E-learning systems : intelligent techniques for personalization /: intelligent techniques for personalization. ([2016])
- Record Type:
- Book
- Title:
- E-learning systems : intelligent techniques for personalization /: intelligent techniques for personalization. ([2016])
- Main Title:
- E-learning systems : intelligent techniques for personalization
- Further Information:
- Note: Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain.
- Authors:
- Klašnja-Milićević, Aleksandra
Vesin, Boban
Ivanović, Mirjana
Budimac, Zoran
Jain, L. C - 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 inForeword; 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. … (more)
- Publisher Details:
- Switzerland : Springer
- Publication Date:
- 2016
- Copyright Date:
- 2017
- Extent:
- 1 online resource (xxiii, 294 pages), illustrations (some color)
- Subjects:
- 371.35/8
Engineering
Web-based instruction
Education
Artificial intelligence
EDUCATION -- Administration -- General
EDUCATION -- Organizations & Institutions
Web-based instruction
Education -- Computers & Technology
Computers -- Information Technology
Computers -- Intelligence (AI) & Semantics
Educational equipment & technology, computer-aided learning (CAL)
Information retrieval
Artificial intelligence
Electronic books - Languages:
- English
- ISBNs:
- 9783319411637
3319411632 - Related ISBNs:
- 9783319411613
- Notes:
- Note: Includes bibliographical references.
Note: Online resource; title from PDF title page (SpringerLink, viewed July 29, 2016). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.356529
- Ingest File:
- 02_339.xml