Overcoming Unpleasant Affective Experiences when Learning: A Latent Profile Analysis of Resilience
Bibliographic record
Abstract
Drawing on conceptualizations of affective experience (Seo, Barrett, & Bartunek, 2004) and experiential learning theory (Kolb, 2014), resilience in learning is considered the capacity to initiate, persist, and direct effort towards learning when experiencing unpleasant affective states such as frustration. In this study, factors shown to support learning were reviewed, a four-factor model of resilience was developed and tested, and a latent profile analysis was completed to test the likelihood of resilience based on profiles associated with differences in scores when learning during frustration and learning during progress. The four resilience factors were positive emotional engagement, creative problem-solving, learning identity, and social support. Results provided support for two latent profiles of resilience: A quarter of individuals could activate the four learning resilience factors under both pleasant and unpleasant affective experiential states, while most saw decrements in these factors when faced with unpleasant affect such as frustration. The results support an underlying structure of resilience factors and the view of resilience as a process of buffering and self-regulation when experiencing unpleasant affective states. Further studies are needed on how to buffer the decremental impact of unpleasant affective experiential states.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".