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Record W4391603789 · doi:10.18260/1-2--43740

Instructor Goals and Practices Related to Sociotechnical Thinking in the Teaching of Undergraduate Engineering Students

2024· article· en· W4391603789 on OpenAlexaff
Lisa Romkey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSociotechnical systemEngineering ethicsMathematics educationComputer scienceEngineering managementKnowledge managementMedical educationPsychologyEngineeringMedicine

Abstract

fetched live from OpenAlex

As a global society, we face significant challenges, including environmental degradation and climate change, increasing economic inequity, rapid urbanization and population growth, the exclusion of individuals and groups from different forms of social engagement, and concerns with privacy and security. Given the omnipresent nature of technology and its influence on our lives, engineers must consider the ethical, environmental and sociological impacts of their work, and some engineering programs are considering new pedagogical methods and broader frameworks to engage students in macroethics, sociotechnical thinking and engineering for social justice. Using a particular perspective on sociotechnical thinking (STSE), the goal of this research was to explore sociotechnical thinking within engineering instructor teaching goals and practices. The study also sought to identify the challenges and enabling factors that engineering instructors experience in utilizing teaching practices related to sociotechnical thinking. STSE was selected given both its inherent flexibility, and its specific features that allow for some natural connections with engineering. Using STSE also allowed for the introduction of a framework from a different context to assess its utility and relevance to the engineering landscape.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.532
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.304
Teacher spread0.295 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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