Design of a Completely New First Year Engineering Program at the University of Saskatchewan– Part III
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aiming at improving the first-year engineering experience by engaging, inspiring, and holistically preparing students for the challenges in the future, the College of Engineering at the University of Saskatchewan redesigned and launched a RE-ENGINEERED First Year (REFY) Program. The project has had three phases, and the first two were reported at CEEA conferences in 20181 and 20192. A small-scale pilot of the competency-based assessment (CBA), one of the key features of the REFY program, was presented at CEEA 20213. In this paper, we will report the changes made to the program structure (Phase II), present the systematic procedure followed to develop the course materials (Phase III) and reflect on the first implementation of the REFY program. This paper will also discuss the proposed changes to the program for next year’s implementation.
 The REFY program consists of 10-12 modular courses with various durations and intensities each term. To better sequence materials and to facilitate just-in-time learning, the structural design of the program was further refined over the past two years via adjusting the start/end dates and contact hours of the modular courses.
 The development of course materials was directed by the identified graduate attributes (Phase I), integrated curriculum schedule (Phase II), and the philosophy of CBA. With the lessons learned from the pilot of CBA in a single first year engineering course, we defined four types of assessments and the evaluation criteria. We also developed a course preparation procedure template and created a series of policies to support the program.
 Initiating the REFY program during the COVID pandemic was a victory, although we identified some challenges and problems in Term 1 through teaching, observations, and reflection, as well as anecdotal comments. We will share the lessons learned, our opinions on the potential causes of the challenges and problems, and propose revisions for the next iteration of program refinement.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it