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Mathematical Modeling, Third Edition is a general introduction to an increasingly crucial topic for today's mathematicians. Unlike textbooks focused on one kind of mathematical model, this book covers the broad spectrum of modeling problems, from optimization to dynamical systems to stochastic processes. Mathematical modeling is the link between mathematics and the rest of the world. Meerschaert shows how to refine a question, phrasing it in precise mathematical terms. Then he encourages students to reverse the process, translating the mathematical solution back into a comprehensible, useful answer to the original question. This textbook mirrors the process professionals must follow in solving complex problems. Each chapter in this book is followed by a set of challenging exercises. These exercises require significant effort on the part of the student, as well as a certain amount of creativity. Meerschaert did not invent the problems in this book--they are real problems, not designed to illustrate the use of any particular mathematical technique. Meerschaert's emphasis on principles and general techniques offers students the mathematical background they need to model problems in a wide range of disciplines. Increased support for instructors, including MATLAB material New sections on time series analysis and diffusion models Additional problems with international focus such as whale and dolphin populations, plus updated optimization problems
This book constitutes the proceedings of the Second International Conference on User Modeling, Adaptation, and Personalization, held on Big Island, HI, USA, in June 2010. This annual conference was merged from the biennial conference series User Modeling, UM, and the conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH. The 26 long papers and 6 short papers presented together with 7 doctoral consortium papers, 2 invited talks, and 4 industry panel papers were carefully reviewed and selected from 161 submissions. The tutorials and workshops were organized in topical sections on intelligent techniques for web personalization and recommender systems; pervasive user modeling and personalization; user models for motivational systems; adaptive collaboration support; architectures and building blocks of web-based user adaptive systems; adaptation and personalization in e-b/learning using pedagogic conversational agents; and user modeling and adaptation for daily routines.
This book on self-improving systems is the seventh 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, self-improving systems, data visualization) in intelligent tutoring system (ITS) design. This book focuses on self-improving systems. The discussion chapters in this book examine topics through the lens of the Generalized Intelligent Framework for Tutoring (GIFT). GIFT is a modular, service-oriented architecture created to reduce the cost and skill required to author ITSs, distribute ITSs, manage instruction within ITSs, and evaluate the effect of ITS technologies on learning, performance, retention, transfer of skills, and other instructional outcomes.
This book is a concise introduction to emerging concepts and ideas found at the intersection of contemporary behavioural science and artificial intelligence. The book explores how these disciplines interact, change, and adapt to one another and what the implications of such an interaction are for practice and society. AI for Behavioural Science book begins by exploring the field of machine behaviour, which advocates using behavioural science to investigate artificial intelligence. This perspective is built upon to develop a framework of terminology that treats humans and machines as comparable entities possessing their own motive power. From here, the notion of artificial intelligence systems becoming choice architects is explored through a series of reconceptualisations. The architecting of choices is reconceptualised as a process of selection from a set of choice architectural designs, while human behaviour is reconceptualised in terms of probabilistic outcomes. The material difference between the so-called "manual nudging" and "automatic nudging" (or hypernudging) is then explored. The book concludes with a discussion of who is responsible for autonomous choice architects.
Personalized and adaptive systems employ user models to adapt content, services, interaction or navigation to individual users’ needs. User models can be inferred from implicitly observed information, such as the user’s interaction history or current location, or from explicitly entered information, such as user profile data or ratings. Applications of personalization include item recommendation, location-based services, learning assistance and the tailored selection of interaction modalities. With the transition from desktop computers to mobile devices and ubiquitous environments, the need for adapting to changing contexts is even more important. However, this also poses new challenges concerning privacy issues, user control, transparency, and explainability. In addition, user experience and other human factors are becoming increasingly important. This book describes foundations of user modeling, discusses user interaction as a basis for adaptivity, and showcases several personalization approaches in a variety of domains, including music recommendation, tourism, and accessible user interfaces.
This book aims at guiding the educators from a variety of available technologies to support learning and teaching by discussing the learning benefits and the challenges that interactive technology imposes. This guidance is based on practical experiences gathered through developing and integrating them into varied educational settings. It compiles experiences gained with various interactive technologies, offering a comprehensive perspective on the use and potential value of interactive technologies to support learning and teaching. Taken together, the chapters provide a broader view that does not focus exclusively on the uses of technology in educational settings, but also on the impact and ability of technology to improve the learning and teaching processes. The book addresses the needs of researchers, educators and other stakeholders in the area of education interested in learning how interactive technologies can be used to overcome key educational challenges.
This handbook is the first book ever covering the area of Multimodal Learning Analytics (MMLA). The field of MMLA is an emerging domain of Learning Analytics and plays an important role in expanding the Learning Analytics goal of understanding and improving learning in all the different environments where it occurs. The challenge for research and practice in this field is how to develop theories about the analysis of human behaviors during diverse learning processes and to create useful tools that could augment the capabilities of learners and instructors in a way that is ethical and sustainable. Behind this area, the CrossMMLA research community exchanges ideas on how we can analyze evidence from multimodal and multisystem data and how we can extract meaning from this increasingly fluid and complex data coming from different kinds of transformative learning situations and how to best feed back the results of these analyses to achieve positive transformative actions on those learning processes. This handbook also describes how MMLA uses the advances in machine learning and affordable sensor technologies to act as a virtual observer/analyst of learning activities. The book describes how this “virtual nature” allows MMLA to provide new insights into learning processes that happen across multiple contexts between stakeholders, devices and resources. Using such technologies in combination with machine learning, Learning Analytics researchers can now perform text, speech, handwriting, sketches, gesture, affective, or eye-gaze analysis, improve the accuracy of their predictions and learned models and provide automated feedback to enable learner self-reflection. However, with this increased complexity in data, new challenges also arise. Conducting the data gathering, pre-processing, analysis, annotation and sense-making, in a way that is meaningful for learning scientists and other stakeholders (e.g., students or teachers), still pose challenges in this emergent field. This handbook aims to serve as a unique resource for state of the art methods and processes. Chapter 11 of this book is available open access under a CC BY 4.0 license at link.springer.com.