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

Nabeela Altrabsheh, Mihaela Cocea, Sanaz Fallahkhair

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-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
Subtitle of host publicationEDM 2015
EditorsO. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, M. Desmarais
PublisherInternational Educational Data Mining Society
Pages436-440
ISBN (Electronic)9788460694250
Publication statusPublished - 2015
Event8th International Conference on Educational Data Mining EDM 2015 - UNED campus , Madrid, Spain
Duration: 26 Jun 201529 Jun 2015

Conference

Conference8th International Conference on Educational Data Mining EDM 2015
Country/TerritorySpain
CityMadrid
Period26/06/1529/06/15

Keywords

  • emotion prediction
  • Educational Data Mining
  • Machine Learning

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