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Record W3179710327 · doi:10.24908/pceea.vi0.14836

A LAB TASK GROUP FOR REVIEW AND CONTINUOUS IMPROVEMENT OF THIRD AND FOURTH-YEAR LABORATORY COURSES

2021· article· en· W3179710327 on OpenAlexafffundvenueabout
David A. Torvi, Scott Noble, Doug Bitner, Melanie Fauchoux, Rob Peace, Rick Retzlaff, I. N. A. Oguocha, Hayden Reitenbach

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsDeliverableWorkloadComputer scienceTask (project management)Task groupEngineering managementMultimediaEngineeringSystems engineeringOperating system

Abstract

fetched live from OpenAlex

Since the mid-1980’s, the mechanical engineering program at the University of Saskatchewan has included three core third and fourth-year lab courses, each of which consists of 9-10 individual labs. In 2015 a task group was set up to review these courses, including deliverables, scheduling and links to material in corecourses. Since this time, the task group has taken on the major responsibility for continuous improvement of the lab program, including reviewing student evaluations, making changes to labs, and recommending equipment purchases. The task group has also been responsible for a major redesign of the lab program, which will improve delivery and scheduling of labs, alignment with core courses, workload of students, and experience gained by graduate teaching assistants. Smaller apparatus have been designed and built in-house to allow students to gain additional hands-on experience. Labs have been designed to build on one another in order to systematically improve students’ general laboratory skills, including the use of data acquisition systems and experimental design. This new approach was used for the first time in ME 328 in 2019-20. This paper will focus primarily on the role of the task group in continuous improvement, and the lab program redesign. The new ME 328 course is described, along with lessons learned from the first offering. The task group’s role in moving to remote labs during COVID-19 is also discussed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.193
Teacher spread0.191 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2021
Admission routes4
Has abstractyes

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