Data Sustainability in Manufacturing Engineering Education
Bibliographic record
Abstract
The value of data in manufacturing is immense, with the proper curation of data resources becoming a necessary skill closely aligned with principles of sustainability, where data infrastructure must be designed and deployed with longevity in mind to realize ongoing growth in manufacturing and related industries. This article summarizes an exploratory investigation into instructor perceptions of data-related learning outcomes through curricular analysis in a manufacturing engineering program. Data sustainability is considered in the context of data lakes, where data are stored enriched with metadata with longevity in mind and made available to all who may need them. Conversely, data swamps, where data are difficult to place in context or to retrieve by those who would benefit from their use, result from unsustainable data practises. Appropriate data learning outcomes were developed through literature review and stakeholder engagement. A qualitative study then asked instructors in a manufacturing engineering program to map course learning outcomes against data learning outcomes at three levels of development. Follow up interviews with instructors provided insight into how data themes are perceived at high levels in the program. Initial surveying indicated that intended course outcomes aligned appropriately with the data outcomes in an increasingly complex way over time, yet the interviews highlighted some discrepancies between survey results and perspectives later revealed by course instructors. Opportunities to emphasize sustainability and long-term utility of data archives (data lakes) are discussed, stressing the need for further work.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".