Factors for Consideration in Capstone Design Assessment Schemes: A Systematic Review and Critical Reflection
Bibliographic record
Abstract
Grading schemes in engineering capstone design courses—whether numeric, letter-based, or pass/fail—may impact student learning, motivation, and stress. Given the summative nature of capstone courses and their influence on graduate studies and employment, understanding grading implications is essential. Despite ongoing discussions about grading, there is limited research on how different grading schemes affect motivation and learning outcomes in capstone courses. A systematic literature review produced over 1000 results, of which 35 were deemed relevant and further analyzed. The review found no studies comparing grading schemes in capstone courses or their effects on motivation, stress, or mark inflation. Six key themes are highlighted: grading scheme characteristics, quantitative scoring, lack of critical grading, project variance, instructor competency, and team project challenges. The research highlights gaps in assessment methods and suggests future study areas to assess grading practices in capstone courses, including instructor training, assessment structures, and grading’s role in student development.
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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 | 0.082 | 0.216 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".