Predicting students' emotions using machine learning techniques

Nabeela Altrabsheh, Mihaela Cocea, Sanaz Fallahkhair

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 publicationArtificial intelligence in education
Subtitle of host publication17th International Conference, AIED 2015, Madrid, Spain, June 22-26, 2015. proceedings
Editors Cristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
Place of PublicationCham
ISBN (Electronic)9783319197739
ISBN (Print)9783319197722
Publication statusPublished - 2015
Event17th International Conference on Artificial Intelligence in Education - Complutense University of Madrid, Madrid, Spain
Duration: 22 Jun 201526 Jun 2015

Publication series

NameLecture Notes in Artificial Intelligence


Conference17th International Conference on Artificial Intelligence in Education
Abbreviated titleAIED 2015


  • Machine Learning
  • models of emotions


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