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 language | English |
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Title of host publication | 17th International Conference on Artificial Intelligence in Education (AIED 2015) |
Place of Publication | Cham |
Publisher | Springer |
Pages | 537-540 |
Number of pages | 4 |
Volume | 9112 |
ISBN (Electronic) | 9783319197739 |
ISBN (Print) | 9783319197722 |
DOIs | |
Publication status | Published - 22 Jun 2015 |
Event | 17th International Conference on Artificial Intelligence in Education (AIED 2015) - Madrid, Spain, 21-25 June, 2015 Duration: 22 Jun 2015 → … |
Publication series
Name | Lecture Notes in Computer Science |
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Conference
Conference | 17th International Conference on Artificial Intelligence in Education (AIED 2015) |
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Period | 22/06/15 → … |
Bibliographical note
The final publication is available at link.springer.comFingerprint
Dive into the research topics of 'Predicting students' emotions using machine learning techniques'. Together they form a unique fingerprint.Profiles
-
Sanaz Fallahkhair
- School of Arch, Tech and Eng - Principal Lecturer
- Computing and Mathematical Sciences Research and Enterprise Group
Person: Academic