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Gaze-Based Detection of Mind Wandering during Lecture Viewing

Authors :
Hutt, Stephen
Hardey, Jessica
Bixler, Robert
Stewart, Angela
Risko, Evan
D'Mello, Sidney K.
Source :
International Educational Data Mining Society. 2017.
Publication Year :
2017

Abstract

We investigate the use of consumer-grade eye tracking to automatically detect Mind Wandering (MW) during learning from a recorded lecture, a key component of many Massive Open Online Courses (MOOCs). We considered two feature sets: stimulus-independent global gaze features (e.g., number of fixations, fixation duration), and stimulus-dependent local features. We trained Bayesian networks using the aforementioned features and students? self-reports of MW and validated them in a manner that generalized to new students. Our results indicated that models built with global features (F[subscript 1] MW = 0.47) outperformed those using local features (F[subscript 1] MW = 0.34) and a chance-level model (F[subscript 1] MW = 0.30). We discuss our results in the context of MOOC development as well as integrating MW detection into attention-aware MOOCs. [For the full proceedings, see ED596512.]

Details

Language :
English
Database :
ERIC
Journal :
International Educational Data Mining Society
Publication Type :
Conference
Accession number :
ED596576
Document Type :
Speeches/Meeting Papers<br />Reports - Research