A review of recent advances in learner and skill modeling in intelligent learning environments

Original paper

Abstract

In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.

Keywords

Student models Learner models Probabilistic models Bayesian Networks IRT Model tracing POKS Bayesian Knowledge Tracing Intelligent Tutoring System Learning environments Cognitive modeling 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Polytechnique MontréalMontréalCanada
  2. 2.Worcester Polytechnic InstituteWorcesterUSA

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