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.
Thompson, M., Robinson, K., Kim, Y., Reich, J., & Owho-Ovuakporie, K. (2018, June 19). Teacher Moments: an online platform for preservice teachers to practice parent-teacher conversations. https://doi.org/10.31235/osf.io/26wkf
Thompson, Meredith, et al. “Teacher Moments: An Online Platform for Preservice Teachers to Practice Parent-teacher Conversations.” SocArXiv, 19 June 2018. Web.
Thompson, Meredith, Kevin Robinson, YJ Kim, Justin Reich, and Kesiena Owho-Ovuakporie. 2018. “Teacher Moments: An Online Platform for Preservice Teachers to Practice Parent-teacher Conversations.” SocArXiv. June 19. doi:10.31235/osf.io/26wkf.