June 17, 12:15 – 13:15, HG E 41
New learning systems and the spread of MOOCs over the last few years have made it increasingly easier to interpret learner data and generate supportive measures and predictions regarding learners and learning behaviour (from George Siemens). Here MOOCs are a particular trigger, as its big data practically invites us to evaluate it. Even in smaller courses, however, interpreting learning data can help us to simplify learning paths and act in ways which benefit students.
As an assistant in the creation of the “Future Cities” MOOC (https://www.edx.org/course/future-cities-ethx-fc-01x-0) Estefania Tapias had access to a large body of data and has filtered out several valuable findings, which she will present.
Lukas Fässler has extensive experience with computer-supported tutorials at ETH (http://www.cta.ethz.ch/computerbased_tutoring/etutorial). He will present a fascinating analysis of his data.
As an Educational Developer at ETH Urs Brändle frequently works with Moodle. He will show what information regarding learner behaviour may be extracted from Moodle with little extra effort.
- Lukas Fässler, David Sichau, Chair of Information Technology and Education
- Estefania Tapias, Chair of Information Architecture
- Urs Brändle, Educational Developer at the Department of Environmental Systems Science
- Mario Bold, Educational Developer at the Department of Biology
- Thomas Korner, Educational Development and Technology (LET)
- Marinka Valkering, Educational Development and Technology (LET)
Presentations, Documents and Links:
- Slides_UrsBrändle:MarioBold_RefreshTeaching_Learning Analytics
- Slides from Lukas Fässler and David Sichau (online available)