School-University Partnerships: A Model for Supporting Transition and Persistence from K-16
Bibliographic record
Abstract
The reasons why students persist in their post-secondary learning are complex. This paper proposes a model for the development of K-16 partnerships that promote student success through the transition from secondary school to post-secondary, supported by teachers, faculty members, and educational developers. This model proposes that each of the partners engage in developing sustainable, collaborative projects. These projects have at their core a focus on students’ transition from one educational institution to the next, with the intended outcome of increasing rates of persistence, while reducing rates of attrition. Not all students may have post-secondary education as their personal goal, but for those that do, this support model aims to provide a framework to scaffold the transition so that learners are successful, and teachers and faculty are prepared to support learners as they move from one institution to another. Les raisons qui expliquent pourquoi les étudiants persistent dans leur apprentissage postsecondaire sont complexes. Le présent article propose un modèle pour l’élaboration de partenariats K-16 qui vise à favoriser la réussite étudiante lors de la transition de l’école secondaire à l’éducation postsecondaire avec l’appui d’enseignants, de professeurs et de concepteurs pédagogiques. À la base, ces projets se focalisent sur la transition des étudiants d’un établissement d’éducation vers un autre. Ils visent à faire augmenter le taux de persistance et à faire diminuer le taux d’abandon. L’éducation postsecondaire ne constitue pas forcément un but personnel pour tous les étudiants, mais pour ceux dont c’est l’objectif, le présent modèle vise à fournir un cadre pour structurer la transition et soutenir la réussite des apprenants de même que la préparation des enseignants et des professeurs qui appuieront les étudiants dans leur transition.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".