Learner Affect through the Looking Glass: Characterization and Detection of Confusion in Online Courses [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017).

Autor/inn/en	Zeng, Ziheng; Chaturvedi, Snigdha; Bhat, Suma
Titel	Learner Affect through the Looking Glass: Characterization and Detection of Confusion in Online Courses [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 Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Online Courses; Large Group Instruction; Educational Technology; Technology Uses in Education; Group Discussion; Computer Mediated Communication; Affective Behavior; Emotional Response; Models; Prediction; College Students; Data Collection; Data Analysis; California + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Gruppendiskussion; Computerkonferenz; Affective disturbance; Active behaviour; Affektive Störung; Emotionales Verhalten; Analogiemodell; Vorhersage; Collegestudent; Data capture; Datensammlung; Auswertung; Kalifornien
Abstract	Characterizing the nature of students' affective and emotional states and detecting them is of fundamental importance in online course platforms. In this paper, we study this problem by using discussion forum posts derived from large open online courses. We find that posts identified as encoding confusion are actually manifestations of different learner affects pertaining to their informational needs--primarily seeking factual answers. We quantitatively demonstrate that the use of content-related linguistic features and community-related features derived from a post serve as reliable detectors of confusion while widely "outperforming" currently available algorithms of confusion detection. We also point out that several prediction tasks in this domain (e.g., confusion and urgency detection) can be correlated, and that a model trained for one task can effectively be used for making predictions on the other task without requiring labeled examples. Finally, we highlight a very significant problem of adapting the classifier to unseen courses. [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