E-learning systems : intelligent techniques for personalization /

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Bibliographic Details
Imprint:Switzerland : Springer, [2016]
Description:1 online resource (xxiii, 294 pages) : illustrations (some color)
Series:Intelligent systems reference library, 1868-4394 ; volume 112
Intelligent systems reference library ; v. 112.
Subject:Web-based instruction.
Educational equipment & technology, computer-aided learning (Calif.)
Information retrieval.
Artificial intelligence.
EDUCATION -- Administration -- General.
EDUCATION -- Organizations & Institutions.
Web-based instruction.
Electronic books.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11264788
Hidden Bibliographic Details
Other authors / contributors:Klašnja-Milićević, Aleksandra, author.
Vesin, Boban, author.
Ivanović, Mirjana, author.
Budimac, Zoran, author.
Jain, L. C., author.
Notes:Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed July 29, 2016).
Summary: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 knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques. .
Other form:Print version: Klasnja-Milićević, Aleksandra E-Learning Systems : Intelligent Techniques for Personalization Cham : Springer,c2016 9783319411613
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; S1. IEEE LOM and IMS Learning Resource Metadata; S2. Dublin Core Metadata Initiative; S3. IMS Learner Information Package; S4. IMS Content Packaging; S5. IMS Simple Sequencing; 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; 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; SVD Algorithm; Tensors and HOSVD Algorithm; Ranking with Tensor Factorization; Multi-mode Recommendations; 7.4.5 Most Popular Tags.