Sinking or Swimming in an Academic Pool: A Study of Resiliency and Student Success in First-Year Undergraduates
Bibliographic record
Abstract
The transition from high school to post-secondary education presents challenges for students. Many variables have been identified as significant predictors of student achievement. Resiliency, defined as the ability to overcome challenges and adversity, may be particularly relevant during the adjustment to post-secondary education. This study assesses whether resiliency incrementally predicts student success after controlling for additional predictors. Participants were 277 undergraduate students who completed self-reports of academic skills, resiliency, personality variables, emotional intelligence (EI), and perfectionism. Students’ year-end GPA was collected from the university registrar. Hierarchical regression analysis revealed that resiliency, measured by sense of mastery, negatively predicted GPA after controlling for other predictors. The sense of mastery facet of self-efficacy positively predicted GPA; however, the adaptability facet was a significant negative predictor of GPA. Findings suggest that self-efficacy is a salient predictor of academic success, and that strong academic skills may serve as a protective factor for poor adaptability.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".