Sustaining the Transformation: Improving College Retention and Success Rates for Youth from Underserved Neighbourhoods
Bibliographic record
Abstract
Student retention is an issue of perennial interest to educational institutions and is frequently a focus of pedagogical research and evaluation of programming, although some recent literature on factors affecting college student retention recommended attention to academic preparedness and student engagement as key variables influencing college student retention. This study explored the barriers and facilitators of retention and attrition of Helping Youth Pursue Education (HYPE) program participants in regular college programming, and the role of service supports and mentorship in improving the college experience of youth from underserved neighbourhoods. The qualitative research focused on how gains in interpersonal and problem-solving skills and connecting with one or more mentors at the College related to student success. The analysis revealed that, while the current program delivery model has resulted in steady improvement in outcomes relating to application, admission and student success for those who are “ready,” more could likely be done to improve participant experience and outcomes. Recommendations for improvement focused on strategies for the early identification of HYPE program participants likely to enter post-secondary education, faculty and staff development to enhance program delivery, and consideration of other program amendments to improve outcomes. Further work is needed to explore reasons why students do not follow-through on their learning plans, and to find ways to encourage them to do so.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".