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.We seek to build our teaching assistants' sense of agency in the classroom by cultivating a positive self-concept, developing their understanding of sociopolitical environments, and providing resources for action.Co-created with faculty, teaching assistants, and DEI experts at the institution, the workshop series provides teaching assistants with the ability to recognize and confront bias among individuals and within teams, helps them develop an understanding of power, privilege, and oppression, and equips them with the tools to employ their knowledge professionally.The workshops feature individual reflection activities and small group discussions, culminating in a community-wide discussion on lessons learned and actionable items to build an inclusive community within our first-year program.To understand the value of this training for the undergraduate teaching assistants, a survey was conducted of participants before and after participation in the workshops.The survey aims to capture the practicality of the training and the teaching assistants' assessment of the climate within the first-year engineering experience.In this paper, findings from the second year of piloting our workshops are described.In this second iteration of training, new teaching assistants participated in our foundational training in GIDBEA, and returning ones built on their introductory knowledge to learn about social justice and principles of inclusive leadership.The data shows that most of the teaching assistants found the workshop content and activities relevant to them as peer educators.Several teaching assistants shared inclusive leadership strategies that they planned to implement in the coming semester.The goal of this study is to inform plans for implementing solutions into training that address deficiencies identified through the survey and provide a set of inclusion best practices and learning objectives for inclusivity training for undergraduate teaching assistants.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".