Assessing Ethical Sensitivity Development in Undergraduate Engineering Students
Bibliographic record
Abstract
Canadian engineers are expected to uphold high ethical standards as part of their responsibility to the profession and society. This expectation is echoed in the Canadian Engineering Accreditation Board (CEAB) graduate attributes and in the Ritual of the Calling of an Engineer [1], [2]. Part of upholding high ethical standards as an engineer involves the essential skill of being able to detect and identify ethical issues. This refers to one’s Ethical Sensitivity (ES), which is often overlooked in Engineering Ethics Education (EEE) currently. EEE in North America primarily focuses on the action plans and justifications developed to address presented ethical dilemmas, not on how to identify ethical dilemmas. This then leads to the question, are students’ ES skills being developed over the course of their undergraduate career? While there is some existing research on ethical decision-making and the factors that influence it, there is markedly less research on ES, less on ES assessment, and even less on ES assessment in engineering students. Additionally, the majority of ES assessment tools currently used are either not designed to specifically assess ES, are not designed for engineering, and/or cue the participant in some way to the ethical dilemmas presented, which could misrepresent the participant’s actual ES abilities. This research will investigate current literature on ethical sensitivity and will also describe a research method to assess ES development in undergraduate engineering students. The focus of this paper will be on a pilot study currently in progress along with the next steps for this research. These results can provide insight to educators, ideally resulting in more effective teaching practices, and ultimately creating more ethically conscious engineering graduates.
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 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".