Developing Inclusive Leadership Training for Undergraduate Engineering Teaching Assistants
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».