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Record W4308713783 · doi:10.24908/pceea.vi.15973

First-year Undergraduate Engineering Student Beliefs About Teamwork: A Qualitative Analysis.

2022· article· en· W4308713783 on OpenAlexafffundvenue
In‐Ho Kim, Patricia Sheridan

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsTeamworkValue (mathematics)PsychologyQualitative researchWork (physics)AccountabilityQualitative analysisMathematics educationMedical educationPedagogySociologyEngineeringComputer scienceManagementMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

This paper discusses a comparative study of first-year engineering students’ beliefs around design-based teamwork. In this study, survey data was collected from first-year students at a large research-based university in 2014 and compared to those discussed in the present literature. Using qualitative analysis, these descriptive textual responses were used to identify themes that represented specific beliefs. These themes discussed individual accountability, seeing teamwork as a collection of individual work, challenges around team member motivation, and intra-team communication challenges. When compared to the literature, themes regarding teamwork as inefficient due to experiences around unequal divisions of work and perceived skill disparity emerged as reflective of the literature. An underlying value of optimization emerged in our analysis. Tapping into this core value could enable students to develop more effective teamworking strategies.

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)0.013
metaresearch head score (Gemma)0.023
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.023
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.008
GPT teacher head0.256
Teacher spread0.248 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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