Intervention Strategies to Increase Self-efficacy and Self-regulation in Adaptive On-Line Learning

This research outline refers to the validation of interventional strategies to increase the learner’s motivation and self-efficacy in an on-line learning environment. Previous work in this area is mainly based on Keller’s ARCS model of instructional design and this study argues for an approach based on Bandura’s Social Cognitive Theory – especially the aspects of self-efficacy and self-regulation. The research plan envisages two phases: The first phase will extract rules for interventional strategy selection from expert teachers. The second phase aims to validate these rules by providing to the learner the selected strategy and observing the resulting behavior. 1 Self-efficacy in Adaptive On-line Learning On-line education is one of the most dynamic and potentially enriching forms of learning that exists today. However, attrition rates for on-line learning courses, which tend to be 40% to 50% higher than traditional classroom courses (Dille & Metzack, 1991) is a serious problem resulting in personal, occupational and financial implications for both students and academic institutions. Motivation to learn is affected by a student’s selfefficacy, goal orientation, locus of control and self-regulation. In a traditional classroom tutors can infer the level of motivation of a student from several cues, including speech, behavior, attendance, body language, or feedback and can offer interventional strategies aimed at increasing self-efficacy and self-regulation. 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 learners’ level of motivation from observing the learning behavior and an adaptation component that selects the most appropriate intervention strategy. This study aims to inform the development of the adaptation component by extracting and validating selection rules for motivational intervention strategies to increase learners’ self-efficacy.