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We present Sensei, the first system designed to understand social interaction and learning in an early-childhood classroom using a distributed sensor network. Our unobtrusive sensors measure proximity between each node in a dynamic range-based mesh network. The sensors can be worn in the shoes, attached to selected landmarks in the classroom, and placed on Montessori materials. This data, accessible to teachers in a web dashboard, enables teachers to derive deeper insights from their classrooms. Sensei is currently deployed in three Montessori schools and we have evaluated the effectiveness of the system with teachers. Our user studies have shown that the system enhances teachers' capabilities and helps discover insights that would have otherwise been lost. From our evaluation interviews, we have established three major use cases of the system. Sensei augments teachers' manual observations, helps them plan individualized curriculum for each student, and identifies their needs for more interaction with some children. Further, the anonymized data can be used in large-scale research in early childhood development.
Saquib et al. (Mon,) studied this question.