Model Based Approaches To Learning
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Model-Based Approaches to Learning
Model-Based Approaches to Learning provides a new perspective called learning by system modeling. This book explores the learning impact of students when constructing models of complex systems. In this approach students are building their own models and engaging at a much deeper conceptual level of understanding of the content, processes, and problem solving of the domain, which is proven to be successful by research from the area of mindtools. Topics covered include the foundations of knowledge structures and mental model development, modeling for understanding, modeling for assessment, individual versus collaborative modeling, and the use of simulations to support learning and instruction in complex, cognitive domains. The thread tying these chapters together is an emphasis on what the learner is doing when he is engaged in modeling and simulation construction rather than merely interacting with constructed simulations. Model-Based Approaches to Learning is an interesting book for Educators (Instructors, K-12 Teachers), who are looking for forms to use advanced computer technology in classrooms. Also Teachers’ educators who are working on the integration of technology into their teacher preparation classrooms can find new concepts and best-practice examples in this book. This also holds true for all Educators and Researchers who are interested in modeling as an activity to successfully work with ill-structured and complex problems.
Learning Through Practice
Author: Stephen Billett
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-06-02
Practice-based learning—the kind of education that comes from experiencing real work in real situations—has always been a prerequisite to qualification in professions such as medicine. However, there is growing interest in how practice-based models of learning can assist the initial preparation for and further development of skills for a wider range of occupations. Rather than being seen as a tool of first-time training, it is now viewed as a potentially important facet of professional development and life-long learning. This book provides perspectives on practice-based learning from a range of disciplines and fields of work. The collection here draws on a wide spectrum of perspectives to illustrate as well as to critically appraise approaches to practice-based learning. The book’s two sections first explore the conceptual foundations of learning through practice, and then provide detailed examples of its implementation. Long-standing practice-based approaches to learning have been used in many professions and trades. Indeed, admission to the trades and major professions (e.g. medicine, law, accountancy) can only be realised after completing extended periods of practice in authentic practice settings. However, the growing contemporary interest in using practice-based learning in more extensive contexts has arisen from concerns about the direct employability of graduates and the increasing focus on occupation-specific courses in both vocations and higher education. It is an especially urgent issue in an era of critical skill shortages, rapidly transforming work requirements and an aging workforce combined with a looming shortage of new workforce entrants. We must better understand how existing models of practice-based learning are enacted in order to identify how they can be applied to different kinds of employment and workplaces. The contributions to this volume explore ways in which learning through practice can be conceptualised, enacted, and appraised through an analysis of the traditions, purposes, and processes that support this learning—including curriculum models and pedagogic practices.
Designing Robot Behavior in Human-Robot Interactions
In this book, we have set up a unified analytical framework for various human-robot systems, which involve peer-peer interactions (either space-sharing or time-sharing) or hierarchical interactions. A methodology in designing the robot behavior through control, planning, decision and learning is proposed. In particular, the following topics are discussed in-depth: safety during human-robot interactions, efficiency in real-time robot motion planning, imitation of human behaviors from demonstration, dexterity of robots to adapt to different environments and tasks, cooperation among robots and humans with conflict resolution. These methods are applied in various scenarios, such as human-robot collaborative assembly, robot skill learning from human demonstration, interaction between autonomous and human-driven vehicles, etc. Key Features: Proposes a unified framework to model and analyze human-robot interactions under different modes of interactions. Systematically discusses the control, decision and learning algorithms to enable robots to interact safely with humans in a variety of applications. Presents numerous experimental studies with both industrial collaborative robot arms and autonomous vehicles.