Current Challenges and Opportunities in Data-Driven Learning

Authors:
Open Access
Publisher website
Google Scholar
Abstract
This chapter discusses some of the current challenges and opportunities in data-driven learning, including the role played by Artificial Intelligence, data protection techniques and human capital. It argues that computational systems can accelerate and support (but not replace) humans in data-driven learning, that advances in data protection can improve privacy and access to microdata for research, and that human capital is a key factor for data-driven learning, both in the production of causal evidence and in its communication and use in society. Finally, the chapter discusses the association between data-driven learning and trust in the public sector in democracies, which further reinforces interest in policy measures that support data-driven learning.

This publication has 28 references indexed in Scilit: