DOES STUDYING VOCABULARY IN SMALLER SETS INCREASE LEARNING?
Bibliographic record
Abstract
The present study examined the effects of part and whole learning on the acquisition of second language (L2, English) vocabulary. In whole learning, the materials to be learned are repeated in one large block, whereas, in part learning, the materials are divided into smaller blocks and repeated. Experiment 1 compared the effects of the following three treatments: 20-item whole learning, four-item part learning, and 10-item part learning. Unlike previous studies, part and whole learning were matched in spacing. In Experiment 2, spacing as well as the part-whole learning distinction were manipulated, and the following three treatments were compared: 20-item whole learning, four-item part learning with short spacing, and four-item part learning with long spacing. Results of the two experiments suggest that, (a) as long as spacing is equivalent, the part-whole distinction has little effect on learning, and (b) spacing has a larger effect on learning than the part-whole distinction.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".