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Record W4405675782 · doi:10.24908/pceea.2024.18523

Learning materials for community engagement in engineering design courses

2024· article· en· W4405675782 on OpenAlexaffvenue
Pranav Chintalapati, Zeina Baalbaki, Tamara Baldwin

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCommunity engagementEngineeringEngineering ethicsMathematics educationPsychologyEngineering managementArchitectural engineeringPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

Engineering students' increasing interest in addressing real community-based challenges underscores the need for ethical community engagement in engineering design courses. This paper explores the development and integration of learning materials across all three levels of the Environmental Engineering program at the University of British Columbia (UBC). Collaborative efforts between instructors, a project assistant, and the Office of Regional and International Community Engagement led to the customization of course materials, aligning them with the specific learning objectives and teaching methods of each course. Customization resulted in a greater breadth of unique content. While the materials appeared to generally enhance student understanding of ethical community engagement practices, topics that deviate from traditional engineering design content, such as Indigenous ways of knowing and power imbalances, were less clearly demonstrated in student outputs. The collaborative approach illuminates the potential for positive incremental outcomes in engineering education, emphasizing the iterative nature of educational reform within content-rich curricula.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
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 head0.016
GPT teacher head0.225
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations1
Published2024
Admission routes2
Has abstractyes

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