Bridging Between High School and University: Assessment of Students’ Readiness to First-Year Engineering Programs
Bibliographic record
Abstract
This descriptive case study uses self-assessment tools to explore 26 prospective students’ preparedness regarding their knowledge and competencies for entering the first year of engineering programs at a large, research-intensive Canadian university. We aim to provide insight for developing the Brigde2Engg (B2E) Program to empower and support students in their transition to university. Applying Conley’s college readiness model as a theoretical framework, findings from this study reveal that most students appear confident about their knowledge and skill preparedness for the first-year engineering programs, including their critical thinking abilities, problem-solving skills, and understanding of engineering professions. However, they are less confident in physics and the use of engineering tools. Therefore, we suggest that, when they choose to participate in the bridging program, these students should focus mainly on the subject they deem inadequate. Results also show that an introduction to engineering tools is essential to familiarize students with the programming and spreadsheet software they will use throughout their university programs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".