Pretesting versus posttesting: Comparing the pedagogical benefits of errorful generation and retrieval practice.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of practice tests to enhance learning, or test-enhanced learning, ranks among the most effective of all pedagogical techniques. We investigated the relative efficacy of pretesting (i.e., errorful generation) and posttesting (i.e., retrieval practice), two of the most prominent practice test types in the literature to date. Pretesting involves taking tests before to-be-learned information is studied, whereas posttesting involves taking tests after information is studied. In five experiments (combined n = 1,573), participants studied expository text passages, each paired with a pretest or a posttest. The tests involved multiple-choice (Experiments 1-5) or cued recall format (Experiments 2-4) and were administered with or without correct answer feedback (Experiments 3-4). On a criterial test administered 5 min or 48 hr later, both test types enhanced memory relative to a no-test control, but pretesting yielded higher overall scores. That advantage held across test formats, in the presence or absence of feedback, at different retention intervals, and appeared to stem from enhanced processing of text passage content (Experiment 5). Thus, although the benefits of posttesting are more well-established in the literature, pretesting is highly competitive with posttesting and can yield similar, if not greater, pedagogical benefits. These findings have important implications for the incorporation of practice tests in education and training contexts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it