Data-driven decision making and the use of 'big data' in education are two practice and policy areas that have yet to be studied carefully at the level of teacher classroom instructional decision making. The researchers will partner with the Summit Public School, a charter management organization, to study the ways in which teachers use data from student virtual learning experiences to make decisions about instruction. The researchers will work with teachers, administrators and information technology support staff in four schools that employ an online learning platform that includes instructional resources and content assessments as a central structure in students' learning environments. Data intensive research methods will be used to mine the rich data arising from the students' use of the online resources and performances to determine the resources that a student needs next. The proposal seeks to determine the key challenges facing practitioners in their use of data intensive research methods and to identify what partnership activities best support evidence-based practices. The findings from this study will lead to an understanding of the utility and feasibility of a teacher's use of the volumes of data that come from virtual learning environments.
The researchers in this study will be working with the students, teachers and administrators from four schools, three high schools and one middle school using a design research perspective. Practitioners will determine particular problems of practice around which the identification and analysis of data will be focused. Students in mathematics courses will be provided access to digital resources that are aligned to content assessments. The researchers will examine the correlation of the use of selected resources and outcomes from those assessments as well as student surveys, and subsequent course taking patterns and successes. Hierarchical cluster analyses, binary factor analyses and hierarchical linear modeling techniques will be used to identify which resources are linked to increased likelihoods of student success. The analyses will be used to develop recommendations of particular resources teachers might make available to students and to develop an early warning system that will help teachers keep students on track.
