Bibliographic record
Abstract
The purpose of capstone courses and projects in engineering is to provide a learning experience that effectively and reliably solidifies earlier acquired understandings. It provides a culminating exercise that lies just beyond a student’s existing ability so that learning is furthered while motivation is preserved. Historically, individual engineering projects, practicums, and internships have been heavily used to provide that culminating experience; however, with often disappointing results. This has been particularly the case in attempting to cap off programs in systems engineering where the learning ideal would be to have a student experience a real-world complex multi-disciplinary engineering and program environment. Given the limitation, this paper proposes using a term-long, class-based, repeatable meta-case as the capstone learning venue, particularly in support of systems engineering programs where securing meaningful experiential learning is difficult. Case teaching is a classic approach in law, medicine and business faculties where the need to develop higher cognitive abilities—analyzing, synthesizing and judging—inside high ambiguity and across multi-disciplines is paramount. A meta-case, as opposed to other case types, is characterized by the use of a very complex, multi-factor (engineering) real-world challenge with a long, multi-stage solution scenario. In proposing the use of a capstone meta-case, the paper presents its use in an aerospace systems engineering environment where development timelines are very long, and where the engineering requirements and solutions are many and highly interdependent. It specifically discusses the course design structure and considerations associated with a meta-case based on the development of the Airbus A400 military transport aircraft. The paper is based on a year-long study into the use of the case method for teaching aerospace systems engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".