Publication
ARFLED: Ability Recognition Framework for Learning and Education
Shoya Ishimaru; Andreas Dengel
In: the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing. International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp-2017), the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, September 11-15, Maui, Hawaii, USA, ISBN 978-1-4503-5190-4, ACM, 9/2017.
Abstract
Learning is one of the vital behaviors of human beings. This paper demonstrates a framework to augment learning activities by packaging two key ideas: Eyetifact and HyperMind. Eyetifact is a system that converts data of eye movements beyond the difference of sensing devices to collect a large amount of training data for machine learning. HyperMind is a digital textbook that displays learning materials dynamically based on a learner's cognitive states as measured by several sensors. In order to implement these two ideas, we have conducted experiments related to eyewear computing, textbook reading behavior analysis, and stress sensing. The contributions of this research are to investigate approaches that recognize human abilities and to transfer them from experts to others.