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Record W1511984175 · doi:10.24908/pceea.v0i0.4870

INCORPORATING TEAM-EFFECTIVENESS AS A LEARNING OBJECTIVE IN THE DESIGN PROJECT WITHIN A TECHNICAL CORE COURSE

2013· article· en· W1511984175 on OpenAlexaffvenue
Greg J. Evans, Patricia Sheridan, Doug Reeve, Maygan L. McGuire, Kristina Minnella, Estelle Olivia Fisher, Lydia Wilkinson, Todd McAlary

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTeamworkTeam effectivenessVocabularyTeam-based learningPresentation (obstetrics)PsychologyPsychological safetyTeam learningProject-based learningDeliverableLearning stylesCooperative learningMathematics educationKnowledge managementEngineeringMedical educationTeaching methodComputer scienceManagementOpen learning

Abstract

fetched live from OpenAlex

CHE 230 ‘Environmental Chemistry’ is a core Chemical Engineering course offered in the spring semester of second year. A central element of the course is the ‘Environmental Consulting Engineering’ project, a full semester design project that is executed in five-member teams. The acquisition of teamwork and group-leadership skills has been one of the project learning objectives for about five years; the related instructional components have been refined every year. This presentation will describe the theoretical foundation and methods used to teach team-effectiveness as part of this design project. Instruction of team skills has been based on two conceptual frameworks. A leadership styles framework examines the preferences of individual students and helps students see how these styles are manifested within a team. This framework allows students to identify their leadership style preferences and more importantly, recognize the strengths of others, and styles that may be missing from their team. A team-effectiveness framework helps students examine organizational, relational and communication behaviours evident within their team, and thereby helps students to recognise where they do, or do not, tend to contribute. These two frameworks provide students with a shared vocabulary, along with a basis to observe and understand their team experiences that can then be used to promote learning and structured reflection. The instructional components used are an introductory lecture on team effectiveness, a two-hour team formation workshop, a reflection done by the team after the first major deliverable and an individual reflection at the end of the course. These components are intended to direct students from recognizing aspects of team-effectiveness, towards seeing deficiencies in their team or their individual contributions. The students are then guided towards finding practical tools and techniques that they can use to become more effective at different aspects of teamwork.

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)0.016
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0060.003
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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 designNot applicable
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

Citations5
Published2013
Admission routes2
Has abstractyes

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