EVALUATION METHODS FOR FINAL YEAR PRACTICE ORIENTED DESIGN COURSES
Bibliographic record
Abstract
The curriculum debate is alive and well amongst engineering faculties across the country. Even as the Canadian Engineering Accreditation Board continues to move towards fostering more design in the curriculum the question remains as to what impacts this is having on creating practice ready engineering graduates. The term practice ready is not to imply they are ready to be registered as professionals in their jurisdiction, but is intended to imply they have enough understanding of the realities of working in the private and public sector to appreciate the many conflicting objectives and limitations organizations face when making decisions that involve a large component of engineering involvement. This paper presents a discussion of using case histories in a final year Civil Engineering course to introduce elements of institutional policy, business management and engineering practice through demonstration with case histories where decisions involved all these elements. The case histories involved discussion of legal claims and dispute resolution on projects where engineering and business considerations were weighed in concert to achieve final solutions. The class was broken into working teams to discuss the case histories at various stages where information to that point was presented. The groups reported on their position and what decisions they would take at each stage so they could appreciate how their position changed as new information became available. This paper will focus on how the curriculum material was evaluated in the context of the examinations. Given the focus around case histories, the evaluation process was difficult given any questions about the case histories covered was simply a recalling knowledge already gained. At the same time the skills to be evaluated were highly subjective and difficult to articulate in answers to specific one-off questions. As such a case study was used in the examination process with considerable success. This paper discusses the development of an examination, conducting the examination and marking the examination of a case study based problem.
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 | 0.135 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".