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Record W2062280517 · doi:10.1177/1469787408095848

Using action research to teach students to manage team learning and improve teamwork satisfaction

2008· article· en· W2062280517 on OpenAlexaff
Brenda Scott‐Ladd, Christopher Chan

Bibliographic record

VenueActive Learning in Higher Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsYork University
Fundersnot available
KeywordsTeamworkSocial loafingPsychologyTeam effectivenessPsychological safetyConflict managementAction researchCooperative learningMedical educationMathematics educationTeaching methodKnowledge managementApplied psychologySocial psychologyManagementComputer scienceSociologyMedicine

Abstract

fetched live from OpenAlex

This article reports on a study investigating strategies that students can use to develop skills in managing team learning. Two groups of second-year management students participated in a semester-long action research project over two semesters. The students were educated on team development, team processes and conflict management and how to review and enhance team development. Teaching staff supported the approach and students were regularly encouraged to reflect on and learn about how their behaviour contributed to team effectiveness. This approach encouraged student participation and ownership as well as early intervention if problems arose. Findings suggest that when students are taught to manage the processes of teamwork and take greater ownership of managing conflict and team relations they report less conflict and less social loafing and are more satisfied with their learning outcomes.

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)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.147
GPT teacher head0.477
Teacher spread0.330 · 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 designQualitative
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

Citations68
Published2008
Admission routes1
Has abstractyes

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