Capstone Project Evaluation – Towards a Student-Centred Approach
Bibliographic record
Abstract
Capstone projects present a particular challenge to assessment as compared to individual course work. Projects unfold over a year, culminating in a final report, begging the question of (balancing the) assessment of process over product. Projects across an individual department, let alone across the engineering faculty, can differ in content, the balance of depth versus breadth, the balance of research versus application, the balance of design versus implementation, and size of teams. One approach to managing this diversity is the use of broadly interpreted categories and no explicit weighted marking scheme, with the final grade determined by consensus in a department-wide meeting, held privately from the students. While flexibility and the authority of the supervising faculty member is acknowledged and maintained in this approach, the needs of the students are not necessarily best served in this approach. The movement toward learning outcomes, including the CEAB Graduate Attribute Criteria, is providing a well-understood and documented language for established indicators. This paper presents the results of an effort this year to incorporate CEAB graduate attributes into a system of marking rubrics. The goal is to better serve the needs of students with an assessment strategy that is based on explicit expectations and transparency, one which includes all deliverables, and yet still accommodates for diversity in project experiences. The paper will present the compound assessment instrument developed and used on select project teams, as well as the feedback of the students involved in the experience. The work done this year is seen as preliminary and the intent is to invite feedback to move towards broaden the adoption of the assessment instruments within our community.
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 | 0.317 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".