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Team Dynamics Feedback for Post-Secondary Student Learning Teams

2017· article· en· W2765424054 on OpenAlexaff
Tom O’Neill, Amanda Deacon, Katherine Gibbard, Nicole Larson, Genevieve Hoffart, Julia Smith, Magda M. Donia

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSuiteTeam compositionContext (archaeology)PsychologyTeam effectivenessMedical educationHealth careApplied psychologySample (material)PerceptionReliability (semiconductor)Knowledge managementComputer scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

In the current research we introduce the team CARE model for supporting team development during post-secondary education. Team CARE is part of a larger suite of assessments at itpmetrics.com. Team CARE is a free online survey-based assessment that allows team members to rate their team’s health and functioning in four key domains (Communicate, Adapt, Relate, and Educate), as well as provide written feedback about the team’s functioning to add nuance and supplemental context to the numeric scores. We report that the team CARE scales were found to have acceptable reliability and were associated with team performance outcomes. Students’ perceptions of the tool were also examined and the findings suggest that team CARE feedback is perceived to be valuable, useful, and easy to use. Recommendations for practice are detailed, including sample assessment schedules for teams with differing life spans. Implications for future research and implementation are discussed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.330
Teacher spread0.314 · 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
Published2017
Admission routes1
Has abstractyes

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