Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Pause, predict, and ponder: use of narrative videos to improve cultural discussion and learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mixture of gaussian processes for combining multiple modalities
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
International Journal of Artificial Intelligence in Education
Modeling engagement dynamics in spelling learning
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains
International Journal of Artificial Intelligence in Education - Best of AIED 2011
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We want to explore the relation between affective states, brainwaves and the learner answers during a multi-choice test questions. 24 participants were used in our experiment. While we were measuring their brainwaves, we asked them to answer 35 questions related to the 7 texts they read, for the first time, the day before. During the experiment, the participants can rate, at any time, their emotional dimensions (pleasure, arousal and dominance) on the Self-Assessment Manikin scale (SAM). Measuring the brainwaves determines the learner mental state and the emotional dimensions indicate the learner affective state. When a participant answers, he mentions if he knows the answer or not. Each answer can be either Right or False. The hypothesis of this paper is: “We can predict the learner's answers from his emotional dimensions and his brainwaves”. By using some machine learning techniques, we reached 90.49% accuracy. In a future work, these results will be implemented in an agent to improve the pedagogical strategies and the adaptation of the content within an Intelligent Tutoring System (STI).