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Content Differences for Abstract and Concrete Concepts

Cognitive science, 2005-09, Vol.29 (5), p.719-736

2005

Volltextzugriff (PDF)

Deep Comprehension: Multi-Disciplinary Approaches to Understanding, Enhancing, and Measuring Comprehension

1st, 2018

Volltextzugriff (PDF)

AutoTutor: A simulation of a human tutor

Cognitive systems research, 1999, Vol.1 (1), p.35-51

1999

Volltextzugriff (PDF)

Using Latent Semantic Analysis to Evaluate the Contributions of Students in AutoTutor

Interactive learning environments, 2000-08, Vol.8 (2), p.129-147

2000

Volltextzugriff (PDF)

Metacognition, need for cognition and use of explanations during ongoing learning and problem solving

Learning and individual differences, 2005, Vol.15 (4), p.321-337

2005

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Changes in Reading Strategies as a Function of Reading Training: A Comparison of Live and Computerized Training

Journal of educational computing research, 2005, Vol.32 (2), p.185-208

2005

Volltextzugriff (PDF)

How can FACT Encourage Collaboration and Self-Correction?

Deep Comprehension, 2019, p.114-127

1, 2019

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Introduction

Deep Comprehension, 2019, p.xiv-xxi

1, 2019

Volltextzugriff (PDF)

Deep Comprehension of Text Revealed by Talking and Writing While Reading

Deep Comprehension, 2019, p.253-264

1, 2019

Volltextzugriff (PDF)

Deep and Shallow Natural Language Understanding for Identifying Explanation Structure

Deep Comprehension, 2019, p.237-252

1, 2019

Volltextzugriff (PDF)

What are we Reading for?: A Disciplinary Literacy Perspective on Purpose

Deep Comprehension, 2019, p.80-95

1, 2019

Volltextzugriff (PDF)

The Unreasonable Effectiveness of AutoTutor

Deep Comprehension, 2019, p.154-165

1, 2019

Volltextzugriff (PDF)

Big Data for Thick Description of Deep Learning

Deep Comprehension, 2019, p.265-277

1, 2019

Volltextzugriff (PDF)

Learning Tasks In Electronic Environments: Advances Towards Interactive eTextbooks

Deep Comprehension, 2019, p.99-113

1, 2019

Volltextzugriff (PDF)

Eliciting Deeper Evidence through Conversation-Based Assessments

Deep Comprehension, 2019, p.209-223

1, 2019

Volltextzugriff (PDF)

Learning Scientific Inquiry from a Serious Game that uses AutoTutor

Deep Comprehension, 2019, p.180-193

1, 2019

Volltextzugriff (PDF)

Prose Comprehension Beyond the Page

Deep Comprehension, 2019, p.3-15

1, 2019

Volltextzugriff (PDF)

Scaffolding Adult Learners’ Reading Strategies in the Intelligent Tutoring System

Deep Comprehension, 2019, p.166-179

1, 2019

Volltextzugriff (PDF)

A Memory Retrieval View of Text Comprehension

Deep Comprehension, 2019, p.27-40

1, 2019

Volltextzugriff (PDF)

AutoTutor: An Intelligent Tutoring System and Its Authoring Tools

Deep Comprehension, 2019, p.140-153

1, 2019

Volltextzugriff (PDF)

Using Scenario-Based Assessments to Measure Deep Learning

Deep Comprehension, 2019, p.197-208

1, 2019

Volltextzugriff (PDF)

NLP: Getting Computers to Understand Discourse

Deep Comprehension, 2019, p.224-236

1, 2019

Volltextzugriff (PDF)

Standards of Coherence in Reading: Variations in Processing and Comprehension of Text

Deep Comprehension, 2019, p.41-51

1, 2019

Volltextzugriff (PDF)

Reusing Neural Networks for Deep Comprehension

Deep Comprehension, 2019, p.68-79

1, 2019

Volltextzugriff (PDF)

What do we Think About When we Learn?

Deep Comprehension, 2019, p.52-67

1, 2019

Volltextzugriff (PDF)

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Content Differences for Abstract and Concrete Concepts

Deep Comprehension: Multi-Disciplinary Approaches to Understanding, Enhancing, and Measuring Comprehension

AutoTutor: A simulation of a human tutor

Using Latent Semantic Analysis to Evaluate the Contributions of Students in AutoTutor

Metacognition, need for cognition and use of explanations during ongoing learning and problem solving

Changes in Reading Strategies as a Function of Reading Training: A Comparison of Live and Computerized Training

How can FACT Encourage Collaboration and Self-Correction?

Introduction

Deep Comprehension of Text Revealed by Talking and Writing While Reading

Deep and Shallow Natural Language Understanding for Identifying Explanation Structure

What are we Reading for?: A Disciplinary Literacy Perspective on Purpose

The Unreasonable Effectiveness of AutoTutor

Big Data for Thick Description of Deep Learning

Learning Tasks In Electronic Environments: Advances Towards Interactive eTextbooks

Eliciting Deeper Evidence through Conversation-Based Assessments

Learning Scientific Inquiry from a Serious Game that uses AutoTutor

Prose Comprehension Beyond the Page

Scaffolding Adult Learners’ Reading Strategies in the Intelligent Tutoring System

A Memory Retrieval View of Text Comprehension

AutoTutor: An Intelligent Tutoring System and Its Authoring Tools

Using Scenario-Based Assessments to Measure Deep Learning

NLP: Getting Computers to Understand Discourse

Standards of Coherence in Reading: Variations in Processing and Comprehension of Text

Reusing Neural Networks for Deep Comprehension

What do we Think About When we Learn?

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