The Role of Student Involvement and Engagement in Rural and Urban High School Environments on Postsecondary Transition and Academic Success
Bibliographic record
Abstract
In Canada’s diverse educational landscape, students transition from various high school environments is influenced by socioeconomic, demographic, and community-specific factors. This diversity affects their preparedness for higher education and presents both challenges and opportunities for higher education institutions to ensure rich and successful student experiences by offering effective transition support. While many studies explore the connection between secondary or postsecondary involvement and success, there is a gap in research comparing how high school involvement in rural versus urban settings has an impact on postsecondary success. This study examined the impact of high school involvement—measured by hours spent on homework, classroom participation, and extracurricular activities—on students’ integration and academic success in postsecondary education, with prominent differences between rural and urban settings. Found, in the quantitative phase, were correlations, to different degrees, between high school involvement and postsecondary success. Qualitative data complements these findings by illuminating the factors that influenced the correlations between high school involvement and the challenges and successes encountered during the transition to postsecondary education. Implications for secondary and postsecondary instructors and advisors, as well as policymakers, are included.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".