Using learner focus of attention to detect learner motivation factors

Authors:
Lei Qu;Ning Wang;W. Lewis Johnson
Affiliations:
Center for Advanced Research in Technology for Education (CARTE), USC / ISI, Marina del Rey, CA;Center for Advanced Research in Technology for Education (CARTE), USC / ISI, Marina del Rey, CA;Center for Advanced Research in Technology for Education (CARTE), USC / ISI, Marina del Rey, CA
Venue:
UM'05 Proceedings of the 10th international conference on User Modeling
Year:
2005

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Abstract

This paper presents a model for pedagogical agents to use the learner's attention to detect motivation factors of the learner in interactive learning environments. This model is based on observations from human tutors coaching students in on-line learning tasks. It takes into account the learner's focus of attention, current task, and expected time required to perform the task. A Bayesian model is used to combine evidence from the learner's eye gaze and interface actions to infer the learner's focus of attention. Then the focus of attention is combined with information about the learner's activities, inferred by a plan recognizer, to detect the learner's degree of confidence, confusion and effort. Finally, we discuss the results of an empirical study that we performed to evaluate our model.