DISTRIBUTED TEACHING COLLABORATIVES FOR AI AND ROBOTICS



A Distributed Teaching Collaborative (DTC) is a network of institutions that agree to collaborate in offering courses through a distributed classroom model. In a distributed classroom, each participating institution connects virtually to the same virtual classroom each from a physical classroom with a group of students and their institution’s instructor. The platform allows for different instructors to take the lead in different topics, and students across institutions to work in groups for discussions and project work. Each student gets course credit in their home institution and program, and each instructor gets teaching credits. Instructors work together to support student projects and learning experiences, but each instructor individually grades and assesses the students from their own institution. We believe that a distributed classroom is a viable approach to build bridges between knowledgeable experts at MSI and R1 institutions to deliver a unique classroom experience where diverse perspectives enrich student experiences and collaborative skills are learned through practice.

A primary goal of this symposium is to understand best practices when developing a distributed classroom, and to facilitate the creation of new teaching collaboratives. To this end, we plan several keynotes as well as talks by speakers with extensive experience educating students through the distributed teaching model. We plan to develop a set of best practices for building distributed courses which would ease the ability for universities to begin teaching their courses in a distributed manner as well as provide a sense of what courses are best candidates for distributed classrooms.

Another goal of this symposium is to bring researchers, instructors, and students from R1 universities and HBCUs together to discuss unique challenges and opportunities in developing a distributed teaching collaborative that is equitable and representative of participating universities. Rather than a top-down model where R1 institutions dictate how programs are designed for HBCUs, this symposium takes a very different approach to ensure that the discussions include all institutions who stand to benefit from it.

The symposium will facilitate community building and discussion through several keynote presentations, lightning talks, and panel discussions by thought leaders across AI, robotics, and education at both R1 institutions and HBCUs. The first day will focus on the general idea of a distributed classroom and why it is important at HBCUs. This will involve talks and a panel on existing distributed teaching collaboratives as well as a panel on teaching challenges at HBCUs. The goal of the first day is to set the tone and expectation of the symposium as well as learn more about the challenges HBCU faculty face teaching new courses. The second day will build on the first day to focus on the curriculum and the student experience. This schedule will include lightning talks, a keynote and panels focused on how best to teach AI courses and the implication of equitable curriculums on the distributive classroom. In addition, we will host a panel discussion with students to get a better sense of the student experience and how they learn and engage with AI curriculum in the classroom.


Schedule(tentative)
Submissions

Submit an abstract (at most one page) describing research related to any of these questions to the Symposium EasyChair site.


Organizing Committee

Emmanuel Johnson, University of Southern California, ejohnson@isi.edu
Jana Pavlasek, University of Michigan, pavlasek@umich.edu
Odest Chadwicke Jenkins, University of Michigan, ocj@umich.edu
Yolanda Gil, University of Southern California, gil@isi.edu


Location

Westin Arlington Gateway, Arlington, VA


Important Dates

Abstracts/Papers due: August 31,2022

Author Notification: September 9th, 2022

Symposium: November 17-19,2022