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Record W2555029378 · doi:10.18260/p.27065

Towards a Multidisciplinary Teamwork Training Series for Undergraduate Engineering Students: Development and Assessment of Two First-year Workshops

2016· article· en· W2555029378 on OpenAlexafffund
Ada Hurst, Erin Jobidon, Andrea Prier, Taghi Khaniyev, Christopher Rennick, Rania Al-Hammoud, Carol Hulls, Jason Grove, Samar Mohamed, Stephanie Johnson, Sanjeev Bedi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsTeamworkMultidisciplinary approachEngineering educationCurriculumContext (archaeology)PsychologyMedical educationEngineeringPedagogyEngineering managementManagementSociologyMedicine

Abstract

fetched live from OpenAlex

Teams have become the default work structure in organizations; thus, in work settings that emphasize teamwork, employees must have knowledge, skills and abilities (KSAs) to communicate and coordinate with their colleagues. Yet, teamwork skills are rarely "taught" in engineering curricula; in fact, compared to business representatives, university educators have been found to underestimate the value of teamwork KSAs. Instead, students are expected to develop teamwork and leadership skills via a sink-or-swim approach where they are assigned group work and left to perform as they can. Often, these poor teamwork experiences combined with the lack of training and opportunities for guided reflection lead to students disliking working in groups, impacting not just the cognitive but also the affective domain of learning.

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)0.011
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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 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

Citations25
Published2016
Admission routes2
Has abstractyes

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