Sensei (Sensing Educational Interaction) is a dynamic range based distributed sensor network that aid the observation process in early childhood Montessori classrooms. The system is currently deployed in Wildflower Schools. In a busy classroom, teachers hardly have time to observe each and every child to learn about their needs. Sensei helps teachers make sense of their classrooms.
Unobtrusive sensors provide a way to tap into classroom interactions data that can be interpreted with machine learning algorithms to provide insights to teachers that would otherwise have been lost from a busy classroom. The observation tools create a novel human-machine interface to empower teachers and students in personalizing the curriculum.
Proximity sensing radio sensors are embedded in children’s shoes, learning materials, and selected landmarks in the classrooms. By logging proximity data, we can reconstruct the daily social network, teacher-student time distribution, and learning time. Based on this data, we provide unique insights to each teacher about their teaching style and the time they spend with each child. We can also understand the kind of lessons a child is interested in. A visualization dashboard lets a teacher explore such data and compare with his/her own intuition about the classroom.
This novel way to understand classroom dynamics has already helped teachers make better sense of what each child needs, as evident from our user interviews.
Nazmus Saquib, Ayesha Bose, Dwyane George, Sepandar Kamvar. Sensei: Sensing Educational Interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). Ubicomp 2018. (pdf)