Two pieces of Talis research have been published over recent weeks. Our Senior Researcher, Alan Dix, gives an overview.
“More than one way to flip a class: learning analytics for mixed models of learning“ in Compass Journal is a Case Study based on one of the early project Lighthouse pilots in Autumn 2014 where the Talis Player was used to deliver video and text material in ‘flip classroom’ mode.
As the title suggests, one of the outcomes of this was wide variety of ways in which the material could be used within the same course. Some parts involved ‘proper’ flipping where the in-class time was used purely for discussion.
However, on other occasions the face-to- face time still included standard lecture-like presentation, but where the more ‘information delivery’ materials were in the videos and the face-to- face toe was for the richer integrative material.
The pilot also gave us a chance to understand the ways in which the embedded learning analytics in project Lighthouse help in teaching. Partly this was about getting a sense of control, alleviating some of the “will the students actually watch or read it” panic, that always accompanies out-of- lecture material!
However, there were also occasions where the fine grained analytics made possible by the Talis Player enabled the academic to give detailed pedagogic advice to the students.
At ACM Learning at Scale (L@S) a Work in Progress paper “Challenge and Potential of Fine Grain, Cross-Institutional Learning Data“ was presented in poster format and also as an edX course, as part of L@S’s ‘flip conference’ experiment. See the poster and watch the video here.
Find out more about the edX course.
Learning at Scale is the premier venue for education research related to MOOCs and other forms of very large-scale learning. However, despite the huge growth in online learning, face-to- face teaching is still many times larger, with over 180 million higher education students worldwide and a new university opening in China every week.
The Talis work-in- progress posed the question and challenge of how to take ‘big data’ methods developed for MOOCs and large online courses, but to bring these to bear on improving the education of hundreds of millions of face-to- face learners. As part of our attempt to answer this, we presented very early work on using the highly detailed data from project Lighthouse to trace individual student’s reading pathways through documents.