The Effect of Predicting Expertise in Open Learner Modeling

Abstract

Learner's self-awareness of the breadth and depth of theirexpertise is crucial for self-regulated learning. Further,of learners report self-knowledge assessments to teachingsystems, this can be used to adapt teaching to them. Thesereasons make it valuable to enable learners to quickly andeasily create such models and to improve them. Followingthe trend to open these models to learners, we present aninterface for in- teractive open learner modeling usingexpertise predictions so that these assist learners inreflecting on their self-knowledge while building theirmodels. 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.