Mathematical Knowledge for Equitable Teaching
The Mathematical Knowledge for Equitable Teaching Project (MKET) researches how to better prepare preservice elementary teachers to equitably teach mathematics. The project enables preservice teachers to better recognize equitable teaching practices by assessing instruction through video cases by using the Mathematical Quality and Equity Observation Protocol (MQE). This occurs through one-hour courses that students complete throughout their year prior to student teaching.
The researchers are studying both how to better prepare preservice mathematics teachers to learn the mathematics necessary for their teaching, and how to equip these teachers to teach in an equitable manner that encourages all of their students to share their mathematical thinking. Researchers will revise MQE to improve its validity and reliability and develop ways to use the MQE both for instruction and assessment. These revisions are helping develop an observational protocol that can greatly improve how researchers study the instructional practices of teachers.
This project produces materials, curriculum and a model for improving how elementary teachers learn mathematics and learn to teach that mathematics in an equitable way. The mini courses used in this project can also be adapted into a longer, more traditional preservice teacher course.
Mathematical Quality and Equity (MQE) Observational Rubric
The MQE Rubric is designed to measure the ability of preservice teachers to notice and identify practices of equitable mathematics instruction while observing teaching. Researchers from universities including Stanford, University of Michigan, University of Wisconsin, and University of Maryland convened in a workshop to engage in research activities to gauge if the instrument is valid and reliable for use by other researchers. They discussed constructs and dimensions included in the rubric and how to identify correct responses on the protocol, to help the MKET research team create a Master Coding document to analyze project data.
Imani Goffney, PI
Jennifer Chauvot, CoPI