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E-learning systems : intelligent techniques for personalization / Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain

Contributor(s): Budimac, Zoran,, autor | Ivanovic, Mirjana, autor | Jain, L. C., autor | Klašnja-Milićević, Aleksandra,, autor | Vesin, Boban,, autor
Material type: materialTypeLabelE-bookSeries: (Intelligent systems reference library, 1868-4394 ; volume 112).Publisher: Switzerland : Springer, [2016]Copyright date: 2017Description: 1 recurso en línea (xxiii, 294 páginas) : ilustraciones (algunas a color).ISBN: 3319411632; 9783319411637.Subject: Enseñanza en líneaOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Foreword; Preface; Contents; About the Authors; Abbreviations; Abstract; Preliminaries; 1 Introduction to E-Learning Systems; Abstract; 1.1 Web-Based Learning; 1.2 E-Learning; 1.3 E-Learning Objects, Standards and Specifications; 1.3.1 E-Learning Objects; 1.3.2 E-Learning Specifications and Standards; 1.3.2.1 S1. IEEE LOM and IMS Learning Resource Metadata; 1.3.2.2 S2. Dublin Core Metadata Initiative; 1.3.2.3 S3. IMS Learner Information Package; 1.3.2.4 S4. IMS Content Packaging; 1.3.2.5 S5. IMS Simple Sequencing; 1.3.2.6 S6. ADL SCORM; 1.3.3 Analysis of Standards and Specifications.
3.3.4 Information Understanding: Sequential and Global LearnersReferences; 4 Adaptation in E-Learning Environments; Abstract; 4.1 Adaptive Educational Hypermedia; 4.2 Content Adaptation; 4.3 Link Adaptation; References; 5 Agents in E-Learning Environments; Abstract; 5.1 Some Existing Agent Based Systems; 5.2 HAPA System Overview; 5.2.1 Harvesting and Classifying the Learning Material; 5.2.1.1 Pedagogical agents; References; 6 Recommender Systems in E-Learning Environments; Abstract; 6.1 Recommendations and Recommender Systems.
6.2 The Most Important Requirements and Challenges for Designing a Recommender System in E-Learning Environments6.3 Recommendation Techniques for RS in E-Learning Environments-A Survey of the State-of-the-Art; 6.3.1 Collaborative Filtering Approach; 6.3.2 Content-Based Techniques; 6.3.3 Association Rule Mining; References; 7 Folksonomy and Tag-Based Recommender Systems in E-Learning Environments; Abstract; 7.1 Comprehensive Survey of the State-of-the-Art in Collaborative Tagging Systems and Folksonomy; 7.1.1 Tagging Rights; 7.1.2 Tagging Support; 7.1.3 Aggregation; 7.1.4 Types of Object.
7.1.5 Sources of Material7.1.6 Resource Connectivity; 7.1.7 Social Connectivity; 7.2 A Model for Tagging Activities; 7.3 Tag-Based Recommender Systems; 7.3.1 Extension with Tags; 7.3.2 Collecting Tags; 7.4 Applying Tag-Based Recommender Systems to E-Learning Environments; 7.4.1 FolkRank Algorithm; 7.4.2 PLSA; 7.4.3 Collaborative Filtering Based on Collaborative Tagging; 7.4.4 Tensor Factorization Technique for Tag Recommendation; 7.4.4.1 SVD Algorithm; 7.4.4.2 Tensors and HOSVD Algorithm; 7.4.4.3 Ranking with Tensor Factorization; 7.4.4.4 Multi-mode Recommendations; 7.4.5 Most Popular Tags.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Biblioteca CRAI (Literatura, ensayo y audiovisuales) LB1044.87 2017 EB (Browse shelf(Opens below)) Acceso electrónico eBook.20022039
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Incluye referencias bibliográficas

Foreword; Preface; Contents; About the Authors; Abbreviations; Abstract; Preliminaries; 1 Introduction to E-Learning Systems; Abstract; 1.1 Web-Based Learning; 1.2 E-Learning; 1.3 E-Learning Objects, Standards and Specifications; 1.3.1 E-Learning Objects; 1.3.2 E-Learning Specifications and Standards; 1.3.2.1 S1. IEEE LOM and IMS Learning Resource Metadata; 1.3.2.2 S2. Dublin Core Metadata Initiative; 1.3.2.3 S3. IMS Learner Information Package; 1.3.2.4 S4. IMS Content Packaging; 1.3.2.5 S5. IMS Simple Sequencing; 1.3.2.6 S6. ADL SCORM; 1.3.3 Analysis of Standards and Specifications.

3.3.4 Information Understanding: Sequential and Global LearnersReferences; 4 Adaptation in E-Learning Environments; Abstract; 4.1 Adaptive Educational Hypermedia; 4.2 Content Adaptation; 4.3 Link Adaptation; References; 5 Agents in E-Learning Environments; Abstract; 5.1 Some Existing Agent Based Systems; 5.2 HAPA System Overview; 5.2.1 Harvesting and Classifying the Learning Material; 5.2.1.1 Pedagogical agents; References; 6 Recommender Systems in E-Learning Environments; Abstract; 6.1 Recommendations and Recommender Systems.

6.2 The Most Important Requirements and Challenges for Designing a Recommender System in E-Learning Environments6.3 Recommendation Techniques for RS in E-Learning Environments-A Survey of the State-of-the-Art; 6.3.1 Collaborative Filtering Approach; 6.3.2 Content-Based Techniques; 6.3.3 Association Rule Mining; References; 7 Folksonomy and Tag-Based Recommender Systems in E-Learning Environments; Abstract; 7.1 Comprehensive Survey of the State-of-the-Art in Collaborative Tagging Systems and Folksonomy; 7.1.1 Tagging Rights; 7.1.2 Tagging Support; 7.1.3 Aggregation; 7.1.4 Types of Object.

7.1.5 Sources of Material7.1.6 Resource Connectivity; 7.1.7 Social Connectivity; 7.2 A Model for Tagging Activities; 7.3 Tag-Based Recommender Systems; 7.3.1 Extension with Tags; 7.3.2 Collecting Tags; 7.4 Applying Tag-Based Recommender Systems to E-Learning Environments; 7.4.1 FolkRank Algorithm; 7.4.2 PLSA; 7.4.3 Collaborative Filtering Based on Collaborative Tagging; 7.4.4 Tensor Factorization Technique for Tag Recommendation; 7.4.4.1 SVD Algorithm; 7.4.4.2 Tensors and HOSVD Algorithm; 7.4.4.3 Ranking with Tensor Factorization; 7.4.4.4 Multi-mode Recommendations; 7.4.5 Most Popular Tags.

Online resource; title from PDF title page (SpringerLink, viewed July 29, 2016).

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