Pan, Sana, Samani, Cooke, and Kim (2020) Students' and instructors' beliefs about learning from errors
Bibliographic record
Abstract
In some educational contexts, such as during assessments, it is essential to avoid errors. In other contexts, however, generating an error can foster valuable learning opportunities. For instance, generating errors can improve memory for correct answers. In two surveys conducted at three large public universities in North America, we investigated undergraduate students’ and instructors’ awareness of the pedagogical benefits of generating errors, as well as related practices, attitudes, and beliefs. Surveyed topics included the incorporation of errors into learning activities, opinions about the consequences of studying errors, and approaches to feedback. Many students had an aversion towards making errors during learning and did not use opportunities to engage in errorful generation, yet studied or analysed errors when they occurred. Many instructors had a welcoming attitude towards errors that occur during learning, yet varied in providing students with resources that facilitate errorful generation. Overall, these findings reveal the prevalence of an ambivalent approach to errors: Students and instructors avoid generating errors but prioritise learning from them when they occur. These results have important implications for the implementation of pretesting, productive failure, and other error-focused learning techniques in educational contexts.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".