The Eyes Have It: Gaze-Based Detection of Mind Wandering during Learning with an Intelligent Tutoring System [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).

Autor/inn/en	Hutt, Stephen; Mills, Caitlin; White, Shelby; Donnelly, Patrick J.; D'Mello, Sidney K.
Titel	The Eyes Have It: Gaze-Based Detection of Mind Wandering during Learning with an Intelligent Tutoring System [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).
Quelle	(2016), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Attention; Intelligent Tutoring Systems; Eye Movements; Biology; Science Instruction; Undergraduate Students; Educational Technology; Technology Uses in Education + Suchen Sie Ihr Suchwort? Aufmerksamkeit; Intelligentes Tutorsystem; Augenbewegung; Biologie; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen
Abstract	Mind wandering (MW) is a ubiquitous phenomenon characterized by an unintentional shift in attention from task-related to task-unrelated thoughts. MW is frequent during learning and negatively correlates with learning outcomes. Therefore, the next generation of intelligent learning technologies should benefit from mechanisms that detect and combat MW. As an initial step in this direction, we used eye-gaze and contextual information (e.g., time into session) to build an automated MW detector as students interact with GuruTutor -- an intelligent tutoring system (ITS) for biology. Students self-reported MW by responding to pseudorandom thought-probes during the tutoring session while a consumer-grade eye tracker monitored their eye movements. We used supervised machine learning techniques to discriminate between positive and negative responses to the probes in a student-independent fashion. Our best results for detecting MW (F[subscript 1] of 0.49) were obtained with an evolutionary approach to develop topologies for neural network classifiers. These outperformed standard classifiers (F[subscript 1] of 0.43 with a Bayes net) and a chance baseline (F[subscript 1] of 0.19). We discuss our results in the context of integrating MW detection into an attention-aware version of GuruTutor. [For the full proceedings, see ED592609.] (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