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Record W4239134430 · doi:10.24908/pceea.vi0.13793

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

2019· article· en· W4239134430 on OpenAlexafffundvenueabout
J. Frey, Sean Maw, Susan Bens, Bruce F. Sparling

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsSummative assessmentScheduleModular designDuration (music)Project-based learningFormative assessmentComputer scienceEngineering managementMathematics educationAcademic yearMedical educationPsychologyEngineeringMedicine

Abstract

fetched live from OpenAlex

Over the last three years, the University of Saskatchewan’s College of Engineering has embarked upon a complete redesign of its first year common program. This project started from a blank slate and posed the question, "If we could design any first year program that we wanted, what would we create?" The goal is to offer a first year program that excites, engages, inspires, and holistically prepares students for learning in subsequent years. At CEEA 2018, Phase I of this project was reviewed with a focus on the content of the new first year. This year, the focus is on the structure of the proposed program and how it aims to satisfy programmatic design objectives. The proposed first year program is highly modular, allowing for more intentional uses of time during the academic year. Course duration and intensity vary and are selected to best serve student learning, rather than conform to the traditional academic schedule. To provide more timely and targeted feedback, summative assessments occur throughout each term allowing course scheduling to extend into the traditional end-of-term final exam period. This paper presents the current structural design of the new first year and the rationale for its significant features. Some of the program design objectives that have been facilitated by this structure include: • strategic sequencing of learning with opportunities to integrate and reinforce essential skills, • multiple, individualized opportunities for students to stumble and recover, • holistic balancing of content and pacing for better student wellbeing, and • comprehensive, well-timed exposure to wide-ranging programmatic choices for students. Throughout this project, the program structure has evolved continuously. This paper will describe the development process, the challenges faced in that process, and the lessons learned. The paper will conclude by describing the current status of the project, and the focus of work currently being undertaken to prepare for implementation.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.003

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 head0.006
GPT teacher head0.161
Teacher spread0.156 · 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
GenreEmpirical

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

Citations13
Published2019
Admission routes4
Has abstractyes

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