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