Leveraging Multimodal Classroom Data for Teacher Reflection: Teachers’ Preferences, Practices, and Privacy Considerations

Teacher reflection is essential for K-12 classrooms, including effective and personalized instruction. Multimodal Learning Analytics (MMLA), integrating data from digital and physical learning environments, could support teacher reflection. Classroom data collected from sensors and TEL environments are needed to produce such analytics. These novel data collection methods pose an open challenge of how MMLA research practices can ensure alignment with teachers’ needs and concerns. This study explores K-12 teachers’ perceptions and preferences regarding MMLA analytics and data sharing. Through a mixed-method survey, we explore teachers’ (N = 100) preferences for analytics that help them reflect on their teaching practices, their favored data collection modalities, and data-sharing preferences. Results indicate that teachers were most interested in student learning analytics and their interactions and ways of motivating students. However, they were also significantly less accepting of collecting students’ audio and position data compared to such data about themselves. Finally, teachers were less willing to share data about themselves than their students. Our findings contribute ethical, practical, and pedagogical considerations of MMLA analytics for teacher reflection, informing the research practices and development of MMLA within TEL.