The faces of failure: understanding students’ affective experiences with failure in introductory chemistry laboratory learning activities
Bibliographic record
Abstract
Failure is recognized as valuable to learning in the classroom and research contexts. While interventions incorporating failure into learning have been explored in science education, the affective experience of failure is less understood. From an affective experience lens, failure is stigmatized and not accounted for in learning and assessment. This study explores introductory chemistry students’ affective experiences with failure through qualitative semi-structured interviews framed from an interpretivist lens. Students shared that failure is overwhelming, shapes their beliefs, is not accounted for in course design, and is defined by the learning and assessment outcomes. Asking students to fail as a part of their learning is much more nuanced than previously discussed interventions where failure is part of the design. This study explores the idea that not all failures are created equal and provides insight into laboratory activities and assessments that ask students to fail. Paying attention to students’ experiences can change your mindset as an educator and offer pathways to creating learning environments that reduce judgment, allow instructors to share their own failures, and offer feedback to help students move forward with their failures.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".