Eliciting Adaptation Knowledge from On-line Tutors to
Teresa Hurley, Stephan Weibelzahl
National College of Ireland, School of Informatics,
Mayor Street 1,
Dublin 1, Ireland
Abstract. 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 drop-out. 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.
On-line learning is a dynamic and potentially enriching forms of learning but attrition remains a serious problem . Motivation to learn is affected by the learner’s self-efficacy, goal orientation, locus of control and perceived task difficulty. In the traditional classroom tutors infer learners’ levels of motivation from several cues, including speech, behavior, attendance, body language or feedback, and offer interventional strategies aimed at increasing motivation. Intelligent Tutoring Systems (ITS) need to be able to recognize when the learner is becoming demotivated and to intervene with effective motivational strategies. Such an ITS would comprise two main components, an assessment mechanism that infers the learner’s level of motivation from observing the learner’s behaviour, and an adaptation component that selects the most appropriate intervention strategy to increase motivation. This paper presents the results of a survey of on-line tutors on how they motivate their learners. These results will inform the development of the adaptation component by extracting and validating selection rules for strategies to increase motivation.
The focus of this research is intervention strategies which can be implemented and validated in an Intelligent Tutoring System to increase motivation and reduce attrition. Previous approaches in this field were mainly based on the ARCS model, which is an instructional design model (). In contrast, the approach being taken in this research is based on Social Cognitive Theory (SCT) , particularly on self-efficacy, locus of control, perceived task difficulty and goal orientation. Self-