MétaCan
Menu
Back to cohort
Record W4308709549 · doi:10.24908/pceea.vi.15977

Evidence of Sociotechnical Thinking in Engineering Students

2022· article· en· W4308709549 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSociotechnical systemThematic analysisEngineering educationTechnical writingVocational educationClass (philosophy)Qualitative researchQualitative analysisMathematics educationEngineering ethicsComputer scienceEngineeringPsychologyPedagogyHigher educationKnowledge managementSociologyEngineering managementArtificial intelligenceSocial science

Abstract

fetched live from OpenAlex

The ability to acknowledge and respond to the combination of the social and technical aspects of structures and processes encompassed in engineering design is called Sociotechnical Thinking (STT). Integrating STT into engineering education is important, as considering sociotechnical aspects can help students develop more thorough understandings of engineering practice and create more well-rounded and inclusive designs. While numerous attempts have been made to promote STT in undergraduate engineering courses, researchers and instructors characterize STT in different ways. The purpose of this qualitative content analysis was to inductively develop a framework for deductively analysing students’ capacities for STT. An inductive thematic analysis of the research literature was conducted to identify themes of STT in engineering education. Using these themes, a framework for deductive analysis was created. The framework was then used to assess publicly available undergraduate engineering reports written for a second-year technical communication class. All six themes in the STT framework were identified in the reports, though the themes occurred with varying frequency and at varying degrees. Students showed evidence of dualistic, or “instrumental” thinking. This work is a pilot phase of a larger research study that aims to develop a theoretical background for STT, which will explain its characteristics, elements, and thinking processes for use in the teaching and assessment of engineering education.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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