2026-09-18T23:56:47Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1075532024-03-26T03:26:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sepandar Kamvar.
Saquib, Nazmus
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2017-03-20T19:39:43Z
2017-03-20T19:39:43Z
2016
2016
http://hdl.handle.net/1721.1/107553
974639235
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 63-65).
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 lessons. This data, accessible to teachers in a web dashboard, enables teachers to derive deeper insights from their classrooms. Further, the anonymized data can be used in large-scale research in early childhood. Sensei is currently deployed in three Montessori schools and we have evaluated the effectiveness of the system with teachers. Our user evaluations have shown that Sensei helps discover insights that would have otherwise been lost.
by Nazmus Saquib.
S.M.
68 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Program in Media Arts and Sciences ()
Sensei : sensing educational interaction
Sensing educational interaction
Thesis
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<Abstract>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 lessons. This data, accessible to teachers in a web dashboard, enables teachers to derive deeper insights from their classrooms. Further, the anonymized data can be used in large-scale research in early childhood. Sensei is currently deployed in three Montessori schools and we have evaluated the effectiveness of the system with teachers. Our user evaluations have shown that Sensei helps discover insights that would have otherwise been lost.</Abstract>
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