choosing when to interact with learners
In this paper, we describe a method for pedagogical agents to choose when to interact with learners in interactive learning environments. This method is based on observations of human tutors coaching students in on-line learning tasks. It takes into account the focus of attention of the learner, the learner's current task, and expected time required to perform the task. A Bayesian network model combines evidence from eye gaze and interface actions to infer learner focus of attention. The attention model is combined with a plan recognizer to detect different types of learner difficulties such as confusion and indecision which warrant intervention. We plan to incorporate this capability into a pedagogical agent able to interact with learners in socially appropriate ways.
interaction learning paper method pedagogical agents agent learning environment observability human student task account attention timing bayesian network model evidence eye gaze interfaces action plan types affective interfaces intelligent assistants task modeling interface agents human-computer collaboration plan recognition