Other Resources for Data Use Examples

Student Privacy in Learning Analytics: An Information Ethics Perspective

May 10, 2017 | University of Wisconsin-Madison

Student Privacy in Learning Analytics: An Information Ethics Perspective

Higher education institutions have started using big data analytics tools. By gathering information about students as they navigate information systems, learning analytics employs techniques to understand student behaviors and to improve instructional, curricular, and support resources and learning environments. However, learning analytics presents important moral and policy issues surrounding student privacy. We argue that there […]

More
A Report on Building the Field of Learning Analytics for Personalized Learning at Scale

May 9, 2017 | Stanford University

A Report on Building the Field of Learning Analytics for Personalized Learning at Scale

The research and associated report, conducted under the auspices of the Learning Analytics Workgroup, describe how education data could transform how students are taught; how teachers are prepared and further developed; how education research is conducted; how education-related information is used and managed; and how foundations’ funds are allocated. The report points to the ways […]

More
Learning Analytics and Educational Data Mining: Towards Communication and Collaboration

May 9, 2017 | Columbia University

Learning Analytics and Educational Data Mining: Towards Communication and Collaboration

Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities – Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal […]

More
Improving Student Outcomes with Advanced Analytics

May 9, 2017 | SAS Institute Inc.

Improving Student Outcomes with Advanced Analytics

This paper argues that educators must recognize the need to treat students holistically through: • Meeting the academic needs of students regardless of where they are. That is, developing low-achieving students, supporting middle-range students, and challenging high-level students. • Ensuring progress each year by every student. Growth is not about every student reaching the same […]

More
Big Data and Analytics in K-12 Education: The Time is Right

May 9, 2017 | Center for Digital Education

Big Data and Analytics in K-12 Education: The Time is Right

This one pager discusses how detailed data about student demographics and test results isn’t easily accessible to the people who need it most — classroom teachers, principals and instructional support staff. It discusses strategies for how to find insights that teachers and schools need in order to pinpoint teaching and learning problems and identify the best […]

More
The Importance of Disaggregating Student Data

May 9, 2017 | National Center for Mental Health Promotion and Youth Violence Prevention

The Importance of Disaggregating Student Data

This brief provides an overview of the value of disaggregating data, common areas of data to disaggregate, examples of how disaggregated data has been used, limitations of disaggregating data, particularly data describing students.

More
Infographic: How Data Empowers Parents

April 28, 2017 | Data Quality Campaign

Infographic: How Data Empowers Parents

When parents have the right information to make decisions, students excel. But often the only information parents receive about their child’s education is through paper report cards and the occasional parent teacher conference. Better information empowers parents to provide better support, make better decisions, and be better advocates.

More