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Autor/inn/enSyed, Munira; Chetlur, Malolan; Afzal, Shazia; Ambrose, G. Alex; Chawla, Nitesh V.
TitelImplicit and Explicit Emotions in MOOCs
[Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle(2019), (6 Seiten)
PDF als Volltext kostenfreie Datei Verfügbarkeit 
Spracheenglisch
Dokumenttypgedruckt; online; Monographie
SchlagwörterOnline Courses; Electronic Learning; Affective Behavior; Emotional Response; Outcomes of Education; Discussion Groups; Student Surveys; Interaction; Positive Attitudes; Introductory Courses; Statistics
AbstractUnderstanding the affect expressed by learners is essential for enriching the learning experience in Massive Open Online Courses (MOOCs). However, online learning environments, especially MOOCs, pose several challenges in understanding the different types of affect experienced by a learner. In this paper, we define two categories of emotions, explicit emotions as those collected directly from the student through self-reported surveys, and implicit emotions as those inferred unobtrusively during the learning process. We also introduce positivity as a measure to study the valence reported by students chronologically, and use it to derive insights into their emotion patterns and their association with learning outcomes. We show that implicit and explicit emotions expressed by students within the context of a MOOC are independent of each other, however, they correlate better with students' behavior compared to their valence. [For the full proceedings, see ED599096.] (As Provided).
AnmerkungenInternational Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2020/1/01
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