Predicting learning-related emotions from students' textual classroom feedback via Twitter

Nabeela Altrabsheh, Mihaela Cocea, Sanaz Fallahkhair

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNResearchpeer-review

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.
Original languageEnglish
Title of host publicationThe 8th International Conference on Educational Data Mining (EDM)
Place of PublicationMadrid, Spain
PublisherInternational Educational Data Mining Society
Pages436-440
Number of pages5
ISBN (Print)9788460694250
Publication statusPublished - 29 Jun 2015
EventThe 8th International Conference on Educational Data Mining (EDM) - Madrid, Spain, 22-26 June 2015
Duration: 29 Jun 2015 → …

Conference

ConferenceThe 8th International Conference on Educational Data Mining (EDM)
Period29/06/15 → …

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Students
Feedback
Learning systems
Teaching
Experiments

Cite this

Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2015). Predicting learning-related emotions from students' textual classroom feedback via Twitter. In The 8th International Conference on Educational Data Mining (EDM) (pp. 436-440). Madrid, Spain: International Educational Data Mining Society.
Altrabsheh, Nabeela ; Cocea, Mihaela ; Fallahkhair, Sanaz. / Predicting learning-related emotions from students' textual classroom feedback via Twitter. The 8th International Conference on Educational Data Mining (EDM). Madrid, Spain : International Educational Data Mining Society, 2015. pp. 436-440
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Altrabsheh, N, Cocea, M & Fallahkhair, S 2015, Predicting learning-related emotions from students' textual classroom feedback via Twitter. in The 8th International Conference on Educational Data Mining (EDM). International Educational Data Mining Society, Madrid, Spain, pp. 436-440, The 8th International Conference on Educational Data Mining (EDM), 29/06/15.

Predicting learning-related emotions from students' textual classroom feedback via Twitter. / Altrabsheh, Nabeela; Cocea, Mihaela; Fallahkhair, Sanaz.

The 8th International Conference on Educational Data Mining (EDM). Madrid, Spain : International Educational Data Mining Society, 2015. p. 436-440.

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNResearchpeer-review

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AB - 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.

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Altrabsheh N, Cocea M, Fallahkhair S. Predicting learning-related emotions from students' textual classroom feedback via Twitter. In The 8th International Conference on Educational Data Mining (EDM). Madrid, Spain: International Educational Data Mining Society. 2015. p. 436-440