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 classifier for three emotions: amused, bored and excitement.
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Keywords
- Maximum Entropy
- Area Under Curve
- Machine Learning Technique
- Sentiment Analysis
- Computer Support Cooperative Work
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Altrabsheh, N., Cocea, M., Fallahkhair, S. (2015). Predicting Students’ Emotions Using Machine Learning Techniques. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_56
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DOI: https://doi.org/10.1007/978-3-319-19773-9_56
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