IMPACTS AND ETHICS OF BIOTECHNOLOGY AS A VEHICLE FOR ASSESSING CEAB OUTCOMES IN A FIRST YEAR CHEMICAL ENGINEERING COURSE AT THE UNIVERSITY OF WATERLOO
Bibliographic record
Abstract
The CEAB outcomes “Impact of Engineering on Society and the Environment”, and “Ethics and Equity” differ from the majority of the technical content of engineering curricula. These outcomes largely involve the affective learning domain rather than the cognitive domain. Teaching and measuring affective domain outcomes can be challenging and has received limited attention in engineering curriculum.To help address this disconnect, we have selected to integrate ethical considerations within an engineering course, namely the first year Engineering Biology course of the Chemical Engineering program at University of Waterloo.This learning activity was successful in providing an opportunity for students to establish a sense of awareness as to the impact of engineering on society and the environment. It also created opportunities for students to practice and develop their communication skills. Next steps should consider the potential need to formally expose students to the ethical decision making process and to evaluate if this type of teaching has modified the student awareness of ethics, social values and responsibilities of the engineer.In this paper, we will comment on the successes and challenges faced in promoting student learning and fairness in a structured panel format learning activity. The careful design of assessment tools will also be discussed.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".