ACADEMIC IMPACT AND PERSONAL EXPERIENCE OF DESIGN TEACHING ASSISTANTS IN UNDERGRADUATE COURSES
Bibliographic record
Abstract
This paper presents the impact that theDesign Engineering and Design (DE&I) program offeredin 2013 has had in the Faculty of Engineering at theUniversity of Victoria. Through this program a pool ofnineteen graduate students were trained as DesignTeaching Assistants (DTAs). The purpose of this programis to train DTAs in engineering design principles andpedagogical skills for mentoring students working ondesign projects. During the year DTAs continued theirtraining by attending seminars presented by guestspeakers. To date, eight DTAs have been appointed toeither assist as qualified Teaching Assistant in alreadyestablished engineering design courses (two DTAs), or todevelop new design projects in courses that are primarilyengineering science (six DTAs). The latter was supportedby the course instructor and the coordinators of thisprogram. The paper describes the development andmanagement of these design projects, their impact onundergraduate students, and the personal experiencegained by the DTAs. Also, the paper presents a review ofthe 2013 DE&I program including a new strategy for theupcoming 2014 DE&I workshops that will focus more onthe development and execution of design projects of theDTAs.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".