Intelligent Tutoring Systems: Structure, Applications and Challenges


Robert Kenneth Atkinson (Editor)
The School of Computing, Informatics, and Decision Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA

Series: Education in a Competitive and Globalizing World
BISAC: COM023000

Intelligent Tutoring Systems: Structure, Applications and Challenges contains eight chapters from leading experts from across the globe, chronicling recent developments in the design, development, and implementation of intelligent tutoring systems (ITSs). The chapters bring together viewpoints from diverse contexts including medicine, the military and education. Various types of ITSs are covered, including simulators, virtual environments and game-based systems designed to teach new skills, improve existing ones, and provide assessment, feedback and remediation.

Our contributors provide expertise on topics that include artificial intelligence, case-based reasoning, algorithms, data-mining, predictive student action models, feature extraction, automated assessment engines, system design, natural language processing and classroom implementation. This book discusses challenges and new trends in system design and development, including new methodologies for automation of assessment in ITSs by presenting chapters that focus on system design by presenting frameworks to aid in the development of ITSs, as well as techniques and algorithms useful in implementing systems using data mining, machine learning. Additional chapters give consideration to the inclusion of effective information as an input and part of the student module. Additionally, discussions of system usability including user experience design and testing, in addition to successful system implementation, are covered.

This book will be of interest to a diverse audience, including software developers, educational technologists, computer scientists, and researchers working in artificial learning, as well as interactive and adaptive learning environments. (Imprint: Nova)



Table of Contents


Chapter 1. A Case Based Reasoning Framework to Structure a Knowledge Base in Intelligent Tutoring Systems
Joice Barbosa Mendes, Isabela Neves Drummond and Alexandre Carlos Brandão Ramos (Federal University of Itajuba, UNIFEI, Brazil)

Chapter 2. Intelligent Tutoring Systems in the Medical Domain: Fostering Self-Regulatory Skills in Problem-Solving
Poitras, E., Lajoie, S. P., Jarrell, A., Doleck, T., and Naismith, L. (University of Utah, Utah, USA, and others)

Chapter 3. Predictive Student Action Model for Procedural Training in 3D Virtual Environments
Diego Riofrio-Luzcando and Jaime Ramírez (Universidad Politécnica de Madrid, Spain)

Chapter 4. Intelligent Automated Assessment and Tutoring: A Pairing of an Intelligent Tutoring System with an Automated Assessment Engine for U.S. Navy Shiphandling Training
Alan D. Koenig, John J. Lee and Elizabeth O. Bratt (CRESST/University of California, Los Angeles, California, USA, and others)

Chapter 5. EEG-Based User Performance Prediction Using Random Forest in a Dynamic Learning Environment
Gustavo A. Lujan-Moreno, Robert Atkinson and George Runger (Arizona State University, Arizona, USA)

Chapter 6. Opportunities from Usability Design for Improving Intelligent Tutoring Systems
Rehman Chughtai, Shasha Zhang and Scotty D. Craig (Arizona State University, Arizona, USA)

Chapter 7. Intelligent Tutoring Systems for Literacy: Existing Technologies and Continuing Challenges
Matthew E. Jacovina and Danielle S. McNamara (Arizona State University, Arizona, USA)

Chapter 8. Comparing Learning Outcomes and Implementation Factors from Student-Managed vs. Teacher-Managed Intelligent Tutoring Systems
Kausalai (Kay) Wijekumar, Bonnie J.F. Meyer, Karen R. Harris, Steve Graham, and Andrea Beerwinkle (Texas A&M University, Texas, USA, and others)


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