An Example of Using Collaborative Online International Learning for Petroleum and Chemical Engineering Undergraduate Courses
Bibliographic record
Abstract
This work describes an international collaboration experience carried out between our process design and petroleum property evaluation courses. This collaboration was developed as part of a partnership between the American University of Ras Al Khaimah (AURAK) in Ras Al Khaimah, United Arab Emirates, and Wayne State University in Detroit, Michigan, United States of America, using a program called Collaborative Online International Learning (COIL) from the State University of New York (SUNY) system. The COIL program provides training to faculty on engaging students in international and cross-disciplinary projects to promote skills needed in the current work force. The COIL training meetings facilitated pairing faculty of different disciplines in different parts of the world and provided a structure for developing a collaborative project. The collaborative project that we undertook in fall 2021, and which we detail throughout this article, was titled Upstream and Downstream of Petroleum Economics.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".