Instructor Goals and Practices related to STSE (Science, Technology, Society and the Environment) in the Teaching of Undergraduate Engineering Students
Bibliographic record
Abstract
Given the role of science and technology in creating and mitigating our society’s biggest challenges, undergraduate engineering programs must build in opportunities for students to explore the complex relationship between science and technology, encourage citizenship and action on critical social issues, and consider the impact of engineering’s work on society and the environment– the hallmarks of STSE (Science, Technology, Society and the Environment) education. The goal of this research was to explore instructor teaching goals and practices, and more specifically their goals and practices related to the use of STSE perspectives in their own teaching of undergraduate engineering students. The study also sought to identify the challenges and enabling factors that engineering instructors experience in utilizing STSE-related practices. This study employed an online survey with 180 participants at four universities in Ontario, designed to provide an initial understanding and broad overview of STSE in the context of engineering education. Following the survey, 12 instructors who had completed the survey were interviewed, to help provide a deeper understanding of instructor goals and practices related to their teaching and teacher identity, and the relationship between their teaching goals and practices and an existing STSE theoretical framework. The study draws from the literature on K-12 STSE education, teaching in higher education, instructor views of teaching engineering, and STSE-related practices and philosophies in engineering education. The study found that engineering instructors employ a diversity of goals and practices in their teaching, and demonstrate a strong bend towards real-world applications. Instructors demonstrated moderate support for STSE, with a strong orientation towards problem solving and design, but shared concerns about exploring issues of social justice and fairness and the possibility of imposing bias on students. This is reflective of work in engineering education that highlights the historically apolitical nature of engineering and its resonance in undergraduate engineering programs. Finally, a reframing of STSE is offered to acknowledge the role of problem solving alongside a more holistic issues-based exploration in engineering, which aligns with recent work on sociotechnical thinking and social responsibility in engineering education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".