Factors influencing academic success and retention following a 1st-year post-secondary success course
Bibliographic record
Abstract
We examined the psycho-social factors predicting performance and retention following a post-secondary success course that was developed after Rosenbaum's (1990 Rosenbaum, M. 1990. “The role of learned resourcefulness in the self-control of health behavior”. In Learned resourcefulness: On coping skills, self-control and adaptive behavior, Edited by: Rosenbaum, M. 4–25. New York: Springer. [Google Scholar], 2000 Rosenbaum, M. 2000. “The self-regulation of experience: Openness and construction”. In Coping, health and organizations, Edited by: Dewe, P., Leiter, A. M. and Cox, T. 51–67. London: Taylor & Francis. [Google Scholar]) model of self-control and the academic success literature. Before and after the course, students completed measures assessing general and academic resourcefulness, academic self-efficacy, explanatory style for failure, anxiety, impulsivity, inattentiveness, and hyperactivity. Students who were most disadvantaged at the onset of the course were more likely to show the most gains in many of these measures. Students showing the greatest improvements in academic self-control and the greatest declines in hyperactivity-impulsivity following the course were more likely to attain the highest 2nd-term grades. Students deciding not to return to university for their 2nd year had impoverished general or academic resourcefulness skills or both. Suggestions are provided to help educators reach these students.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".