"REAL" ENGINEERING FOR A MULTI-DISCIPLINARY 1ST YEAR PROJECT/DESIGN COURSE APSC100
Bibliographic record
Abstract
Queen’s University has a common first year for engineering. A few years ago a project/design course was introduced, APSC100, which has been quite successful. It was predicated on the fact that students who are attracted to engineering are really excited about the possibility of “doing engineering” early in their student experience. The design of Chemical processes is something that few if any first year students have any appreciation of. We are in the process of developing a project for APSC100 which will introduce the subject as well as have the students work with a commercial FlowSheet simulator. We believe that simulators “warts and all (1)” can be an excellent learning tool, and exposure to these programs is essential as they have become so much a part of today’s engineering career experience. Commercial Flowsheet simulators continue to be a challenge for people with many years familiarity with these systems. There is however a potential for a great deal of learning about the “design process” by the use of these tools, provided these tools are presented in such a way as to be challenging but not intimidating. The paper will describe the approach to developing the APSC100 module, the challenges faced and the anticipated solutions. One particular problem will be developing something that will interest a broad spectrum of students. It has been noted that “Chemistry” often seems to be a subject that is avoided by many. This module will hopefully demonstrate the fact that “Chemistry” is one of the basic sciences and how it is the basis for much of the product of the modern world. Since this is a “work-in-progress” we anticipate and welcome suggestions as to how to present a successful module to our students.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.229 | 0.122 |
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".