In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning

This paper explores automatically detecting student zoning out while performing a spoken learning task. Standard supervised machine learning techniques were used to create classification models, built on prosodic and lexical features. Our results suggest these features create models that can outperform a Bag of Words baseline.

• Type: Conference
• Year: 2010
• Where: ITS
• Authors: Joanna Drummond, Diane J. Litman
• Keywords: Features Create Models, ITS 2010, Machine Learning Techniques, Multimedia, Spoken Learning Task