E-learning systems : intelligent techniques for personalization

• Main Authors: Klašnja-Milićević, Aleksandra (Author), Vesin, Boban (Author), Ivanović, Mirjana (Author), Budimac, Zoran (Author), Jain, L. C. (Author)
• Format: Electronic eBook
• Language: English
• Published: Switzerland : Springer, [2016]
• Series: Intelligent systems reference library ; v. 112.
• Subjects: Web-based instruction.; elektronické knihy; electronic books
• ISBN: 9783319411637; 9783319411613
• ISSN: 1868-4394 ;
• Physical Description: 1 online resource (xxiii, 294 pages) : illustrations (some color)

This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific k...

Table of 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.