An interdisciplinary review of learning through failure in higher education
Bibliographic record
Abstract
This critical review examines how learning through failure is understood and mobilized in higher education. It maps how failure and associated concepts have been put to work across different post-secondary teaching and learning contexts and serves four purposes: (1) to bring literature from different disciplines into conversation with each other; (2) to provide a shared conceptual vocabulary for discussing failure across disciplines; (3) to highlight how educational practitioners can teach students to embrace, learn from, and bounce back from failure; and (4) to capture both a range of theoretical frameworks as well as praxis – practical classroom activities – which are currently in use. This review identifies several significant research gaps in the literature and opportunities for new avenues of inquiry regarding the role of failure in pedagogy, learning, and course design. Gaps include the absence of research on the institutional/systemic context of learning through failure; the role power and privilege plays in having the opportunity and resources to engage with and bounceback from failure; and concrete advice and support structures for instructors that bring failing forward activities into their classroom. This review not only provides an overview for instructors on how students can engage with, learn from and recover from failure but also advocates for future cross-disciplinary collaborations that engage with failure as a complex experience informed by multi-scalar processes.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".