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Justin Reich. Ng, Kevin (Kevin Y.) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-12-18T19:47:33Z 2018-12-18T19:47:33Z 2018 2018 http://hdl.handle.net/1721.1/119729 1078687644 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 49-50). Existing state of the art practice-based teacher education models either rely on heavy teacher educator time commitment to process teacher candidate performance stored in rich media like audio or video, or rely on teacher candidates to voluntarily share experiences with minimal teacher educator interaction with data. Using an iterative design process, I work with teacher educators to gauge interest in and build a new teacher education model that simplifies how teacher educators interact with rich media. The new model builds on Teacher Moments, an online simulator for preservice teachers, and takes advantage of state of the art speech recognition and data visualization technology to help teacher educators learn the contents of rich media generated by teacher candidates without dedicating the time to listen or watch media. In my investigation, I find that there is an interest in such a model and that the new model succeeds in empowering teacher educators with the ability to use teacher candidate data to inform instructional decisions and substantiate discussion point during group debrief sessions. by Kevin Ng. M.Eng. 50 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 Electrical Engineering and Computer Science. Design of a teacher education model that improves teacher educator efficiency in processing teacher candidate data Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d22756f4-fa53-46ef-80cf-982adee7556c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Design of a teacher education model that improves teacher educator efficiency in processing teacher candidate data</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Ng, Kevin (Kevin Y.)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Existing state of the art practice-based teacher education models either rely on heavy teacher educator time commitment to process teacher candidate performance stored in rich media like audio or video, or rely on teacher candidates to voluntarily share experiences with minimal teacher educator interaction with data. Using an iterative design process, I work with teacher educators to gauge interest in and build a new teacher education model that simplifies how teacher educators interact with rich media. The new model builds on Teacher Moments, an online simulator for preservice teachers, and takes advantage of state of the art speech recognition and data visualization technology to help teacher educators learn the contents of rich media generated by teacher candidates without dedicating the time to listen or watch media. In my investigation, I find that there is an interest in such a model and that the new model succeeds in empowering teacher educators with the ability to use teacher candidate data to inform instructional decisions and substantiate discussion point during group debrief sessions.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>