I​ntegrated data systems ​enable leaders to look across public programs and uncover ​actionable intelligence for social policy​. Find e​xamples of data-driven decision making​ and ​resources on…

Starting this fall, students will be getting into the dining halls hands-free.

by Frida Alim, Nate Cardozo, Gennie Gebhart, Karen Gullo, and Amul KaliaDownload the report as a PDF.EXECUTIVE SUMMARYStudents and their families are backed into a corner. As students across the

Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation — learning analytics — with three other big data contexts — the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.

This paper studies emerging technologies for tracking reading behaviors (“reading analytics”) and their implications for reader privacy, attempting to place them in a historical context. It discusses what data is being collected, to whom it is available, and how it might be used by various interested parties (including authors). I explore means of tracking what’s being read, who is doing the reading, and how readers discover what they read. The paper includes two case studies: mass-market e-books (both directly acquired by readers and mediated by libraries) and scholarly journals (usually mediated by academic libraries); in the latter case I also provide examples of the implications of various authentication, authorization and access management practices on reader privacy. While legal issues are touched upon, the focus is generally pragmatic, emphasizing technology and marketplace practices. The article illustrates the way reader privacy concerns are shifting from government to commercial surveillance, and the interactions between government and the private sector in this area. The paper emphasizes U.S.-based developments.

Weak sauce. Planning doesn't fix everything.

More thinking on predictive analytics.

Responses prompt new questions.

This is part nine of my annual review of the year in ed-tech

School surveillance poses challenges for equity and student data privacy. The authors suggest six principles to guide state policymakers toward effective policies that balance these challenges with the school safety benefits.

Update– Please see follow-on posts addressing changes since this post: Nov 13, 2016: “Piazza Response To Blog Post On Student Privacy” Nov 21, 2016: “University Responses to Piazza: Some good, some bad, some web site changes” Dec 21, 2016: “Piazza Makes Three Significant Changes To Deal With Privacy Issues” Piazza is a collaborative question-and-answer platform […]

With access to more 'big' data than ever before, how can college avoid invading students’ privacy while promoting academic success?

CREEPY AF.

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students