Contents

Foreword

Reference

Preface

Acknowledgments

Section I

Digital Technology and Deep Learning: Multiple Theoretical Perspectives

Chapter 1

The Psychology of Deep Learning

Abstract

1. What is Learning?

2. The Cognitive Psychology of Human Memory

2.1. Short-Term Memory: The Modal Model

2.2. Levels of Processing

2.3. Working Memory

2.4. Long-Term Working Memory

2.4.1. Long-Term Memory: Semantic versus Episodic Memory

2.4.2. Long-Term Memory: Large Scale Structures

2.5. Implicit Memory

2.6. Declarative versus Procedural Memory and Skill Acquisition

2.7. Deep Learning and Memory

2.7.1. Retrieval Cues

2.7.2. Organizing Information

2.8. Engaging Learners and Processing for Meaning

2.8.1. Building Procedural Skills

2.9. Text Processing and Deep Learning

2.9.1. The Construction-Integration (CI) Model

2.9.2. Implications of the CI Model for Deep Learning

3. Educational Psychology, Instructional Design, and Deep Learning

3.1. Scaffolded Knowledge Integration Framework

3.2. Multimedia Learning

3.3. Problem- and Project-Based Learning

3.4. Implications for Deep Learning from Educational Psychology and Instructional Design

3.5. Enhancing Deep Learning: Takeaways from Psychology

References

Chapter 2

Promoting and Assessing Deep Learning Using Technology

Abstract

1. Introduction

2. Complex Problem Solving

2.1. Complex Educational Systems

3. Holistic Perspectives of Learning

4. Assessment and Evaluation

5. An Illustrative Example

Concluding Remarks

References

Chapter 3

Fostering Self-Regulated Learning with Digital Technologies

Abstract

1. Introduction

2. Background/Theoretical Framework

2.1. Background

2.2. Research on the Role of LMS Web-Based Pedagogical Tools on SRL Processes

2.3. Research on the Role of Web 2.0 Technologies on SRL Processes

2.4. Research on the Role of e-Rubrics and e-Portfolios on SRL Processes

2.5. Development of Self-Regulatory Functioning

2.6. Self-Regulatory Functioning in Digital Learning Environments

3. Conclusion and Future Directions

References

Chapter 4

Online Discussion Structure and Instructor Roles for the Promotion of Deep Learning

Abstract

1. Introduction

2. Framework for Online Discussion

3. Framework Application

3.1. Online Environment

3.2. Instructor Role

3.2.1. Structured Task Discussion - Instructor as Project Lead

3.2.2. Structured Topic Discussion - Instructor as Role Model

3.2.3. Collaborative Task Discussion ­ Instructor as Discussant

4. Outcomes

Conclusion

References

Section II

Strategies for Promoting Deep Learning with Digital Technology

Chapter 5

Multimedia Simulations That Foster Transfer: Findings from a Review of the Literature

Abstract

1. Introduction

1.1. Transfer as an Outcome for Learning with Simulations

1.2. Feedback and Its Role in Transfer of Learning from Computer-Based Learning Environments

2. Method

2.1. Literature Search

2.2. Criteria for Inclusion

2.3. Study Characteristics

3. Results

3.1. Sample

3.2. Transfer from Business-as-Usual Control Group Designs

3.3. Transfer from Active Control Group Designs

Conclusion

5. Implications for Research, Design, and Teaching with Simulations

References

Chapter 6

Visualizations for Deep Learning: Using 3D Models to Promote Scientific Observation and Reasoning during Collaborative STEM Inquiry

Abstract

1. Introduction

2. Research Quest

2.1. Critical Thinking Processes Supported by Research Quest

2.2. Evidence-Based Reasoning in Research Quest

2.3. Use of 3D Virtual Models in Research Quest

3. Learning with 3D Models

3.1. Concrete 3D Models

3.2. Virtual 3D Models

3.3. 3D Models in Museum Education

4. Classroom Implementation

5. Interaction Behaviors and Critical Thinking Processes

5.1. Interaction Behaviors during 3D Virtual Model Use

5.2. Critical Thinking Processes during 3D Virtual Model Use

6. Discussion and Practical Implications

7. Limitations

Conclusion

References

Chapter 7

Strategies for Designing Advanced Learning Technologies to Foster Self-Regulated Learning

Abstract

1. Introduction

2. Theoretical Frameworks for Designing Advanced Learning Technologies

2.1. Mayer’s Cognitive Theory of Multimedia Learning

2.2. Information Processing Theory of SRL including Macro vs. Micro Level SRL Processes

2.3. Using Theories of SRL to Design MetaTutor, a Multi-Agent Hypermedia-Based Intelligent Tutoring System

2.4. Norman’s Usability Principles: Implications for Designing ALTs

3. A Review of Design Strengths and Weaknesses in ALTs

3.1. MetaTutor

3.2. MetaTutor: Intelligent Virtual Human (IVH)

3.3. SimSelf: A Multi-Agent System Designed to Foster SRL while Fostering Ecological Understanding

3.4. Crystal Island: A Game-Based Learning Environment for Science Learning

4. Challenges for Developing Adaptive ALTs

4.1. Issues of Adaptivity

4.2. Making ALTs Adaptive Based on Multi-Channel Data

4.2.1. Multi-Channel Data

4.2.2. Student-System Dialogues

4.3. How Do Adaptive ALTs Enhance SRL?

Conclusion

Acknowledgments

References

Chapter 8

nBrowser: An Intelligent Web Browser for Studying Self-Regulated Learning in Teachers’ Use of Technology

Abstract

1. Introduction

2. An Argument for Metacognitive Tools in Teacher Professional Development

3. Student Teachers’ Self-Regulated Learning in Instructional Planning

4. Network-Based Approach to Designing Adaptive Metacognitive Tools

5. Instructional Principles for Network-Based Tutors

6. Preliminary Findings on Instructional Principles for Network-Based Tutors

Conclusion

References

Chapter 9

An Empirical Examination of Goals, Student Approaches to Learning, and Adaptive Outcomes

Abstract

1. Introduction

Theoretical Overview

2. The Importance of Mastery Goals

3. SAL Framework

4. Academic Engagement in Educational Contexts

5. The Present Study

6. Method

6.1. Sample

6.2. Instruments

6.2.1. Academic Engagement

6.2.2. Achievement Goals

6.2.3. Deep and Surface Levels of Processing

6.2.4. Academic Achievement

6.3. Results

6.3.1. Structural Relations between the Variables

6.3.2. Mediating Test

6.3.3. The Testing of Absorption

7. Discussion

7.1. Predictive Influences of Mastery Goals

7.2. Levels of Processing: Associations with Engagement and Achievement

7.3. Engagement and Academic Achievement

7.4. Indirect Effects

Conclusion

Implications and Directions for Further Research Development

References

Section III

Case Studies in Digital Technology and Deep Learning

Chapter 10

Supporting Students’ Reflective Practice Using the OneNote Class Notebook and Scaffolding

Abstract

1. Introduction

2. Reflective Skills as a Key Goal in Higher Education

3. Learning, Teaching and Assessment Activities to Foster Reflective Processes

3.1. Designing Reflective Learning Activities

3.2. Scaffolding as Guidance in Reflective Practice

3.3. Assessing Reflective Competencies

4. E-Portfolios as an Approach to Foster Reflection

4.1. E-Portfolios as a Digital Tool

4.2. E-Portfolios as a Didactic Approach

5. Implementing OneNote Class Notebook in an Educational Science Bachelors Course

5.1. Scaffolding with the OneNote Class Notebook

5.2. Example of a Course Sequence

6. Outcomes

6.1. Descriptive Analysis

6.2. Quality Analysis

6.3. Content Analysis

7. Discussion and Conclusion

7.1. Interpretation and Discussion of the Results

7.2. Conclusion for the Further Development of the Course Design

7.3. Conclusion for Deep Learning Scenarios

References

Chapter 11

Exploiting Innovative Technology-Enhanced Learning Environments for Teacher Professional Development

Abstract

1. Introduction

2. TELEs and TPD

3. MUVES and TPD – Conceptual Model: Cognitive Apprenticeship Model

3.1. MUVEs as Social Virtual Learning Environments (VLEs)

3.2. Rationale of the Study

3.3. Conceptual Framework

3.4. Conclusion

4. E-Portfolio and TPD – Conceptual Model: Self-Regulated Learning

4.1. E-Portfolios as an Academic and Professional Context

4.2. Rationale of the Study

4.3. Conceptual Framework

4.4. Conclusion

Conclusions and Future Directions

References

Chapter 12

Innovative Technologies-Embedded Scientific Inquiry Practices: A Socially Situated Cognition Theory

Abstract

1. Introduction

1.1. Chapter Objectives

2. Background/Theoretical Framework

2.1. Socially Situated Cognition

2.2. Scientific Communities of Practice

2.3. Scientific Practices

3. Methodology

3.1. Design

3.2. Data Collection and Analysis

4. Results and Discussion

4.1. Technology-Embedded Scientific Conceptualization

4.1.1. Data Logging and Graphing

4.1.2. Modeling, Animating, Visualizing, and Simulating

4.1.3. Conceptions and Conceptual Change

4.1.4 Inquiry

4.2. Technology-Embedded Scientific Investigation

4.3. Technology-Embedded Scientific Communication

5. Conclusion

6. Implications

6.1. Relevance for Scientific Practices

6.2. Relevance for Developing Critical Thinking

Acknowledgments

References

Chapter 13

Fostering Deep Learning in an Online Learning Environment

Abstract

1. Introduction

2. Transactional Distance Theory and Deep Learning

3. Empirical Studies of Transactional Distance Theory in the Online Learning Environment

4. Self-Regulated Learning Theory and Deep Learning

4.1. Self-Regulated Learning Theory

4.2. Self-Efficacy

4.3. Self-Monitoring

4.4. Other Factors in Self-Regulation Process

5. Empirical Studies of Self-Regulated Learning in Online Learning Environment

Conclusion and Recommendations

References

Chapter 14

A Study on Deep Learning and Mental Reasoning in Digital Technology in Relation to Cognitive Load

Abstract

1. Introduction

2. Theoretical Background

2.1. Deep and Shallow Learning

2.1.1. Marton and Säljö’s Level of Cognitive Processes

2.1.2. Chi’s Active-Constructive-Interactive Framework

2.2. General Mental Reasoning and Deep Learning in Science

2.2.1. Multiple Rule-Based Reasoning and Science Learning

2.2.2. Challenges in Training Multiple Rule-Based Problem Reasoning

2.3. Cognitive Load Theory and Challenges

2.3.1. Multiple-Rule Based Problems and Intrinsic Cognitive Load

2.3.2. Individual Differences

2.3.3. Multiple Rule-Based Problem Solving, Cognitive Load, and Media

3. Present Study

3.1. Description of the Study

3.2. Hypothesis

3.3. Method of the Study

3.3.1. Participants

3.3.2. Instrumentation

3.3.3. Procedure

4. Results

4.1. Data Analysis

4.2. Findings

4.2.1. Effects of Interactive Digital Technology

4.2.2. Difficulty of Problem

4.2.3. Specific Load Difference for Problem

4.2.4. Gender Differences

5. Discussion

5.1. Limitations and Implications

Conclusion

References

Concluding Remarks

References

About The Editor

List of Contributors

Index

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