Analysis of a Bridging Program for Incoming First-Year Students to Transition from Secondary Education to Engineering Education
Bibliographic record
Abstract
Bridging programs exist in many institutions to address the challenges incoming students experience. This study aims to identify some of the benefits and challenges of one such program at the Faculty of Applied Science and Engineering at the University of Toronto. Developed during the pandemic, this program aims to reduce the step-in height as students transition from secondary to post-secondary. This exploratory research uses a program evaluation framework to analyze this program. Literature review suggests a systems approach is an effective method, as it dissects the program into its fundamental parts and provides insights to the decision-makers of such programs [1]. We gather anecdotes from entities within the program and pair them with the analysis to identify if this program improves the student experience, and to what degree. This study is not an evaluation of the program, but rather a discussion about bridging programs and factors to consider when designing them. Findings from this study inform developers of bridging programs about their effect on the student experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".