Linking learning goal orientation to learning from error: the mediating role of motivation to learn and metacognition
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
Purpose Errors are increasingly recognized as beneficial to the learning process and are more frequently integrated into training curriculums. Despite this growing interest, the work carried out so far offers little evidence highlighting the psychological qualities implicit in learning from error. By focussing on the role of specific trainee’s attributes [i.e. learning goal orientation (LGO) motivation to learn and metacognition], this study aims to better understand the reasons why some trainees benefit more (than others) from being confronted with errors during training. Design/methodology/approach A total of 142 trainees took part in this study by participating in a training on interviewing techniques that also exposed them to various committable errors, and by completing questionnaires at two different times (i.e. before and after training). Findings Results of bootstrap regression analysis highlights three main findings: LGO is positively linked to learning from errors; a significant portion of the link between LGO and learning from error is explained by motivation to learn and metacognition; and these effects are presented in the form of a double-mediated model which suggests two different explanatory pathways (i.e. motivational and cognitive). Originality/value To the best of the authors’ knowledge, this study is among the first to offer insight on the psychological attributes influencing learning from errors and to bring forward the role of two underlying mechanism that are linked to this specific type of learning. It also invites researchers and practitioners to reflect on the best ways to make use of errors in training and promote the value of personal attributes on trainees’ learning experience.
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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 | 0.004 | 0.000 |
| 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