The Impact of Depth of Encoding on the Transfer of Test Enhanced Learning
Bibliographic record
Abstract
Abstract. The mediator effectiveness hypothesis states the benefit of retrieval practice is a consequence of the activation of mediators linking cue and target items during review. Evidence has found that mediators are more effective at prompting recall of target words than words not associated with the original cue, a pattern that is larger following testing than restudy. The benefit of testing for unstudied cues at the final test is referred to as transfer of test-enhanced learning. The current study examined whether the activation of mediators is moderated by the depth of processing completed at encoding. During an initial study of weakly related word pairs (e.g., Mother–CHILD), participants completed an encoding task that directed deep, shallow, or no specific depth of processing. During review, participants restudied the pairs or attempted to recall the target given the original cue (e.g., Mother). On the final test, participants were presented with unstudied cues that were related to either the original cue (mediators, e.g., Father) or the target (target-related cues, e.g., Baby). The results found mediator generation during review to be greatly impaired by shallow processing tasks completed during encoding. In contrast, the effectiveness of target-related cues was not affected by depth of processing tasks.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".