When the Life Lesson is More Important than Course Content
Bibliographic record
Abstract
Abstract When the Life Lesson is More Important than Course ContentTeaching ethics in engineering and engineering technology programs has been a growing topicfor many years and has been a subject of numerous papers.1-5 It is incumbent on the faculty toteach ethics as part of the profession and because it is a topic which is evaluated by everyone:including, ABET. As with all courses, teaching ethics is different from learning andinternalizing ethics.In every program there are times when students fail in their moral responsibilities and succumbto the easy way out. The overwhelming response to such events is assigning a failing grade andmaking the students repeat the course. What may not happen is remediation of the moral issueleading to a more ethical person. Of course, what we want as faculty and engineers is a graduatewho has the ethical underpinning that will honor the responsibilities that are listed in the NSPECode of Ethics for Engineers.6 The six canons are what we should strive to inculcate in ourstudents. Taking the course over, getting a new grade may do this but are there other ways?This paper is about a real incident and the method of resolving the ethical/moral situation infavor of the course content. It is about learning what is right by stressing what is wrong and howa practicing engineer or an engineering system could stray ethically resulting in violation of thefirst canon: Hold paramount the safety, health, and welfare of the public. Those involved at thefaculty level took a chance with this resolution method and the students responded well to theprocess. The details of the incident are intentionally sketchy – the resolution procedure and thelearning are highlighted.References:1. Houston, Brian, “Ethics A Tough Choice,” Proceeding of the 2006 Annual ASEEConference and Exposition, Chicago, Il. June 20062. Alenskis, Brian, “Integrating Ethics into an Engineering Technology Course: AnInterspersed Component Approach,” Proceeding of the 1997 Annual ASEE Conference andExposition, Milwaukee, WI. June 19973. Mindek, R. B., Keyser, T. K., Musiak, R. E., Schreiner, S., Vollaro, M.B., “Integration ofEngineering Ethics Into The Curriculum: Student Performance and Feedback,” Proceeding of the2003 Annual ASEE Conference and Exposition, Nashville, TN. June 20034. Durfee, J., Loendorf, W., “Using the National Society of Professional Engineer’ (NSPE)Ethics Examination as an Assessment Tool in the Engineering Technology Curriculum,”Proceeding of the 2008 Annual ASEE Conference and Exposition, Pittsburgh, PA. June 20085. Puri, I., Culver, S., Lohani, V., “Engagement with Ethics in a Large EngineeringProgram, A Status Report,” Proceeding of the 2010 Annual ASEE Conference and Exposition,Vancouver, BC. June 20106. “NSPE Code of Ethics for Engineers,”www.nspe.org/Ethics/CodeofEthics/index.html
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.082 | 0.017 |
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".