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Record W4308713558 · doi:10.24908/pceea.vi.15963

Design of a Completely New First Year Engineering Program at the University of Saskatchewan– Part III

2022· article· en· W4308713558 on OpenAlexaffvenueabout
Whitney Curtis, J. Frey, Shaobo Huang, Glyn Kennell, Xiaoyi Mao, Sean Maw, Randi Strunk

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsModular designCurriculumScheduleScale (ratio)Engineering managementComputer scienceEngineeringPsychologyPedagogyProgramming language

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.003

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.

Opus teacher head0.007
GPT teacher head0.165
Teacher spread0.158 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations8
Published2022
Admission routes3
Has abstractyes

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