The Assessment of a Gradeless Residency Model for Software Engineering Education
Bibliographic record
Abstract
The traditional practice of assessing academic performance through conventional letter and numerical grades has faced criticism for its limitations in promoting active engagement and curiosity among students. In response, the concept of gradeless education has gained traction, with the aim of fostering a more holistic learning experience. This work explores the implementation of the residency model, a form of gradeless education, in the context of engineering education. The model focuses on skill acquisition and competency demonstration while enhancing student wellness by minimizing assessment-related anxiety that students often face in graded assessments. This study evaluates the effectiveness of the residency model through a comprehensive survey conducted in a software engineering technology program at McMaster University. The survey investigates student perspectives on the model’s impact on motivation, learning experience, and attitudes towards learning. The results reveal a complex interplay of attitudes, with students acknowledging the importance of grades while appreciating the model’s rigorous assignments. The findings suggest that the residency model can encourage transformative learning experiences while warranting ongoing attention to optimize both learning outcomes and student well-being. Further research is recommended to assess the long-term impact and effectiveness of gradeless education models, emphasizing both their benefits and challenges.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".