Exploring the Potential of Automated and Personalized Feedback to Support Science Teacher Learning

Jamie N. Mikeska, Beata Beigman Klebanov, Alessia Marigo, Jessica Tierney, Tricia Maxwell, Tanya Nazaretsky

Abstract: This study investigates how automated and personalized feedback can support elementary teachers’ learning within digital teaching simulations. We interviewed 15 participants to elicit their understanding and evaluation of feedback about a teacher’s facilitation of a science discussion, which was generated using natural language processing (NLP). Findings indicate that participants: (a) perceived strong alignment between the feedback and the discussion facilitated, (b) used the feedback to identify future instructional moves, and (c) noted how the feedback can support reflection.