A Framework for Learning From Erroneous Examples and Meta-Analysis of Empirical Research
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
While there is ample theoretical and empirical evidence detailing which conditions benefit learning from one’s own errors, the evidence on learning from others’ errors has not yet been synthesized. In this meta-analysis, we examine the overall impact of erroneous examples on learning and the effects of potential moderating variables based on a novel framework. Following the robust variance estimation method, we synthesized findings from 42 papers (177 effect sizes) comparing erroneous examples with correct examples or problem-solving in experimental studies. The results revealed a statistically significant but weak effect of erroneous examples on learning (g = .136). Further analysis indicated a statistically significant moderating effect of the design of error-explanation activities. Specifically, providing self-explanation prompts or instructional explanations enhanced learning from erroneous examples more than not providing any error explanations. Our findings draw attention to the design of error explanation activities as well as several areas for future research.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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