Abstract
This work explores the idea of improving e-learning objects through the use of analytics modeled after Google Analytics. Prior work on the use of metrics in e-learning has focused on user satisfaction, and the ranking and selection of learning objects from a set of available choices. The work is unique in its focus on the kinds of metrics needed to improve an existing e-learning object, and more specifically to make improvements to specific pages within an e-learning object. The approach is based on the now well-established track record of using Google Analytics for website optimization in e-commerce. The work presented here addresses adaptations needed to apply similar metrics in the context of e-learning and more specifically e-learning objects.
Faculty Sponsors
Dr. Alvaro Escobar, Dr. Michael Van Hilst
Project Type
Event
Location
Alvin Sherman Library
Start Date
4-4-2014 1:00 PM
End Date
4-4-2014 5:30 PM
Google Analytics for Online Education
Alvin Sherman Library
This work explores the idea of improving e-learning objects through the use of analytics modeled after Google Analytics. Prior work on the use of metrics in e-learning has focused on user satisfaction, and the ranking and selection of learning objects from a set of available choices. The work is unique in its focus on the kinds of metrics needed to improve an existing e-learning object, and more specifically to make improvements to specific pages within an e-learning object. The approach is based on the now well-established track record of using Google Analytics for website optimization in e-commerce. The work presented here addresses adaptations needed to apply similar metrics in the context of e-learning and more specifically e-learning objects.
