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Record W2885949532 · doi:10.24908/pceea.v0i0.10191

Working with “Others”: Developing Sustainability Skills in the First Year Engineering Classroom

2018· article· en· W2885949532 on OpenAlexafffundvenue
Susan Nesbit, Naoko Ellis, Pete Ostafichuk

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsCurriculumEngineering educationArgument (complex analysis)Engineering ethicsSustainabilityMetacognitionWork (physics)Test (biology)EngineeringPsychologyMathematics educationPedagogyEngineering managementMechanical engineeringMedicineCognition

Abstract

fetched live from OpenAlex

Abstract While engineering education excels at training students to solve well-defined and highly structured problems, it struggles to support the development of students’ abilities to address highly complex, ill-structured, and contested engineering problems that lack in definite solutions, where engineers are called on to work with non-engineers in a transdisciplinary environment. The challenge for engineering educators is to develop and teach constructively aligned curricula aimed at developing transdisciplinary skills so that, as practitioners, graduating engineering students contribute to addressing these types of problems within transdisciplinary environments.
 Efforts are underway in many institutions to close the gap between the transdisciplinary needs in practice and current engineering curricula. At the University of British Columbia (UBC), a team of faculty members and engineering practitioners have recently developed and are teaching a design-focused engineering course to all first year students. In this paper we, a subset of UBC’s teaching team, present the argument for teaching skills to engineering students that support transdisciplinary. Wesummarize the definitions of these skills found in the literature, and we speculate that the development of one aspect of transdisciplinary is related to personal development. Specifically, we hypothesize that systems thinking is correlated to metacognition. We describe an experimental strategy for testing the hypothesis within a first year engineering program, then we present and discuss preliminary test results.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.081
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.291
Teacher spread0.274 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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