Analyzing Instruction in Mathematics using the Teaching for Robust Understanding Framework (AIM-TRU)

This is a research-practice partnership between three universities (Montclair State University, SUNY Buffalo State, DePaul University) and two teacher-leadership programs (MƒA, New York State Master Teacher Program), working to develop a video-based model of professional development that coherently integrates key elements of instructional systems.

In this project, we have iteratively refined an approach to professional development that is innovative because it is practice-based, incorporates video, and focuses on an open, high-quality instructional resource. Our project is called Analyzing Instruction in Mathematics Using the Teaching for Robust Understanding Framework (AIM-TRU).

In AIM-TRU, teachers collaboratively analyze video cases showing other teachers implementing lessons called Classroom Challenges. Classroom Challenges are 100 free, high-quality math lesson plans developed by the Shell Center for Mathematical Education to support teaching aligned to the Teaching for Robust Understanding (TRU) framework. A crucial element of these lessons is that they are built to foster rich classroom conversations on deep mathematical ideas, making them an ideal vehicle for video cases.

In order to create these video cases, our research team solicits teacher volunteers from the affiliated leadership programs to videotape Classroom Challenge lessons in their classrooms. Next, we identify episodes showing students engaged in rich mathematical activity, which we cut into a video case with associated case materials including the lesson plan, transcripts and context. These video cases form the backbone of our PD model, where teachers use these materials as well as sets of reflective questions based on the TRU framework. In developing the PD model, members of our research team have facilitated workshops based on the video cases with eight different groups of teachers in New York City, Buffalo, Chicago, Washington DC, and Los Angeles. We collected video data and/or field notes for these sessions to continue to refine the video cases and PD model.