Eliciting Adaptation Knowledge from On-Line Tutors to Increase Motivation

In the classroom, teachers know how to motivate their students and how to exploit this knowledge to adapt or optimize their instruction when a student shows signs of demotivation. In on-line learning environments it is much more difficult to assess a learner’s motivation and to have adaptive intervention strategies and rules of application to help prevent attrition or dropout. In this paper, we present results from a survey of on-line tutors on how they motivate their learners. These results will inform the development of an adaptation engine by extracting and validating selection rules for strategies to increase motivation depending on the learner’s self-efficacy, goal orientation, locus of control and perceived task difficulty in adaptive Intelligent Tutoring Systems.