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Record W3036173571 · doi:10.24908/pceea.vi0.14163

SELF-AWARENESS AND EMPATHY AS TOOLS TO MITIGATE CONFLICT, PROMOTE WELLNESS, AND ENHANCE PERFORMANCE IN A THIRD-YEAR ENGINEERING DESIGN COURSE

2020· article· en· W3036173571 on OpenAlexaffvenue
Jenna Usprech, Gabrielle Lam

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmpathyPsychologyConflict managementConflict resolutionTeamworkPreparednessPsychological interventionSituation awarenessApplied psychologyAccountabilitySocial psychologyMedical educationEngineeringPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Historically, students in engineering design courses learn how to resolve conflict almost exclusively through experience and with varying degrees of success, which can have ramifications on student wellness and performance [1]. Instructors can intervene by scaffolding conflict resolution, but since they are often made aware only when team conflict becomes unmanageable, proactive strategies are needed. Several strategies were implemented in a new third-year course to enhance students’ self-awareness and empathy for others when working in teams. These included personality and conflict style exercises, the generation of an approachability statement, and the reflective monitoring of team dynamics using ITP metrics’ assessments during the term [2]. Surveys gauged student satisfaction with teamwork, the frequency of team conflict, and preparedness for resolving conflicts. Overall, students felt better prepared to handle future conflict as a result of the course. However, additional accountability measures may enhance the perceived value of the interventions used.

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)0.001
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 score0.242
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.219
Teacher spread0.208 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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