Does generation benefit learning for narrative and expository texts? A direct replication attempt
Bibliographic record
Abstract
Abstract Generated information is better recognized and recalled than information that is read. This generation effect has been replicated several times for different types of material, including texts. Perhaps the most influential demonstration is by McDaniel, Einstein, Dunay, and Cobb (Journal of Memory and Language, 1986, 25(6), 645–656; henceforth MEDC). This group tested whether the generation effect occurs only if the generation task stimulates cognitive processes not already stimulated by the text. Numerous studies, however, report difficulties replicating this text by generation‐task interaction, which suggests that the effect might only be found under conditions closer to the original method of MEDC. To test this assumption, the present study closely replicated MEDC's Experiment 2 in two separate German and English‐speaking samples. The present study provided partial evidence in favor of the expected interaction, which ultimately depended on successful completion of the generation task (with near‐to‐perfect accuracy). Moreover, it indicates that sentence unscrambling might enhance learning across genres.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".