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基于境脈感知的同伴推薦研究(英文)

基于境脈感知的同伴推薦研究(英文)

定 價(jià):¥36.00

作 者: 鄭燕林 著
出版社: 吉林大學(xué)出版社
叢編項(xiàng):
標(biāo) 簽: 計(jì)算機(jī)理論

ISBN: 9787560150413 出版時(shí)間: 2009-12-01 包裝: 平裝
開本: 16開 頁數(shù): 202 字?jǐn)?shù):  

內(nèi)容簡(jiǎn)介

  This book is organized as follows. Chapter 1 introduces the research background and purposes of this study. Chapter 2 recognizes the theoretical foundations for this study. Chapter 3 explores the e-learning context.Chapter 4 discusses peer interaction in the e-learning context, and suggests that effective peer interaction should be built on the considerations of knowledge relevance, social proximity and technical access between participants. Chapter 5 proposes a three-dimensional CA model for peer recommendation. Chapter 6 describes a CA-supported peer recommendation mechanism, which is based on mining learners activity contexts in e-learning spaces. Chapter 7 presents a prototype case study (PeerFinder), which implements CA in a weblog system. Further discussion is provided in Chapter 8.

作者簡(jiǎn)介

  鄭燕林,女,1974年10月15日生,1997年獲東北師范大學(xué)理學(xué)士,2000年獲東北師范大學(xué)教育學(xué)碩士學(xué)位,2002年獲日本文部省獎(jiǎng)學(xué)金公派留學(xué)日本,于2006年獲得日本國立德島大學(xué)信息系統(tǒng)工學(xué)博士學(xué)位。自2000年在東北師范大學(xué)任教?,F(xiàn)為東北師范大學(xué)傳媒科學(xué)學(xué)院教育技術(shù)學(xué)系副教授、副系主任、碩士生導(dǎo)師。目前主要從事CSCL、泛在學(xué)習(xí)(ubiquitous learning)系統(tǒng)設(shè)計(jì)與開發(fā)、網(wǎng)絡(luò)環(huán)境下知識(shí)管理能力發(fā)展相關(guān)研究。近年來,共發(fā)表中英文學(xué)術(shù)論文50余篇,其中包括SSCI、EI、ISTP、CSSCI檢索論文。

圖書目錄

Chapter 1 Introduction
1.1 Research Background
1.2 Purpose of this Study
1.3 Book Overview
Chapter 2 Theoretical Foundation of this Study
2.1 Social Constructivism Theory
2.2 Social Information Processing Theory
2.3 Activity Theory
Chapter 3 The e-Learning Context
3.1 Two Concepts
3.1.1 Context
3.1.2 e-Learning
3.2 Framework Description of the e-Learning Context
3.2.1 Key Elements in e-Learning
3.2.2 Learning Process based on Knowledge Transfer
3.2.3 Knowledge Goals of e-Learning
3.3 Knowledge Context
3.4 Social Context
3.5 Technical Context
3.6 Mediator: Activity Context
3.7 Modeling Knowledge, Social, Technical Contexts
Chapter 4 Peer Interaction in the e-Learning Context
4.1 Interaction
4.2 Peer Interaction
4.3 Computer-Mediated Communication
4.4 Computer-supported Collaborative Learning
4.5 Virtual Learning Community
4.6 Support Potential Online Peer Interaction
4.6.1 Knowledge Relevance
4.6.2 Social Proximity
4.6.3 Technical Mediation
4.7 Motivation and Engagement in Peer Interaction
Chapter 5 Three-dimensional Context-awareness Model for Peer Recommendation
5.1 Literatures on Awareness
5.1.1 The Concept of Awareness
5.1.2 Classifications of Awareness
5.1.3 Awareness Models
5.1.4 Awareness Technology
5.1.5 Knowledge Awareness
5.1.6 Context-awareness and its Applications
5.2 Three-dimensional Context-awareness Model for Peer Recommendation
5.2.1 CA to Knowledge Relevance
5.2.2 CA to Social Proximity
5.2.3 CA to Technical Access
5.3 A Case Study of Learners Percepti9ns toward the CA Model
5.3.1 Methodology
5.3.2 Results of Questionnaires
5.3.3 Findings
Chapter 6 Context-awareness Supported Peer Recommendation Mechanism based on Mining Activity Context
6.1 Literacy Review on Recommender Systems
6.1.1 Information Filtering
6.1.2 Content-based Approach for Recommendation
6.1.3 Collaborative Filtering Approach for Recommendation
6.1.4 SNA-based Approach for Recommendation
6.1.5 Cold-start Problem in Recommender Systems
6.1.6 Recommender System Assessment
6.2 Context Management
6.3 Activity Context
6.3.1 Classifications of Activity Contexts
6.3.2 Modeling Learning Activity Context
6.3.3 Content Analysis of Learning Activity
6.4 Framework Description of CA-supported Peer Recommendation Mechanism
6.5 User Modeling
6.6 Context Monitoring
6.7 Context Filtering
6.8 CA Visualization
6.8.1 Information Visualization
6.8.2 Social Visualization
6.8.3 CA Visualization
Chapter 7 A Case Study: PeerFinder
7.1 Social Media
7.2 Weblogs as the Test-bed
7.3 The Design Space for Helping Learners Find Suitable Helpers
7.4 System Configuration of PeerFinder
7.5 Learning Activity Contexts in Weblogs
7.6 Content Analysis in PeerFinder
7.7 Recommendation Flow of PeerFinder
7.8 Basis Strategies for Context Filtering in PeerFinder
7.9 Mining Technical Context
7.10 Indicators for CA Filtering
7.11 Computing the Similarity among Learners
Chapter 8 Discussions
8.1 The Extensibility and Flexibility of the CA Model
8.2 The Feasibility and Validity of the CA-supported Peer Recommendation Mechanism
8.3 More Than Peer Recommendation
8.3.1 Inspiration for the Construction of Learning Resources
8.3.2 Decreasing the Learning Curve of New Learners
8.3.3 Motivating Active and Potential Peer Interaction
8.3.4 Inspirations for the Cultivation and hnprovement of Online Learners Learning Ability
Chapter 9 Conclusions and Future Research
9.1 Conclusions
9.2 Future Research
Bibliography

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