Predictors of Student Success in Canadian Polytechnics and CEGEPs
Bibliographic record
Abstract
Student success in post-secondary education is an ongoing concern, however, research has focused on relatively homogeneous university samples. Moreover, Canadian research on predictors of student success is limited. Following recent trends, we examined non-cognitive, personal qualities, rather than cognitive predictors (e.g., IQ), of student success. Relying on a psychosocial model, we examined age, gender, perceived stress, maternal education, identity style, perseverance, and student engagement as predictors of student success in a multi-site sample of students attending a CEGEP in Quebec (N = 239; Mage = 18.6 years; 68.2% female) and a polytechnic school in Ontario (N = 209; Mage = 20.6 years; 71.3% female). Maternal education and perseverance emerged as significant predictors in both samples. Links between informational identity and cognitive engagement and student success differed by location. Our findings suggest the need to focus on student perseverance, and to consider identity and cognitive engagement dependent on the educational context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".