Beyond matchmaking: Considering aims for teacher data use

Abstract

Teachers who practice data-driven decision-making (DDDM) often do so with different desired results. Teachers’ end goal or aim for DDDM promotes particular inquiries and decisions, while stifling other possibilities. This idea that teachers’ data use aims promote certain types of inquiries while stifling others is the theme of this chapter. This chapter presents an instrumental case study of elementary teachers’ data-driven decision-making. The authors position matchmaking and investigating as two distinct data use aims, which drove teachers toward different data-driven inquiries and decisions. Matchmaking is described as the practice of using student data to sort students into preexisting educational tracks. By contrast, investigating is described as teachers’ use of student data to reflect on their teaching practice, identify systemic barriers to student learning, and make informed decisions about curricula, their teaching strategies, and students’ learning environment. Multiple examples of matchmaking and investigating are offered in the chapter, and the authors close with an argument favoring investigating as a promising practice for DDDM.