Exploring factors facilitating and hindering college-university Pathway Program completion
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore not only the academic measures such as grade point average of success of college-to-university transfer programs (Pathway Programs), but also the social-cultural facilitators and barriers throughout the students’ Pathway experience. Design/methodology/approach – The experience of students and academic advisors moving between Queensdale College and North Star University (NSU) (pseudonyms) were analyzed using a mixed-methods approach including analysis of data from online surveys, secondary data (course performance), and focus group interviews. Findings – Students who are able to enter the Pathway Programs at NSU perform on average better than their four-year traditional program peers. There remain a number of social-cultural barrier which need to be addressed to improve the overall experience of these transfer students. Practical implications – The results from this study will assist the administrative decision makers in designing Pathways and their associated communication plans in order to meet the needs of the students with tools and supports that are both perceived by the students as valuable and are improving their Pathway experience and ultimately their academic performance. Originality/value – The move to develop Pathway Programs in Ontario is a new phenomenon, even in provinces where this is more common, few studies exist which consider the social-cultural aspects of the student journey between the two institutions. This study moves beyond the standard academic performance data and provides insight into the critical role played by the social aspects in higher education experiences.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".