This project focuses on understanding the training and capacity needs of schools and districts around data use, from evidence-based improvement cycles, to data analytics, dashboards and early warning systems. The goal of this project is to help teachers, school leaders, and central office administrators use the data that we already collect in schools in more effective ways through leveraging the knowledge and analytics applied through big data and data science techniques. This project is funded through a generous grant from the office of the Provost of Teachers College and the Robertson Foundation.
Through a collaborative partnership with the Nassau County Long Island Board of Cooperative Educational Services (BOCES), and funded through a generous grant from the National Science Foundation (NSF), the goal of this project is to help build capacity and analytics for improving evidence-based improvement cycles in schools. In the first phase of the project we are surveying teachers and administrators on their data use needs for improving instructional practice. The second phase of the project is then understanding how educators use data dashboards, and then building open source code in collaboration with educators to provide the visualizations and analytics they need to help improve instruction in their schools.
This project brings together large public nationally representative education datasets with visual data analytics, clustering and data mining techniques to pattern and describe the journeys of students in Science, Technology, Engineering and Mathematics (STEM) from high school, through college and into careers. The aim of this project is to help provide a richer source of information for students as they move through the system, to help them find the resources that they need to help support their success. This project is funded through a generous grant from the National Science Foundation (NSF).
Teacher assigned grades are one of the most predictive early warning indicators of student success or challenge in school. This is due to the multi-dimensional nature of grades in that the majority of what teachers award grades for relate to engaged participation in the education system, which then is highly predictive of overall success in K-12 school, college, and ultimately employment. But grades are just one component of a constellation of datapoints that can help pinpoint student challenge and provide leaders information on how to direct the limited resources of schools to specific student needs. The goal of this project is to identify the most accurate predictors of student success in school and provide these predictors through early warning systems to schools.
Leadership in schools and districts is complex hard work. A persistent question is what priorities should leaders focus on for schools in different stages of the improvement continuum. The goal of this project is to describe the different types of school leadership that actual leaders practice, such as the interaction between instructional and transformational leadership, when different types of leadership are best applied for different schools, and how teachers conceive of and participate in the leadership of their schools. This work has involved collaborations with the international Organisation for Economic Cooperation and Development (OECD) in Paris France, University of Wisconsin-Madison, Politecnico di Milano (Polytechnic University of Milan) Italy, and Bejing Normal University China.
The goal of this project is to understand the pipeline of school and district leadership preparation, from teacher building-level leader certification, to the time and experiences in districts that prepare them to become principals, to the journey of school leaders to district-level administration.
Are there unusually effective school districts, and what are they doing that is different from districts with similar communities? The goal of this project is to identify school districts that continually outperform their peers across entire states over many years, and then identify strategies that will help generalize what these districts are doing differently to districts that are looking to improve.
Technology use in schools has long been of interest to students, parents, teachers and school leaders. In this project we work to identify different patterns of teacher technology use in schools to understand how to improve effective instructional practices and how school leaders can help influence technology use in schools. This work has involved collaborations with CSNYC, CS4ALL and the North East Big Data Hub.
This project focuses on the effect of school facilities on student learning, and how school district leaders can best fund needed improvements in capital facility construction to provide safe and effective learning environments for their students.
In this project we work use text data mining techniques and social network analysis to study how education research journals interact over time, through citation networks that describe the most prominent journals and clusters of journals, as well as automated text mining of all articles across specific journals’ entire publication history to map the rise and fall of latent topics that journals in the education research literature discuss over time. The project has involved collaborations with Georgia State University.