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Gaze-Based Detection of Mind Wandering during Lecture Viewing
- 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