NOTE: this event will start at 12:00 rather than our usual 11:00.

Abstract:

In K-12  (Kindergarten- Grade 12) computer science classrooms there are many issues of equity. For example, In the U.S. there are many initiatives that seek to close the gap in terms of achievement in CS based on gender because women are not reaching the same level of success as men. To prepare teachers to face similar challenges we explore simulation-based learning as a form of practice based pedagogy providing pre-service teachers with opportunities to learn from their own improvisational response to equity dilemmas. We explore teacher education as there are far fewer teacher preparation programs than there are K-12 schools providing a smaller target population to shift K-12 CS education in an equitable direction. In many of the simulations we use there are no correct answers. At best we can hope for alignment between pre-service teacher behaviors and principles of equity. The key challenge we explore in this presentation is: how can we provide rich technical learning environments to support learning when there are no right answers? To explore how to build dynamic supports within simulation-based learning we lean on findings from learning science in contexts where there are correct answers such as intelligent tutoring systems. Intelligent tutoring systems frequently teach procedural knowledge where there are both correct answers and common misconceptions. Frequently, intelligent tutoring system researchers identify when users are confused and triggers dynamic supports. We explore if there is utility in the identification of confusion in audio recorded responses from simulation based learning on issues of equity – where there are no correct answers. The first and critical step toward exploring the role of confusion in simulation-based learning is establishing a valid measure of confusion. In this presentation we will illustrate how we use the Constructed Theory of Emotion, which defines emotions are a consensus within a social context, to establish a valid measurement strategy for confusion. We reference models from cognitive psychology frequently used in Intelligent Tutoring System to validate the approach of modeling confusion based on retrospective labeling from participants listening to their own audio recordings. Finally, we unpack what participants look for when listening for confusion in their own audio recorded responses to examine from a theoretical perspective how the detection of confusion can support pre-service teachers in simulation-based learning.

Speaker Bio:

Dr. Garron Hillaire is a post-doctoral researcher in Learning Engineering at the Teaching Systems Lab at the Massachusetts Institute of Technology where he investigates how to broaden participation in K-12 computer science education through innovative teacher education using simulations. His work focuses on building and integrating innovative emotional measures, AI architecture, and simulation based learning environments for teacher education. He has spent the last two years building a community of practice for simulation-based teacher education with 22 professors of education across the U.S.  As an educational technology researcher, Garron focuses on issues of equity related to Digital Decolonization and Universal Design for Learning.