Evidence of Sociotechnical Thinking in Engineering Students
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".