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Digital Knowledge Maps in Education : Technology-Enhanced Support for Teachers and Learners

By: Ifenthaler, Dirk.
Contributor(s): Hanewald, Ria.
Material type: TextTextSeries: eBooks on Demand.Publisher: Dordrecht : Springer, 2013Description: 1 online resource (414 p.).ISBN: 9781461431787.Genre/Form: Electronic books.Additional physical formats: Print version:: Digital Knowledge Maps in Education : Technology-Enhanced Support for Teachers and LearnersDDC classification: 378.1734 LOC classification: LB2331.D54 20Online resources: Click here to view this ebook.
Contents:
Preface; About the Authors; Reviewers; Acknowledgements; Contents; Part I: Digital Knowledge Maps in Open,Distance, and Flexible Learning Contexts; Chapter 1: Digital Knowledge Mapping in Educational Contexts; 1 History of Concept Maps; 2 Various Types of Maps, Characteristics and Definitions; 3 Anatomy and Terminology of a Map; 4 Guidelines for Creating Knowledge Maps; 5 Approaches to Mapping; 6 Individual Mapping Versus Collaborative Mapping; 7 Comparison of Manual Versus Digital Maps; 8 Examples of Mapping Software; 9 The Current Knowledge Base; 10 Conclusion; References
Chapter 2: Making Sense of Knowledge Integration Maps1 Introduction; 1.1 Concept Maps and Knowledge Integration; 2 Knowledge Integration Map; 2.1 Forms of KIM Analysis; 2.2 Quantitative Concept Map Analysis; 2.2.1 Benchmark KIM; 2.2.2 Indicator Concepts; 2.2.3 Essential Connections; 2.2.4 KI-Rubric for Concept Maps; 2.2.5 Concept Placement Analysis; 2.2.6 Primary Analysis Variables; 2.2.7 KIM Secondary Analysis Variables; 2.3 KIM Network Analysis; 2.4 Qualitative KIM Analysis; 2.4.1 KIM Topological Analysis; 2.4.2 Qualitative Proposition-Type Analysis; 3 Discussion and Implications
3.1 KIM Analysis and Benchmark MapsReferences; Chapter 3: Concept Maps for Comprehension and Navigation of Hypertexts; 1 Introduction; 2 Concept Maps and Theoretical Backgrounds Used in Hypertexts; 2.1 Diversity of Concept Maps in Hypertexts; 2.2 Theoretical Frameworks; 3 Functions and Criteria of Effective Concept Maps to Support Learning from Hypertexts; 3.1 Concept Maps as Content Overviews Supporting the Organization of Mental Representations; 3.2 Concept Maps Reduce the Navigation Requirements While Guiding Learners Within the Semantic Space
4 Conditions Under Which Concept Maps Are Effective4.1 Concept Maps Should Convey Semantic Organization of Hypertext Content; 4.2 Hierarchical Structures Are More Effective than Network Structures; 4.3 Processing Concept Maps Early During the Learning Task; 4.4 Individual Differences and Concept Maps; 4.4.1 Prior Domain Knowledge; 4.4.2 Other Abilities; 5 Discussion and Conclusion; 5.1 Educational Implications; 5.2 Further Research; References; Chapter 4: Using Digital Knowledge Maps for Supporting Tutors Giving Effective Explanations; 1 Introduction
2 Tutors' Expertise and the Effectiveness of Instructional Explanations3 Effective Instructional Explanations; 4 Assessment of Tutor's Explanations with Knowledge Maps; 4.1 Analysis of Explanatory Generality; 4.1.1 Omission of Concepts; 4.1.2 Structural Complexity; 4.2 Coherence; 4.2.1 Interrelatedness of Concepts; 4.2.2 Fragmentation; 5 Empirical Findings for the Effectiveness of Knowledge Maps for Assessing Tutors' Explanations; 5.1 Using Knowledge Maps for Investigating Expertise-Related Features of Explanations; 5.1.1 Participants and Research Design; 5.1.2 Materials and Procedure
5.1.3 Summary of Results
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
LB2331.D54 20 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1592676 Available EBL1592676

Preface; About the Authors; Reviewers; Acknowledgements; Contents; Part I: Digital Knowledge Maps in Open,Distance, and Flexible Learning Contexts; Chapter 1: Digital Knowledge Mapping in Educational Contexts; 1 History of Concept Maps; 2 Various Types of Maps, Characteristics and Definitions; 3 Anatomy and Terminology of a Map; 4 Guidelines for Creating Knowledge Maps; 5 Approaches to Mapping; 6 Individual Mapping Versus Collaborative Mapping; 7 Comparison of Manual Versus Digital Maps; 8 Examples of Mapping Software; 9 The Current Knowledge Base; 10 Conclusion; References

Chapter 2: Making Sense of Knowledge Integration Maps1 Introduction; 1.1 Concept Maps and Knowledge Integration; 2 Knowledge Integration Map; 2.1 Forms of KIM Analysis; 2.2 Quantitative Concept Map Analysis; 2.2.1 Benchmark KIM; 2.2.2 Indicator Concepts; 2.2.3 Essential Connections; 2.2.4 KI-Rubric for Concept Maps; 2.2.5 Concept Placement Analysis; 2.2.6 Primary Analysis Variables; 2.2.7 KIM Secondary Analysis Variables; 2.3 KIM Network Analysis; 2.4 Qualitative KIM Analysis; 2.4.1 KIM Topological Analysis; 2.4.2 Qualitative Proposition-Type Analysis; 3 Discussion and Implications

3.1 KIM Analysis and Benchmark MapsReferences; Chapter 3: Concept Maps for Comprehension and Navigation of Hypertexts; 1 Introduction; 2 Concept Maps and Theoretical Backgrounds Used in Hypertexts; 2.1 Diversity of Concept Maps in Hypertexts; 2.2 Theoretical Frameworks; 3 Functions and Criteria of Effective Concept Maps to Support Learning from Hypertexts; 3.1 Concept Maps as Content Overviews Supporting the Organization of Mental Representations; 3.2 Concept Maps Reduce the Navigation Requirements While Guiding Learners Within the Semantic Space

4 Conditions Under Which Concept Maps Are Effective4.1 Concept Maps Should Convey Semantic Organization of Hypertext Content; 4.2 Hierarchical Structures Are More Effective than Network Structures; 4.3 Processing Concept Maps Early During the Learning Task; 4.4 Individual Differences and Concept Maps; 4.4.1 Prior Domain Knowledge; 4.4.2 Other Abilities; 5 Discussion and Conclusion; 5.1 Educational Implications; 5.2 Further Research; References; Chapter 4: Using Digital Knowledge Maps for Supporting Tutors Giving Effective Explanations; 1 Introduction

2 Tutors' Expertise and the Effectiveness of Instructional Explanations3 Effective Instructional Explanations; 4 Assessment of Tutor's Explanations with Knowledge Maps; 4.1 Analysis of Explanatory Generality; 4.1.1 Omission of Concepts; 4.1.2 Structural Complexity; 4.2 Coherence; 4.2.1 Interrelatedness of Concepts; 4.2.2 Fragmentation; 5 Empirical Findings for the Effectiveness of Knowledge Maps for Assessing Tutors' Explanations; 5.1 Using Knowledge Maps for Investigating Expertise-Related Features of Explanations; 5.1.1 Participants and Research Design; 5.1.2 Materials and Procedure

5.1.3 Summary of Results

Description based upon print version of record.

Author notes provided by Syndetics

Dr. Ifenthaler's research interests focus on the learning-dependent progression of mental models, problem solving, decision making, situational awareness, game-based learning, and emotions. He developed an automated and computer-based methodology for the assessment and analysis of graphical and natural language representations (SMD Technology, HIMATT, AKOVIA). Additionally, he developed components of course management software and an educational simulation games (DIVOSA, SEsim). He is also interested in the development of educational software and learning management systems (LMS). Dr. Hanewald has worked on funded research projects investigating 'Pedagogical Agents for Modelling On-Line and Computer-Interactive Learning' (PEDANT) and 'Reusable Learning Objects' (RELO). She has also conducted research into the use of ICT in education, including online and mobile technologies with several grants for projects in these areas. Her contribution to the wider community includes being co-editor for the ICT in Education Journal (2007-2010), serving as a state council committee member of the Computer in Education Group of Victoria (2006-2010), a Consulting Editor for the Australian Journal of Teacher Education (2007-present)and an Editorial Advisory Board Member for the Handbook on Collaborative Learning using Concept Mapping (2010).

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