Reimagining Industrial Engineering: Embedding Sustainability and Societal Impact in Course Design
Bibliographic record
Abstract
This work presents ongoing efforts to embed sustainability and societal impact into the Industrial Engineering (IE) program at Quinnipiac University (QU).QU's strategic emphasis on sustainability aligns with its commitment to community engagement and industry partnerships, providing fertile ground for the integration of sustainability principles into academic programs.In the Fall 2023 semester, the Lean Systems Engineering course was restructured to emphasize sustainability, integrating Lean Green examples and sustainability components throughout the curriculum.Guest speakers, including experts in Lean Green initiatives, enriched class discussions, providing real-world insights.Class activities, such as presentations and discussions, further reinforced the connection between Lean principles and sustainability goals.Additionally, a final project with a biofuel company offered students hands-on experience in streamlining sustainable processes.Feedback from both students and the industry partner was positive, affirming the effectiveness of integrating sustainability into the curriculum.Student responses indicated a significant shift in their perception of the role of engineers in addressing environmental challenges.Statistical analysis revealed a meaningful impact on student attitudes towards sustainability.Looking ahead, the author is spearheading initiatives to build on this momentum, including the formation of a sustainability consortium and pursuing grant opportunities to support sustainability-focused projects.The evolution of the IE curriculum at QU reflects a commitment to producing engineers equipped to address the complex challenges of a sustainable future.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".