Transforming the Client Relationship to Support Large Capstone Classes
Bibliographic record
Abstract
Ahstract–This innovative practice full paper describes a case study from a software engineering capstone project course. Undergraduate programs often have a final-year capstone course designed to integrate and apply the knowledge and skills students have previously acquired while adapting to industry-standard practices. Capstones play a critical role in bridging the gap between academia and the industry as students transition to the workforce. Over the past twelve years, our institution has adopted a client-based model where industry clients work closely with a single team to solve a real-world problem. However, rising enrollment has put a strain on running this model effectively because of difficulties in recruiting clients, managing numerous client relationships simultaneously, and keeping client-student interactions sustainable. To tackle these challenges, we propose a new client model where clients pitch their ideas as themes in a competition and act as panel judges in evaluating student team submissions. We call this the hackathon client model and evaluate it in a class with 22 teams and 104 students. Through a thematic analysis of the qualitative responses gathered from this study, our findings suggest this new model provides a scalable alternative to operating a large capstone class while preserving many of the benefits of the traditional client model. However, both students and clients indicate having more means of communication would improve the project requirements phase and strengthen their relationship. We discuss ideas on improving the hackathon client model and plans for future experimentation in large capstones.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".