The Effect of Predicting Expertise in Open Learner Modeling

  • Martin Hochmeister
  • Johannes Daxböck
  • Judy Kay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7563)


Learner’s self-awareness of the breadth and depth of their expertise is crucial for self-regulated learning. Further, of learners report self-knowledge assessments to teaching systems, this can be used to adapt teaching to them. These reasons make it valuable to enable learners to quickly and easily create such models and to improve them. Following the trend to open these models to learners, we present an interface for interactive open learner modeling using expertise predictions so that these assist learners in reflecting on their self-knowledge while building their models. We report study results showing that predictions (1) increase the size of learner models significantly, (2) lead to a larger spread in self-assessments and (3) influence learners’ motivation positively.


Prediction Expertise Open Learner Model Self-assessment Metacognition Adaptive Educational Systems 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Martin Hochmeister
    • 1
  • Johannes Daxböck
    • 1
  • Judy Kay
    • 2
  1. 1.Electronic Commerce GroupVienna University of TechnologyViennaAustria
  2. 2.School of Information TechnologiesUniversity of SydneyAustralia

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