Using MotSaRT to Support On-Line Teachers in Student Motivation

Motivation to learn is affected by a student’s self-efficacy, goal orientation, locus of control and perceived task difficulty. 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 the level of motivation of the student and to have adaptive intervention strategies and rules of application to help prevent attrition. We have developed MotSaRT – a motivational strategies recommender tool to support on-line teachers in motivating learners. The design is informed by the Social Cognitive Theory constructs outlined above and a survey on motivation intervention strategies carried out with sixty on-line teachers. The survey results were analysed using a data mining algorithm (J48 decision trees) which resulted in a set of decision rules for recommending motivational strategies. The recommender tool, MotSaRT, has been developed based on these decision rules. Its functionality enables the teacher to specify the learner’s motivation profile. MotSaRT then recommends the most likely intervention strategies to increase motivation. A pilot study is currently being carried out using the MotSaRT tool.