Developing Inclusive Leadership Training for Undergraduate Engineering Teaching Assistants
Bibliographic record
Abstract
This complete experience-based practice paper describes the ongoing development of diversity, equity, and inclusion (DEI) training for undergraduate engineering teaching assistants in a firstyear, team project-based design course. At a large private university, undergraduate teaching assistants play a key role in first-year student success and the mentorship of their cornerstone design project. As the first points of reference for students, they assist with content delivery, guide students through hands-on labs and projects, and deliver regular feedback on assignments. Effective teaching assistants are leaders, thus their training as educators is essential to our firstyear students' success. To support this endeavor, peer-facilitated training on course content, technical skills, and best teaching practices is provided every semester to the undergraduate teaching assistant community. The training is grounded in global inclusion, diversity, belonging, equity, and access (GIDBEA) to foster a sense of belonging among the community of teaching assistants, students, and faculty. To this effort, we are piloting a series of workshops on inclusive leadership to be delivered every semester.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".