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Record W4320012773 · doi:10.7451/cbe.2022.64.9.1

Supporting teaching practice, program improvement, and accreditation efforts in an engineering program

2022· article· en· W4320012773 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Biosystems Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of ManitobaCanadian Bio-Systems (Canada)
FundersUniversity of Manitoba
KeywordsRubricAccreditationMedical educationWork (physics)Peer assessmentEngineering managementPsychologyEngineering ethicsEngineeringPedagogyMedicine

Abstract

fetched live from OpenAlex

This paper emphasizes the essential role of a support person for faculty teaching and assessing the Canadian Engineering Accreditation Board (CEAB) graduate attributes as part of an ongoing accreditation cycle. It details the continuous program improvement process adopted by the Department of Biosystems Engineering at the University of Manitoba, and the role of engineering stakeholders. It recounts a study that details the supportive efforts of a Research Associate who helped to validate and implement rubrics with individual professors as outcomes-based tools for teaching and assessing the 12 CEAB graduate attributes, which resulted in the creation of 14 rubrics for 12 courses. Findings included new pedagogical understandings, the appreciation of individual support from the Research Associate, and the continued use of rubrics; the work led most professors to think deeply and in new ways about teaching and assessment. There was evidence that six professors engaged in ‘reverse design’, developing rubrics with targeted learning outcomes and course materials in mind. The work led to critical improvement in teaching practices and evidence of continual program improvement. Despite overall engagement and success, some professors continued to struggle with the concept and use of rubrics. In sum, this experience emphasizes the benefit of a dedicated person to support professors to implement rubrics, and in creating and sustaining an outcomes-based assessment culture in the department.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.249
Teacher spread0.245 · 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