Is There a Best Fit? Assessing Alternative Entrance Pathways into an Undergraduate Degree for Non-Traditional Students at York University
Bibliographic record
Abstract
This pilot study examines alternative entrance pathways into York University undergraduate degree programs for students who apply from outside the formal education system. These alternative pathways are designed to facilitate university access for students from under-represented populations (for example, low-income, first-generation, Aboriginal, racialized minorities, differently abled, newcomers to Canada, sole-support caregivers, students with incomplete high school education, or some combination of the preceding). The bridges into a York University undergraduate degree examined in this project are: 1. Direct admission into an academic degree under the ‘mature student’ admissions category 2. Pre-university courses offered through the School of Continuing Studies, serving as a basis of admission into an academic degree under the ‘mature student’ admissions category 3. Women’s Bridging Program – non-credit courses located in the community and serving as a basis of admission into an academic degree under the ‘mature student’ admissions category 4. Transition Year Program – a combination of credit and non-credit courses serving as a full-time ‘qualifying year’ for an academic degree These alternative entrance pathways differ by intensity of intervention, range of interventions incorporated directly into a program, tuition and delivery costs. We know from the literature that under-represented students particularly face a range of academic, cultural and financial barriers. We know that these students differ in their needs, personal characteristics and identities as university learners. Our primary research question is, which entrance pathway is best suited to a student’s situation in light of the barriers she may face?We employed a mixed methods approach to examine this question. A literature review identified the potential barriers experienced by these non-traditional students. Focus groups with students entering through the four different pathways allowed us to explore students’ own perceptions of their pathway experience. Quantitative examination of the financial need (as measured by financial support received), prior experience with post-secondary education (PSE), and subsequent academic performance (as measured by cumulative GPA in the first eight months of their degree studies) of a sample of 1,502 students in five different cohorts across the four alternative pathways offered additional comparative information on the effectiveness of students’ self-selected entrance pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".