Failing to Learn: Design Thinking and the Development of a Failure-Positive Mindset in the University Classroom
Bibliographic record
Abstract
Meaningful and impactful learning experiences are rife with failure. And yet, students struggle with framing, tolerating and attributing failure in a positive manner within the post secondary learning context. This paper explores whether using design thinking as a pedagogical approach might help students learn to tolerate, reframe and attribute failure in a more productive way. Findings from this comparative study of 600 undergraduate business students enrolled in a common first year marketing class reveal the ways in which design thinking-based learning approaches might be used to re-orient student’s conceptions of failure as a part of their creative problem-solving skill development process. Students were surveyed to learn more about how they perceived the concept of failure within their learning, to whom they attributed failures within their learning, and how well they tolerated failure as a part of their learning experience. Results from the nearly 400 responses to the online survey suggest that integrating design thinking focused approaches to learning into the post secondary classroom has a positive impact on the development of a student’s self-reported failure tolerance and may change the way that failure is attributed and framed in students’ descriptions of their individual learning. I find that design thinking-based learning might be used as an effective pedagogical approach in classes where the development of a failure-positive mindset is considered an essential competency or learning objective, and I offer practical recommendations for educators seeking to develop a failure-positive mindset within their learning communities.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".