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Record W3036858878 · doi:10.24908/pceea.vi0.14127

DEVELOPMENT OF THE INDIVIDUAL AND TEAM WORK ATTRIBUTE AMONG UNDERGRADUATE ENGINEERING STUDENTS: TRENDS ACROSS 4 YEARS OF ASSESSMENT

2020· article· en· W3036858878 on OpenAlexafffundvenueabout
Alyssa Grocutt, Alec Barron, Malika Khakhar, Tom O’Neill, William Rosehart, Robert W. Brennan, S. Li

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsTeamworkAccreditationMedical educationWork (physics)PsychologyPsychological interventionEngineering educationEngineeringEngineering managementMedicinePolitical science

Abstract

fetched live from OpenAlex

The Engineers Canada Accreditation Board outlines 12 Canadian Engineering Graduate Attributes required for program accreditation. One of these attributes is Individual and Team Work. Since 2016, at the University of Calgary, there has been a voluntary, undergraduate-wide survey administered to the Schulich School of Engineering students every spring via an online platform. The purpose of the survey is to assess students’ perceived development of teamwork skills during their program, and identify avenues to improve program offerings. After four consecutive years of this survey, with sample sizes ranging from 683-973 students, there are three main trends that can be identified: students perceive teamwork skills as highly important for their future careers, there are noticeable differences between male and female students regarding teamwork experiences, and students value teamwork skills training and opportunities for peer feedback. Implications of these findings are that there are gendered teamwork experiences among undergraduate engineering students and more research is needed to understand interventions that can mitigate this.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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 designObservational
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

Citations2
Published2020
Admission routes4
Has abstractyes

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