What Is Learned from Errors: Development and Validation of a Learning from Errors Inventory
Bibliographic record
Abstract
Learning from errors may result in changes in the mental model of a task and lead to performance improvement, or reduced error occurrence (e.g., Ellis, Mendel, & Nir, 2006; Harteis, Bauer, & Gruber, 2008). We build on this concept of error learning, to further advance our understanding of the nature and content of such learning. We develop and validate a multidimensional learning from errors (LFE) scale that includes task-related, error prevention, error management and emotional coping learning outcomes. We demonstrate that using a four-dimensional LFE scale, yields better predictions than the existing measures individually or in combination. Additionally, we discuss conceptual and empirical distinctions among the LFE dimensions and illustrate differences in their nomological nets. Comparison of the four existing unidimensional error learning scales with the new LFE inventory shows that existing scales tap mostly individual learning to correct mistakes, to a lesser extent learning to prevent mistakes, and only modesty capture task learning or improvements in one’s emotional coping. Thereby, the new inventory provides scholars with a more nuanced framework to theorize about the antecedents and consequences of error learning and practitioners with ways to stimulate most relevant learning outcomes.
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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 | 0.030 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".