Bibliographic record
Abstract
This research-to-practice full paper presents guiding principles for developing assessments based on our experience in teaching the software engineering capstone course over the last twelve years. Software engineering courses are central to computer science and engineering programs. To provide students with an authentic learning experience, teams of students work on realistic projects that help them apply theoretical concepts to develop practical skills. Challenges arise with increasing class sizes and limited teaching resources. Despite these constraints, educators must intentionally design assessments that align with learning theories that promote deeper learning opportunities and support lifelong learning. In this work, we report on our experience designing and adapting the assessments used in our software engineering capstone course over the past twelve years. We reflect on our approach by aligning the evaluation strategies to learning theories such as behaviorism, constructivism, and social constructivism. This research-to-practice paper discusses the practical implications behind different assessment strategies in the face of large class sizes and presents guiding principles for developing assessments for software engineering team projects.
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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 | 0.072 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".