Reflections on Multi-campus Teaching in a New Manufacturing Engineering Program
Bibliographic record
Abstract
In 2019, the University of British Columbia (UBC) initiated a new multi-campus manufacturing engineering program involving two campuses situated over 450 km apart.Each institution is responsible for managing its own curriculum and specialization within manufacturing engineering, with some courses being taught in a multi-campus instructional (MCI) format.Although well established in some areas, managing and delivering a new program in a multi-campus format presents several challenges, exacerbated by COVID-19, administrative hurdles, cultural differences between campuses, and institutional context including lab equipment.Two case studies representing two courses in the manufacturing engineering curriculum are examined with an emphasis placed on challenges encountered, adaptation to a changing teaching environment, and student experience of teaching and learning.The course instructors are interviewed with narratives examined through an interpretivist paradigm using inductive thematic analysis to explore themes, challenges, and the instructor's experience teaching MCI.Reflections on emerging themes and their connection to manufacturing engineering and Education 4.0 are discussed, with both opportunities and challenges for continuing program growth elucidated.Finally, understanding that multi-campus education is of growing interest to the community, some recommendations and best practices are proposed.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".