Interpreting Students' Experiences with Academic Disappointments Using Resourcefulness Scores as a Lens
Bibliographic record
Abstract
Most postsecondary students have to deal with academic disappointments at some point in time, with many of them succumbing to their anxieties and failing to learn from these lived experiences. Our study aimed to understand the “why and how” disappointments unfolded in a sample of 20 undergraduate students, using a design whereby interview text was concurrently analyzed across the continuum of learned resourcefulness in conjunction with an inductive, data-driven coding, and theme generation perspective. Reasons for attending university, attributional style, coping and learning, and perceptions of others markedly differed for high- and low- resourcefulness scorers. Whereas high-resourceful scorers used academic disappointments as a motivator to engage in more effort and problem-solving strategies, low scorers ruminated and tried to forget about them. Suggestions are provided on ways to effectively help students become more resourceful and in control of their studies.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".