Failure as a Means of Creating Spaces for Learning and Growth
Bibliographic record
Abstract
Failure is often seen as a deterrent of success, rather than part of a learning process. This work examines the practical effects of failure in different learning environments, such as engineering design courses at the University of Guelph, where failure is often established during design, prototyping, and problem-solving tasks. Observations have indicated that students consider failure as a setback rather than an opportunity for improving. When introducing reflective practices, thismay help view failure as a tool for constructive learning. This work looks into the effects of failure that can be integrated into learning spaces through the use of reflective practices. The significance of this work lies in how the concept around positive failure can be applied with learning inside and outside the classroom. This work compares methodological approaches that assess the role of failure in learning outcomes. The aim is to unlock strategies that can redefine how failure is perceived within education and pushing failure to become an initiation for growth. This work looks to address questions including: How can reflective practices reshape students' perceptions of failure? What strategies are effective in transforming failure into a learning tool within engineering? How can these approaches be implemented beyond the classroom?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".