A review of recent advances in learner and skill modeling in intelligent learning environments
A review of recent advances in learner and skill modeling in intelligent learning environments
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DOI：
10.1007/s11257-011-9106-8
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发表时间：
2012-04-01
影响因子：
3.6
通讯作者：
Baker, Ryan S. J. D.
中科院分区：
文献类型：
--
作者：
Desmarais, Michel C.;Baker, Ryan S. J. D.
近年来，学习者模型已经从研究实验室和研究教室走向更广阔的世界。学习者模型现在被嵌入到真实的世界的应用程序中，这些应用程序可以声称拥有数千甚至数十万用户。技能评估的概率模型在这些高级学习环境中发挥着关键作用。在本文中，我们回顾了在这些学习环境的成功中发挥了最大作用的学习者模型，以及学习者技能建模和评估的最新进展。最后，我们讨论了其他关键结构，如学习者动机，情绪和注意力状态，元认知和自我调节学习，小组学习，以及最近的运动朝着开放和共享的学习者模型建模的相关进展。
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.