Assessing High School Preparedness for University Engineering Programs: A Study of First-Year Students at the University of Saskatchewan
Bibliographic record
Abstract
The transition from high school to engineering school is notably challenging, exacerbated by the COVID-19 disruptions. The College of Engineering at the University of Saskatchewan introduced Summer Top Up (STU) quizzes to enhance incoming students' readiness, covering essential subjects like Math, Physics, Chemistry, Reading and Writing, Indigenous Culture, and Computer Programming. The perceived usefulness of these quizzes is analyzed using survey results from first-year engineering cohorts (2021-2022 and 2022-2023). The survey also examined students' self-confidence and self-perceived preparedness from high school upon entering first-year engineering, considering demographic factors. Findings revealed no gender disparities but noted differences in preparedness levels based on age, regional background, and community background. The STU quizzes were generally well-received, though their effectiveness varied across subjects. The study recommends targeted support for older, rural, and international students and emphasizes the importance of refining preparatory programs to align with students' needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".