Literaturnachweis - Detailanzeige
| Autor/inn/en | Hutt, Stephen; Hardey, Jessica; Bixler, Robert; Stewart, Angela; Risko, Evan; D'Mello, Sidney K. |
|---|---|
| Titel | Gaze-Based Detection of Mind Wandering during Lecture Viewing [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017). |
| Quelle | (2017), (6 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Eye Movements; Attention; Lecture Method; Student Behavior; Online Courses; Large Group Instruction; Models; Intelligent Tutoring Systems; Video Technology; Foreign Countries; Undergraduate Students; Canada |
| 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.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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