Enabling scalability, richer experiences and ABET-accreditable learning outcomes in computer science Capstone courses through inversion of control
Bibliographic record
Abstract
Capstone courses are expected to prepare students for the “real world” by putting them into a microcosm of the real world. In these courses, students are given a problem of some complexity, and are expected to exercise and develop problem-solving skills as they address the problem. Within our Computer Science and Engineering program we have, over the past eight years, successfully scaled up the Capstone courses. Doing so has required innovative thinking about the roles of the students, faculty, and project sponsors. In this paper, we discuss issues with scaling up the components that have made this program successful. These include housing the courses in an NSF IUCRC that enable the cultivation of highly-committed industry partners, the creation of strong pre-requisite courses, careful development of faculty resources through the selective hiring and mentoring of clinical faculty, a commitment of the faculty to give up close management and control, strong partnerships with other organizations within the university to provide students greater access to resources, an emphasis on cross-team knowledge sharing and learning, and the development of unique assessment and evaluation tools so as to be able to monitor, measure and fairly assess a wide-spectrum of projects.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".