Presentation used by Tinne De Laet, KU Leuven, for a keynote presentation during an event: organised by Leiden University, Erasmus University Rotterdam, and Delft University of Technology.
The presentations presents the results of two case studies from the Erasmus+ project ABLE and STELA, and provides 9 recommendations regarding learning analytics
The workshop aims to raise awareness on the effects of culture on learning and beliefs about learning, an aspect that becomes more and more relevant as technology and the Internet are seen as means for making education available to people all over the globe…
This presentation explores shortcomings of learning analytics for the wide adoption in educational organisations. It is NOT about ethics and privacy rather than focuses on shortcomings of learning analytics for teachers and students in the classroom (micro-level).
Data and analytics are at the center of today’s student success movement. Increased interest and access to institutional data are helping colleges and universities identify systematic and structural barriers to retention and graduation. Predictive analytics is making proactive advising possible at scale.
OU Analyse is a project piloting machine-learning based methods for early identification of students at risk of failing. All students with their risk of failure are available weekly to the course tutors and the Student Support Teams to consider appropriate support. The overall objective is to significantly improve the retention of OU students.