How We Teach: Capstone Design
Bibliographic record
Abstract
Abstract Capstone design was the topic of the 2022 survey by AIChE's Education Division's Curriculum Survey Committee. Sixty responses have been received so far and preliminary results are reported here. Data collection will resume after the AIChE Annual Meeting. Institutions are more likely to have a two- or three-course capstone design series than a single capstone course (68% to 27%) with a mean of 5.4 credit hours. More institutions have design projects throughout the curriculum (40%) than have design courses throughout the program (20%). The course or courses in the capstone design are primarily offered only once a year (78%) with a slight edge to the spring semester/winter quarter (50%) over the fall semester/quarter (42%). Most institutions (78%) include instruction in software or programming as part of the course(s). The culminating design project is most often a theoretical design (68%) as opposed to one based on experiments (3%) or resulting in a prototype (7%), and most institutions do not use the AIChE Design Competition problems (70%). Professional skills are mainly lightly covered with the exception of professional communication being covered in depth at 58% of institutions. At most institutions, capstone design is used to assess the extent of ABET outcome achievement for all seven outcomes except #6 (develop and conduct appropriate experimentation…). Students typically complete only one culminating major design (67%) in a team of 4.0 (mean) students. The mean of unique projects completed by the cohort is 8.6. Students receive formal feedback on their progress weekly at 50% of the institutions and every other week at 23% of institutions. The mode for weekly student time spent on projects is 6 – 10 hours per week, 62% of institutions. The mean size of a faculty team teaching capstone design is 2.8 faculty. Fifty-eight percent of the instructors have at least three years of industrial experience, and instructors are split evenly between teaching-track and tenure-track. At least one capstone design instructor is a licensed professional engineer at 43% of the responding institutions. The faculty collaborate with industry at 63% of responding institutions. The conference paper and presentation will include many other details such as how teams are created, deliverables, software used, project topics, and the role of industrial collaborators. Student contact hours both inside and outside the classroom will be described. Comparisons will be made to earlier surveys.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.017 |
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".