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Record W3001122246 · doi:10.24908/pceea.vi0.13825

DESIGN TEAM PERFORMANCE: A COMPARISON BETWEEN SELF-FORMED TEAMS AND TEAMS WITH DIVERSE COGNITIVE MODES

2019· article· en· W3001122246 on OpenAlexaffvenue
Michele Hastie

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCreativityCognitionPsychologyDiversity (politics)Capstone courseCognitive styleCapstoneQualitative propertyMathematics educationApplied psychologySocial psychologyPedagogyComputer scienceCurriculumSociology

Abstract

fetched live from OpenAlex

The impact of cognitive mode diversity on team performance and student satisfaction was assessed qualitatively and quantitatively in a capstone chemical engineering design course. In the capstone design course, students were permitted to form their own teams and the distribution of cognitive modes was assessed. In a concurrent design course, the same group of students performed projects in instructor-formed teams that optimized the distribution of cognitive modes. The results indicated no significant difference in team satisfaction between teams that had different levels of cognitive diversity. Although trends seemed to indicate higher rank in the course and greater independence and creativity for groups with higher cognitive diversity, these differences were not statistically significant. Generally, students seemed to have similar experiences in student-formed and instructor-formed groups. However, qualitative comments seem to indicate that groups may have worked more professionally and cohesively in the more cognitively-diverse, instructor-formed groups.

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 score0.185
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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