Predicting Learning-Related Emotions from Students' Textual Classroom Feedback via Twitter [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Altrabsheh, Nabeela; Cocea, Mihaela; Fallahkhair, Sanaz
Institution	International Educational Data Mining Society
Titel	Predicting Learning-Related Emotions from Students' Textual Classroom Feedback via Twitter [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Feedback (Response); Social Media; Emotional Response; Teaching Methods; Prediction; Ambiguity (Semantics); Teaching Styles; Learning Processes; Teacher Student Relationship; Foreign Countries; English (Second Language); Second Language Learning; Language of Instruction; College Students; Student Attitudes; Computational Linguistics; Language Usage; Data Analysis; Jordan + Suchen Sie Ihr Suchwort? Soziale Medien; Emotionales Verhalten; Teaching method; Lehrmethode; Unterrichtsmethode; Vorhersage; Lehrstil; Unterrichtsstil; Learning process; Lernprozess; Teacher student relationships; Lehrer-Schüler-Beziehung; Ausland; English as second language; English; Second Language; Englisch als Zweitsprache; Zweitsprachenerwerb; Teaching language; Unterrichtssprache; Collegestudent; Schülerverhalten; Linguistics; Computerlinguistik; Sprachgebrauch; Auswertung
Abstract	Teachers/lecturers typically adapt their teaching to respond to students' emotions, e.g. provide more examples when they think the students are confused. While getting a feel of the students' emotions is easier in small settings, it is much more difficult in larger groups. In these larger settings textual feedback from students could provide information about learning-related emotions that students experience. Prediction of emotions from text, however, is known to be a difficult problem due to language ambiguity. While prediction of general emotions from text has been reported in the literature, very little attention has been given to prediction of learning-related emotions. In this paper we report several experiments for predicting emotions related to learning using machine learning techniques and n-grams as features, and discuss their performance. The results indicate that some emotions can be distinguished more easily then others. [For complete proceedings, see ED560503.] (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