Trainers’ responses to errors matter in trainees’ learning from errors: evidence from two studies
Bibliographic record
Abstract
Purpose Learning from errors is important for employees, particularly at early stages of their career. The purpose of this paper is to examine the influence of perceived trainer responses to errors on trainee learning from errors in a workplace setting. In Study 1, the authors test a model that examines the associations between perceived trainer responses to errors and trainee learning from errors, which are mediated by affective-motivational adaptivity. In Study 2, the authors further hypothesize that the link between perceived trainer responses and affective-motivational adaptivity is moderated by perceived error climate. Design/methodology/approach The authors test the hypotheses using data from 213 Swiss apprentices (Study 1) and 1,012 German apprentices (Study 2) receiving dual vocational training. Findings Study 1 suggests that negative trainer reaction impedes trainee learning from errors by impairing trainees’ affective-motivational adaptability. Trainer tolerance of errors and trainer support following errors were not related to trainee learning from errors. Study 2 indicates that perceived error climate is an important boundary condition that affects the relationship between trainer responses and trainee learning from errors. Originality/value This study contributes to research on learning from errors in three ways. First, it enriches the understanding regarding the role of trainers in enhancing learning from errors in organizations. Second, it extends research on learning from errors by investigating the interaction effects between perceived trainer responses and error climate. Third, it refines knowledge about the role of positive affect in learning from errors. Findings of this study also offer practical insights to trainers and managers regarding what they should do to encourage trainee learning from errors.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".