User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science
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Published By Springer International Publishing

9783319087856, 9783319087863

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Adaptive Support versus Alternating Worked Examples and Tutored Problems: Which Leads to Better Learning?

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_15 ◽

2014 ◽

pp. 171-182 ◽

Cited By ~ 6

Author(s):

Amir Shareghi Najar ◽

Antonija Mitrovic ◽

Bruce M. McLaren

Keyword(s):

Worked Examples ◽

Adaptive Support

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Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Users

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_13 ◽

2014 ◽

pp. 146-157 ◽

Cited By ~ 6

Author(s):

Pawel Matuszyk ◽

Myra Spiliopoulou

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Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_30 ◽

2014 ◽

pp. 338-349 ◽

Cited By ~ 2

Author(s):

Yun Huang ◽

Yanbo Xu ◽

Peter Brusilovsky

Keyword(s):

Performance Prediction ◽

Student Modeling ◽

And Performance

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Client-Side Hybrid Rating Prediction for Recommendation

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_33 ◽

2014 ◽

pp. 369-380

Author(s):

Andrés Moreno ◽

Harold Castro ◽

Michel Riveill

Keyword(s):

Rating Prediction ◽

Client Side

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The Magic Barrier of Recommender Systems – No Magic, Just Ratings

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_3 ◽

2014 ◽

pp. 25-36 ◽

Cited By ~ 11

Author(s):

Alejandro Bellogín ◽

Alan Said ◽

Arjen P. de Vries

Keyword(s):

Recommender Systems

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Who’s Afraid of Job Interviews? Definitely a Question for User Modelling

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_37 ◽

2014 ◽

pp. 411-422 ◽

Cited By ~ 5

Author(s):

Kaśka Porayska-Pomsta ◽

Paola Rizzo ◽

Ionut Damian ◽

Tobias Baur ◽

Elisabeth André ◽

...

Keyword(s):

User Modelling ◽

Job Interviews

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Enhancing Exploratory Information-Seeking through Interaction Modeling

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_43 ◽

2014 ◽

pp. 478-483

Author(s):

Kumaripaba M. Athukorala

Keyword(s):

Information Seeking ◽

Interaction Modeling

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Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_41 ◽

2014 ◽

pp. 459-470 ◽

Cited By ~ 33

Author(s):

João Vinagre ◽

Alípio Mário Jorge ◽

João Gama

Keyword(s):

Matrix Factorization

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Extending Log-Based Affect Detection to a Multi-User Virtual Environment for Science

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_25 ◽

2014 ◽

pp. 290-300 ◽

Cited By ~ 8

Author(s):

Ryan S. Baker ◽

Jaclyn Ocumpaugh ◽

Sujith M. Gowda ◽

Amy M. Kamarainen ◽

Shari J. Metcalf

Keyword(s):

Virtual Environment ◽

Affect Detection

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Evil Twins: Modeling Power Users in Attacks on Recommender Systems

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-08786-3_20 ◽

2014 ◽

pp. 231-242 ◽

Cited By ~ 3

Author(s):

David C. Wilson ◽

Carlos E. Seminario

Keyword(s):

Recommender Systems ◽

Modeling Power

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User Modeling, Adaptation, and Personalization - Lecture Notes in Computer ScienceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Adaptive Support versus Alternating Worked Examples and Tutored Problems: Which Leads to Better Learning?

Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Users

Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models

Client-Side Hybrid Rating Prediction for Recommendation

The Magic Barrier of Recommender Systems – No Magic, Just Ratings

Who’s Afraid of Job Interviews? Definitely a Question for User Modelling

Enhancing Exploratory Information-Seeking through Interaction Modeling

Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback

Extending Log-Based Affect Detection to a Multi-User Virtual Environment for Science

Evil Twins: Modeling Power Users in Attacks on Recommender Systems

User Modeling, Adaptation, and Personalization - Lecture Notes in Computer Science
Latest Publications