Webinar: Privacy for the Total Learning Architecture (TLA)
Presenter: Bart Knijnenburg, Professor at Clemson University
The Total Learning Architecture (TLA) is envisioned to rely on ubiquitous data collection and advanced user modeling techniques to provide users with personalized and pervasive learning interventions. However, with so much personal data and learning material sharing, the psychology of the learner can be negatively affected by these far-reaching data collection practices.
In a series of webinars, Dr. Knijnenburg, from Clemson University and supported by the ADL Initiative, will share his research focused on the TLA privacy and cybersecurity. Dr. Knijnenburg will resolve this tension between data collection and privacy/security, to support the TLA in providing advanced personalization benefits to users, while at the same time respecting their privacy preferences and avoiding security risks.
Current solutions to such privacy problems are typically post hoc, these webinars will introduce the idea of privacy by design; a philosophy that allows one to build privacy into the core design of these learning/training systems, thereby avoiding privacy threats altogether. Particularly, Dr. Knijnenburg will discuss how different users deal with privacy, and how to optimally design data collection practices, data storage and ownership, sharing, and adaptation mechanisms with the users’ privacy in mind.
There are three webinars, each tailored to a different audience. More generally, if your organization develops or uses software that works extensively with end-user data, please feel free to join one of these webinars to learn how to best navigate the privacy landscape that comes with these data collection practices.
Registration links and the dates for the webinars are below:
Back-end developers session (Tuesday June 6):
Learning activity providers (Thursday June 8):
- Training department managers (Monday June 12):
Time: All webinars will start at 12pm ET / 9am PT / 5pm
Note: The views expressed are those of the author and do not necessarily represent the views or policies of the Advanced Distributed Learning (ADL) Initiative, the Federal Government, or DoD.