Impact of the Preparation for Academic Success in Science (PASS) High School to University Transition Program
Bibliographic record
Abstract
The transition from high school to university can be difficult and stressful for many students who are not sure of how to be successful in their courses and become engaged in extracurricular activities beyond the classroom. This paper describes the design and outcomes of the Preparation for Academic Success in Science (PASS) transition program in the Faculty of Science at the University of Windsor, a mid-sized university in Ontario, Canada. The two-day PASS program, offered in the week before fall classes begin, is designed to introduce incoming students to effective study habits, note taking, and preparation for examinations. Moreover, students are advised on how to get involved in undergraduate research, study abroad, service learning, internships, and student organizations, while balancing their time, health and wellness. Results from PASS cohorts between 2017 and 2019 suggest that students who participated in the PASS program had higher major and overall averages in their first and subsequent years, and significantly greater engagement in extracurricular activities compared to the (control group) students who did not participate in the transition program. PASS is presented as an effective transition program, but it is argued that further study is required to determine how academic performance and engagement are related to the intentionality of the student when they start university, and the importance of the program to building community and a sense of belonging.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".