Representational Alignment Supports Effective Teaching

Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya, Nori Jacoby, Weiyang Liu, Theodore Sumers, Michalis Korakakis, Umang Bhatt, Mark K Ho, Joshua B. Tenenbaum, Bradley C. Love, Zachary Pardos, Adrian Weller, Thomas L. Griffiths

Keywords: machine teaching, representational alignment

TL;DR: Effective machine teachers/tutors require not only domain expertise but also representation alignment with human students

Abstract: A good teacher should not only be knowledgeable, but should also be able to communicate in a way that the student understands -- to share the student's representation of the world. In this work, we introduce a new controlled experimental setting, GRADE, to study pedagogy and representational alignment. We use GRADE through a series of machine-machine and machine-human teaching experiments to characterize a utility curve defining a relationship between representational alignment, teacher expertise, and student learning outcomes. We find that improved representational alignment with a student improves student learning outcomes (i.e., task accuracy), but that this effect is moderated by the size and representational diversity of the class being taught. We use these insights to design a preliminary classroom matching procedure, GRADE-Match, that optimizes the assignment of students to teachers. When designing machine teachers, our results suggest that it is important to focus not only on accuracy, but also on representational alignment with human learners.

Submission Type: Short Paper (4 Pages)

Archival Option: This is a non-archival submission

Submission Number: 51