Improving conceptual learning via pretests.
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
Although examples can be structured to emphasize diagnostic features of concepts, novice learners tend to focus on irrelevant surface features and struggle to encode deeper structures. Experiment 1 examined whether pretesting-answering questions about content before it is studied-could enhance learners' noticing of diagnostic features, making them easier to process during subsequent study. Participants studied statistical concepts with examples that emphasized surface details or deep structure, and then classified new examples of these concepts. Studying examples that emphasized deep structure increased classification performance compared to examples that emphasized surface details. Moreover, taking pretests prior to studying the examples increased classification performance and eliminated differential benefits of studying structure versus surface examples. Experiment 2 examined whether pretesting serves a role beyond directing attention. After studying different statistical concepts with only surface-emphasizing examples, classification performance was better when participants actually took pretests compared to being given the correct responses. It is the generative aspect of pretesting, beyond attention directing, that improves conceptual learning among novice learners. (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 | 0.000 | 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