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Record W2724514459 · doi:10.24908/pceea.v0i0.10197

Measuring Students’ Motivation to Engage in Sustainable Engineering Practice

2018· article· en· W2724514459 on OpenAlexafffundvenue
Natasha Lanziner, David S. Strong

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIdentification (biology)Sustainable developmentReliability (semiconductor)Task (project management)Measure (data warehouse)Value (mathematics)ValidityOrder (exchange)PsychologyKey (lock)Knowledge managementComputer scienceEngineeringPolitical scienceBusinessPsychometricsSystems engineering

Abstract

fetched live from OpenAlex

Abstract – Worldwide, political and professional organizations consider engineering to be a key profession in the application of sustainable development to solve global problems. In order for engineering professionals to play a key role in sustainable development, they must be motivated to engage in such practice. The purpose of this study is to develop a measure of students’ motivation to engage in sustainable engineering practice. A survey instrument was developed by applying a mixed-method approach consisting of a survey instrument design phase, small pilot study, and national study.
 The proposed survey instrument includes 3 openended and 40 closed-ended questions to measure previous experiences and stereotypes, self-concept of abilities, and subjective task value with respect to sustainable engineering practice. Factor analyses of the closed-ended questions resulted in the identification of 7 factors, 5 of which can be considered to be strong factors. Evidence for validity and reliability is established through the pilot study and factor analyses.

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)0.004
metaresearch head score (Gemma)0.042
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.281
Teacher spread0.267 · 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.

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

Citations5
Published2018
Admission routes3
Has abstractyes

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