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• W1987626674 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." @default.
• W1987626674 created "2016-06-24" @default.
• W1987626674 creator A5005514400 @default.
• W1987626674 creator A5088695714 @default.
• W1987626674 date "2011-10-18" @default.
• W1987626674 modified "2023-10-16" @default.
• W1987626674 title "A review of recent advances in learner and skill modeling in intelligent learning environments" @default.
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• W1987626674 doi "https://doi.org/10.1007/s11257-011-9106-8" @default.
• W1987626674 hasPublicationYear "2011" @default.
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