Empowering teacher learning with AI: Automated evaluation of teacher attention to student Ideas during argumentation-focused discussions

Tanya Nazaretsky, Jamie Mikeska, Beata Beigman Klebanov

Abstract: Engaging students in argument from evidence is an essential goal of science education. This is a complex skill to develop; recent research in science education proposed the use of simulated classrooms to facilitate the practice of the skill. We use data from one such simulated environment to explore whether automated analysis of the transcripts of the teacher's interaction with the simulated students using Natural Language Processing techniques could yield an accurate evaluation of the teacher's performance. We are especially interested in explainable models that could also support formative feedback. The results are encouraging: Not only can the models score the transcript as well as humans can, but they can also provide justifications for the scores comparable to those provided by human raters.