Designing For The User Experience In Learning Systems
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Designing for the User Experience in Learning Systems
While the focus of the UX research and design discipline and the Learning Sciences and instructional design disciplines is often similar and almost always tangential, there seems to exist a gap, i.e. a lack of communication between the two fields. Not much has been said about how UX Design can work hand-in-hand with instructional design to advance learning. The goal of this book is to bridge this gap by presenting work that cuts through both fields. To illustrate this gap in more detail, we provide a combined view of UX Research and Design & Educational Technology. While the traditional view has perceived the Learning Experience Design as a field of Instructional Design, we will highlight its connection with UX, an aspect that has become increasingly relevant. Our focus on user experience research and design has a unique emphasis on the human learning experience: we strongly believe that in learning technology the technological part is only mediating the learning experience, and we do not focus on technological advancements per se, as we believe they are not the solution, in themselves, to the problems that education is facing. This book aims to lay out the challenges and opportunities in this field and highlight them through research presented in the various chapters. Thus, it presents a unique opportunity to represent areas of learning technology that go very far beyond the MOOC and the classroom technology. The book provides an outstanding overview and insights in the area and it aims to serve as a significant and valuable source for learning researchers and practitioners. The chapter "User requirements when designing learning e-content: interaction for all" is available open access under a CC BY 4.0 license at link.springer.com
Machine Learning Systems
Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside Working with Spark, MLlib, and Akka Reactive design patterns Monitoring and maintaining a large-scale system Futures, actors, and supervision About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING Learning reactive machine learning Using reactive tools PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM Collecting data Generating features Learning models Evaluating models Publishing models Responding PART 3 - OPERATING A MACHINE LEARNING SYSTEM Delivering Evolving intelligence
Design, User Experience, and Usability: Health, Learning, Playing, Cultural, and Cross-Cultural User Experience
The four-volume set LNCS 8012, 8013, 8014 and 8015 constitutes the proceedings of the Second International Conference on Design, User Experience, and Usability, DUXU 2013, held as part of the 15th International Conference on Human-Computer Interaction, HCII 2013, held in Las Vegas, USA in July 2013, jointly with 12 other thematically similar conferences. The total of 1666 papers and 303 posters presented at the HCII 2013 conferences was carefully reviewed and selected from 5210 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The total of 282 contributions included in the DUXU proceedings were carefully reviewed and selected for inclusion in this four-volume set. The 67 papers included in this volume are organized in the following topical sections: cross-cultural and intercultural user experience; designing for the learning and culture experience; designing for the health and quality of life experience; and games and gamification.