Abstract
Novel and simplified methods for determining low-level states of student behavior and predicting affective states enable tutors to better respond to students. The Many Eyes Word Tree graphics is used to understand and analyze sequential patterns of student states, categorizing raw quantitative indicators into a limited number of discrete sates. Used in combination with sensor predictors, we demonstrate that a combination of features, automatic pattern discovery and feature selection algorithms can predict and trace higher-level states (emotion) and inform more effective real-time tutor interventions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
