Predicting students' emotions using machine learning techniques

Nabeela Altrabsheh, Mihaela Cocea, Sanaz Fallahkhair

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

Detecting students' real-time emotions has numerous benefits, such as helping lecturers understand their students' learning behaviour and to address problems like confusion and boredom, which undermine students' engagement. One way to detect students' emotions is through their feedback about a lecture. Detecting students' emotions from their feedback, however, is both demanding and time-consuming. For this purpose, we looked at several models that could be used for detecting emotions from students' feedback by training seven different machine learning techniques using real students' feedback. The models with a single emotion performed better than those with multiple emotions. Overall, the best three models were obtained with the CNB classiffier for three emotions: amused, bored and excitement.
Original languageEnglish
Title of host publication17th International Conference on Artificial Intelligence in Education (AIED 2015)
Place of PublicationCham
PublisherSpringer
Pages537-540
Number of pages4
Volume9112
ISBN (Electronic)9783319197739
ISBN (Print)9783319197722
DOIs
Publication statusPublished - 22 Jun 2015
Event17th International Conference on Artificial Intelligence in Education (AIED 2015) - Madrid, Spain, 21-25 June, 2015
Duration: 22 Jun 2015 → …

Publication series

NameLecture Notes in Computer Science

Conference

Conference17th International Conference on Artificial Intelligence in Education (AIED 2015)
Period22/06/15 → …

Bibliographical note

The final publication is available at link.springer.com

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  • Cite this

    Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2015). Predicting students' emotions using machine learning techniques. In 17th International Conference on Artificial Intelligence in Education (AIED 2015) (Vol. 9112, pp. 537-540). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-19773-9_56