Paper Info
Title
Scalable Mind-Wandering Detection For Moocs: A Webcam-Based Approach
Abstract
Mind-wandering or loss of focus is a frequently occurring experience for many learners and negatively impacts learning outcomes. While in a classroom setting, a skilled teacher may be able to react to students' loss of focus, in Massive Open Online Courses (MOOCs) no such intervention is possible (yet). Previous studies suggest a strong relationship between learners' mind-wandering and their gaze, making it possible to detect mind-wandering in real-time using eye-tracking devices. Existing research in this area though has made use of specialized (and expensive) hardware, and thus cannot be employed in MOOC scenarios due to the inability to scale beyond lab settings. In order to make a step towards scalable mind-wandering detection among online learners, we propose the use of ubiquitously available consumer grade webcams. In a controlled study, we compare the accuracy of mind-wandering detection from gaze data recorded through a standard webcam and recorded through a specialized and high-quality eye tracker. Our results suggest that a large-scale application of webcam-based mind-wandering detection in MOOCs is indeed possible.
Year
DOI
Venue
2017
10.1007/978-3-319-66610-5_24
DATA DRIVEN APPROACHES IN DIGITAL EDUCATION
Keywords
Field
DocType
Learning analytics, MOOCs, Mind-wandering, Eye tracking
Learning analytics,Gaze,Computer science,Eye tracking,Multimedia,Mind-wandering,Scalability
Conference
Volume
ISSN
Citations 
10474
0302-9743
2
PageRank 
References 
Authors
0.39
5
3
Name
Order
Citations
PageRank
Yue Zhao118633.54
Christoph Lofi223725.27
Claudia Hauff379065.52