Modeling and understanding students' off-task behavior in intelligent tutoring systems

We present a machine-learned model that can automatically detect when a student using an intelligent tutoring system is off-task, i.e., engaged in behavior which does not involve the system or a learning task. This model was developed using only log files of system usage (i.e. no screen capture or audio/video data). We show that this model can both accurately identify each student's prevalence of off-task behavior and can distinguish off-task behavior from when the student is talking to the teacher or another student about the subject matter. We use this model in combination with motivational and attitudinal instruments, developing a profile of the attitudes and motivations associated with offtask behavior, and compare this profile to the attitudes and motivations associated with other behaviors in intelligent tutoring systems. We discuss how the model of off-task behavior can be used within interactive learning environments which respond to when students are off-task. Author Key...

• Type: Conference
• Year: 2007
• Where: CHI
• Authors: Ryan Shaun Joazeiro de Baker
• Keywords: CHI 2007, Human Computer Interaction, Intelligent Tutoring Systems, Keywords Intelligent Tutoring, Off-task Behavior