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Using Games and Simulations for Teaching and Assessment
Incorporates several innovative and increasingly popular subject areas, including the gamification of education, assessment, and STEM subjects Combines research and authorship from both civilian and military worlds as well as interdisciplinary fields Rigorously defines and analyzes the criteria of selecting, designing, implementing, and evaluating emerging educational technologies while offering implications for future use
Design Recommendations for Intelligent Tutoring System - Volume 5: Assessment Methods
This book is the fifth in a planned series of books that examine key topics (e.g., learner modeling, instructional strategies, authoring, domain modeling, assessment, impact on learning, team tutoring, machine learning, and potential standards) in intelligent tutoring system (ITS) design through the lens of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg & Holden, 2012; Sottilare, Brawner, Sinatra, & Johnston, 2017). GIFT is a modular, service-oriented architecture created to reduce the cost and skill required to author ITSs, manage instruction within ITSs, and evaluate the effect of ITS technologies on learning, performance, retention, transfer of skills, and other instructional outcomes. Along with this volume, the first four books in this series, Learner Modeling (ISBN 978-0-9893923-0-3), Instructional Management (ISBN 978-0-9893923-2-7), Authoring Tools (ISBN 978-0-9893923-6-5) and Domain Modeling (978-0-9893923-9-6) are freely available at www.GIFTtutoring.org and on Google Play.
Army RD & A.
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Design Recommendations for Intelligent Tutoring Systems
Design Recommendations for Intelligent Tutoring Systems (ITSs) explores the impact of intelligent tutoring system design on education and training. Specifically, this volume examines “Authoring Tools and Expert Modeling Techniques”. The “Design Recommendations book series examines tools and methods to reduce the time and skill required to develop Intelligent Tutoring Systems with the goal of improving the Generalized Intelligent Framework for Tutoring (GIFT). GIFT is a modular, service-oriented architecture developed to capture simplified authoring techniques, promote reuse and standardization of ITSs along with automated instructional techniques and effectiveness evaluation capabilities for adaptive tutoring tools and methods.
Learning Engineering Toolkit
The Learning Engineering Toolkit is a practical guide to the rich and varied applications of learning engineering, a rigorous and fast-emerging discipline that synthesizes the learning sciences, instructional design, engineering design, and other methodologies to support learners. As learning engineering becomes an increasingly formalized discipline and practice, new insights and tools are needed to help education, training, design, and data analytics professionals iteratively develop, test, and improve complex systems for engaging and effective learning. Written in a colloquial style and full of collaborative, actionable strategies, this book explores the essential foundations, approaches, and real-world challenges inherent to ensuring participatory, data-driven, learning experiences across populations and contexts. "Introduction: What Is Learning Engineering?", "Chapter 1: Learning Engineering is a Process", and "Chapter 2: Learning Engineering Applies the Learning Sciences" are freely available as downloadable Open Access PDFs at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
Design Recommendations for Intelligent Tutoring Systems
Design Recommendations for Intelligent Tutoring Systems explores the impact of computer-based tutoring system design on education and training. Specifically, this volume, “Learner Modeling” examines the fundamentals of learner modeling and identifies best practices, emerging concepts and future needs to promote efficient and effective tutoring. Part of our design recommendations include current, projected, and needed capabilities within the Generalized Intelligent Framework for Tutoring (GIFT), an open source, modular, service-oriented architecture developed to promote simplified authoring, reuse, standardization, automated instruction and evaluation of tutoring technologies.
Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization
This book on data visualization is the eighth in a planned series of books that examine key topics (e.g., learner modeling, instructional strategies, authoring, domain modeling, assessment, team tutoring, self-improving systems, data visualization, and competency based scenario design) in intelligent tutoring system (ITS) design. This book focuses on data visualization and how it is applied in ITSs. The chapters within this book specifically examine topics in relationship to the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg & Holden, 2012; Sottilare, Brawner, Sinatra, & Johnston, 2017). GIFT is an open-source, domain-independent, modular, service-oriente...
Army RD & A Bulletin
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Proceedings of the Second Annual GIFT Users Symposium
This book contains the proceedings of the second annual Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium conducted in June 2014 at Carnegie Mellon University in Pittburgh, Pennsylvania, USA. GIFT, the Generalized Intelligent Framework for Tutoring, is a modular, service-oriented architecture developed to lower the skills and time needed to author effective adaptive training. Design goals for GIFT also include capturing best instructional practices, promoting standardization and reuse for adaptive instructional content and methods and evaluation of the effectiveness of tutoring technologies. Truly adaptive systems make intelligent (optimal) decisions about tailoring instruction in real-time and make these decisions based on information about the learner and the instructional context (training environment. The papers presented by experts in the field include authoring, ITS interoperability, user perspectives of GIFT, non-cognitive factors in intelligent tutoring, teachable agents, dialogue-based tutoring methods, and intelligent tutoring for teams.
Artificial Intelligence in Education
This two-volume set LNAI 12163 and 12164 constitutes the refereed proceedings of the 21th International Conference on Artificial Intelligence in Education, AIED 2020, held in Ifrane, Morocco, in July 2020.* The 49 full papers presented together with 66 short, 4 industry & innovation, 4 doctoral consortium, and 4 workshop papers were carefully reviewed and selected from 214 submissions. The conference provides opportunities for the cross-fertilization of approaches, techniques and ideas from the many fields that comprise AIED, including computer science, cognitive and learning sciences, education, game design, psychology, sociology, linguistics as well as many domain-specific areas. *The conference was held virtually due to the COVID-19 pandemic.
