different concepts, particularly when the concepts are related or hard to discriminate (Carvalho & Goldstone, 2015). But students rarely study only 1 course, so how should the study of unrelated courses be sequenced? Should the study sessions be blocked by course to avoid unproductive juxtapositions or be interleaved across different courses because it inherently involves spaced practice, which is also effective for learning? In Experiments 1 and 2, we explored how students construct their study sessions by using hypothetical scenarios. Finally, in Experiment 3, we experimentally manipulated the study sequence of related concepts within 2 unrelated domains (i.e., physics and statistics). Given only 1 level to schedule (related modules or unrelated courses; Experiment 1), students chose to block related modules but to interleave unrelated topics—even though the literature suggests the related concepts are more likely to benefit from interleaving. Given 2 levels to schedule (concepts and domains; Experiment 2), students chose to interleave everything—even though empirical data from Experiment 3 suggests that the optimal schedule involves interleaving at either the concept or the domain level, but not both or neither. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
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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 | 0.024 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".