This volume provides an overview of research from the learning sciences into understanding, enhancing, and measuring "deep comprehension" from a psychological, educational, and psychometric perspective. It describes the characteristics of deep comprehension, what techniques may be used to improve it, and how deep levels of comprehension may be distinguished from shallow ones. It includes research on personal-level variables; how intelligent tutors promote comprehension; and the latest developments in psychometrics. The volume will be of interest to senior undergraduate and graduate students of cognitive psychology, learning, cognition and instruction, and educational technology.
Part 1: Understanding Deep Comprehension
1. Prose Comprehension Beyond the Page
Jennifer Wiley and Tricia A. Guerrero
2. Prose Comprehension Beyond the Word Revisited
Rolf A. Zwaan
3. A Memory Retrieval View of Text Comprehension
Debra L. Long and Erin M. Freed
4. Standards of Coherence in Reading: Variations in Processing and Comprehension of Text
Marja Oudega and Paul van den Broek
5. What do We Think About When We Learn?
Sidney K. D’Mello
6. Reusing Neural Networks for Deep Comprehension
Manuel de Vega and David Beltran
7. What are We Reading for? A Disciplinary Literacy Perspective on Purpose
Susan R. Goldman and MariAnne George
Part 2: Using Technology to Increase Deep Comprehension
8. Learning Tasks in Electronic Environments: Advances Towards Interactive eTextbooks
Eduardo Vidal-Abarca et al.
9. How can FACT Encourage Collaboration and Self-correction?
Kurt VanLehn, et al.
10. Design Principles for Virtual Humans in Educational Technology Environments
Scotty D. Craig and Noah L. Schroeder
11. AutoTutor: An Intelligent Tutoring System and its Authoring Tools
Zhiqiang Cai and Xiangen Hu
12. The Unreasonable Effectiveness of AutoTutor
Andrew M. Olney
13. Scaffolding Adult Learners’ Reading Strategies in the Intelligent Tutoring System
Haiying Li and Whitney Baer
14. Learning Scientific Inquiry from a Serious Game that Uses AutoTutor
Keith Millis, et al.
Part 3: Measuring Deep Comprehension
15. Using Scenario-based Assessments to Measure Deep Learning
O’Reilly, John Sabatini, and Zuowei Wang
16. Eliciting Deeper Evidence through Conversation-Based Assessments
Blair Lehman and G. Tanner Jackson
17. NLP: Getting Computers to Understand Discourse
Danielle S. McNamara, et al.
18. Deep and Shallow Natural Language Understanding for Identifying Explanation Structure
Peter Hastings, et al.
19. Deep Comprehension of Text Revealed by Talking and Writing While Reading
Joseph P. Magliano, Karyn Higgs, and Keith Millis
20. Big Data for Thick Description of Deep Learning
David Williamson Shaffer
One of the hallmarks of an educated mind is the ability to think critically and analytically about what is read, seen, or heard; in effect, to look beyond the words to the message that lies within. That very ability is the essence of deep comprehension and the theme that weaves through the pages of this thoughtful and invaluable book by Millis, Long, Magliano, and Wiemer. Given the complex and often confusing world in which we find ourselves, there is no more essential topic to be explored than what it means to understand text—in its many forms—deeply. This edited volume not only seeks to address that timely topic, but also to arm educators with the knowledge required to measure depth of understanding accurately and to facilitate students’ engagement in deep learning effectively. There is no more thorough treatment of these vital topics than Deep Comprehension—a must read for anyone who values the ability to think beyond the words read, seen, or heard to the message that lies within. Professor Patricia Alexander, Ph.D., University of Maryland, College Park