article

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

Authors:

Michel C. Desmarais,

Ryan S. BakerAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 22, Issue 1-2

Pages 9 - 38

https://doi.org/10.1007/s11257-011-9106-8

Published: 01 April 2012 Publication History

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.

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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 22, Issue 1-2

April 2012

216 pages

ISSN:0924-1868

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Copyright © Copyright © 2012 Springer Science+Business Media B.V.

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Kluwer Academic Publishers

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Published: 01 April 2012

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Guo YShen SLiu QHuang ZZhu LSu YChen ESerra ESpezzano F(2024)Mitigating Cold-Start Problems in Knowledge Tracing with Large Language Models: An Attribute-aware ApproachProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679664(727-736)Online publication date: 21-Oct-2024
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