Abstract
Learning analytics researchers often face problems when dealing with data that contains personally identifying information, and the protection of stakeholder privacy in analysis systems. As learning management systems become more important within education institutions, these systems are being subject to increasingly stringent standards to protect user privacy. This however has the potential to hinder learning analytics research because data collected in controlled LMS environments cannot simply be transferred as is to research systems for real-time analysis. In this talk, I will introduce a proposed system design that provides an interface between integrated production and research systems to allow user authentication, information, and learning analytics results to be seamlessly transferred between systems. The interface provides a level of anonymity to allow a greater degree of research freedom when analysing data without exposing private data directly through research systems.
Bio
Brendan Flanagan is a Researcher at the Academic Center for Computing and Media Studies, Kyoto University, Japan.
He received a PhD in Information Science from the Graduate School of Information Science and Electrical Engineering, Kyushu University. Before joining Kyoto University in 2017, he was a Young Scientist Research Fellow with the Japan Society for the Promotion of Science from 2015. His current research interests include: learning analytics, text mining, machine learning, and language learning.
