Titlebook: User Modeling, Adaptation and Personalization; 23rd International C Francesco Ricci,Kalina Bontcheva,Séamus Lawless Conference proceedings

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書目名稱User Modeling, Adaptation and Personalization影響因子(影響力)




書目名稱User Modeling, Adaptation and Personalization影響因子(影響力)學(xué)科排名




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書目名稱User Modeling, Adaptation and Personalization網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱User Modeling, Adaptation and Personalization被引頻次




書目名稱User Modeling, Adaptation and Personalization被引頻次學(xué)科排名




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書目名稱User Modeling, Adaptation and Personalization年度引用學(xué)科排名




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Conference proceedings 2015chnologies on a large number of everyday application areas such as: intelligent learning environments, recommender systems, e-commerce, advertising, personalized information retrieval and access, digital humanities, e-government, cultural heritage, and personalized health..

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Where to Next? A Comparison of Recommendation Strategies for Navigating a Learning Object Repositoryhavior, and alignment to academic standards. An evaluation from subject experts suggests that usage-based recommendations are best aligned with teacher needs, though there are interesting domain interactions that suggest the need for continued investigation.

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Modeling Motivation in a Social Network Game Using Player-Centric Traits and Personality Traitsults show that several player characteristics impact motivation, expressed in terms of enjoyment and effort. We also show that player enjoyment and effort, as predicted by our models, impact players’ in-game behaviors, illustrating both the predictive power and practical utility of our models for guiding user adaptation.

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MENTOR: A Physiologically Controlled Tutoring Systemch involving two groups of learners showed that the group who interacted with the mental state-based adaptive version of the system obtained higher learning outcomes and had a better learning experience than the group who interacted with a non-adaptive version.

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