Bibliographic record
Abstract
The Faculty of Engineering at the University of Ottawa is home to multiple rapid prototyping facilities and entrepreneurship spaces. These include a makerspace, a machine shop and a design space for any student to use free of charge. First- and second-year students also take courses in the Makerlab, a sister facility to the Makerspace, that introduces them to collaborative project-based learning, engineering problem-solving and prototyping in a cornerstone design course. Each three-hour weekly lab has a teaching assistant (TA), typically a graduate student, and a project manager (PM), typically an undergraduate student who has taken the course previously. They are responsible for teaching the lab content and guiding the students through their design process. Since design courses are weighted heavily toward projects and labs, this evidence-based practice paper is part of a study that has the goal of understanding, via student evaluations, the impact of TA and PM training on their performance. This paper presents an analysis of the impact of TA and PM training, based on the students’ evaluation of their TA’s and PM’s performance. Factors considered were the amount of training received by the TAs and PMs, the type of training and the satisfaction of the students. The students were surveyed to gauge their satisfaction with the quality of their TAs and PMs, and the survey results were compared with a number of outcomes.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.023 |
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".