Using MotSaRT to Support On-Line Teachers in Student Motivation

  • Teresa Hurley
  • Stephan Weibelzahl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4753)

Abstract

In classrooms teachers know how to motivate their students and 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 motivation of the student and to have adaptive intervention strategies and rules of application to help prevent attrition. We developed MotSaRT – a motivational strategies recommender tool – to support on-line teachers in motivating learners. The design is informed by Social Cognitive Theory 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. 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.

Keywords

on-line learning motivation intervention strategies on-line teachers self-efficacy goal orientation locus of control perceived task difficulty recommender tool 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Teresa Hurley
    • 1
  • Stephan Weibelzahl
    • 1
  1. 1.National College of Ireland, School of Informatics, Mayor Street, Dublin 1Ireland

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