Resulting from a conference that took place in Amiens, France, in June 2019, this book examines the place and role of objects centered in teaching practices from kindergarten to university, both in the context of France and elsewhere. These “objects for learning” are considered in their physicality as productions, work or signs that are used for learning. They become “objects to learn about” when the object itself is the learning objective. This book offers a cross-disciplinary perspective, linking the different disciplinary fields studied and the many reference sources used by the authors. This two-volume work offers an overview of current research on the subject, with this second volume focusing on objects in representations of space and time, then on learners’ activities in the making or use of objects, before concluding with different cultural and philosophical perspectives on objects
"Kids love to move. But how do we harness all that kinetic energy effectively for math learning? In Math on the Move, Malke Rosenfeld shows how pairing math concepts and whole body movement creates opportunities for students to make sense of math in entirely new ways. Malke shares her experience creating dynamic learning environments by: exploring the use of the body as a thinking tool, highlighting mathematical ideas that are usefully explored with a moving body, providing a range of entry points for learning to facilitate a moving math classroom. ..."--Publisher description.
This is a philosophical work that develops a general theory of ontological objects and object-relations. It does this by examining concepts as acquired dispositions, and then focuses on perhaps the most important of these: the concept of learning. This concept is important because everything that we know and do in the world is predicated on a prior act of learning. A concept can have many meanings and can be used in a number of different ways, and this creates difficulty when considering the nature of objects and the relationships between them. To enable this, David Scott answers a series of questions about concepts in general and the concept of learning in particular. Some of these questions are: What is learning? What different meanings can be given to the notion of learning? How does the concept of learning relate to other concepts, such as innatism, development and progression? The book offers a counter-argument to empiricist conceptions of learning, to the propagation of simple messages about learning, knowledge, curriculum and assessment, and to the denial that values are central to understanding how we live. It argues that values permeate everything: our descriptions of the world, the attempts we make at creating better futures and our relations with other people.
The use of museum collections as a path to learning for university students is fast becoming a new pedagogy for higher education. Despite a strong tradition of using lectures as a way of delivering the curriculum, the positive benefits of ’active’ and ’experiential learning’ are being recognised in universities at both a strategic level and in daily teaching practice. As museum artefacts, specimens and art works are used to evoke, provoke, and challenge students’ engagement with their subject, so transformational learning can take place. This unique book presents the first comprehensive exploration of ’object-based learning’ as a pedagogy for higher education in a broad context. An international group of authors offer a spectrum of approaches at work in higher education today. They explore contemporary principles and practice of object-based learning in higher education, demonstrating the value of using collections in this context and considering the relationship between academic discipline and object-based learning as a teaching strategy.
The third edition of this award-winning Handbook continues the mission of its predecessors: to provide a comprehensive compendium of research in all aspects of distance education, arguably the most significant development in education over the past three decades. While the book deals with education that uses technology, the focus is on teaching and learning and how its management can be facilitated through technology. Key features include: Comprehensive coverage that includes all aspects of distance education, including design, instruction, management, policy, and a section on different audiences. Chapter authors frame their topic in terms of empirical research (past and present) and discuss the nature of current practice in terms of that research. Future research needs are discussed in relation to both confirmed practice and recent changes in the field. Section one provides a unique review of the theories that support distance education pedagogy. Section six includes a unique review of distance education as a component of global culture. This book will be of interest to anyone engaged in distance education at any level. It is also appropriate for corporate and government trainers and for administrators and policy makers in all these environments. Recipient of the 2013 IAP Distance Education Book Award
This monograph presents the challenges, vision and context to design smart learning objects (SLOs) through Computer Science (CS) education modelling and feature model transformations. It presents the latest research on the meta-programming-based generative learning objects (the latter with advanced features are treated as SLOs) and the use of educational robots in teaching CS topics. The introduced methodology includes the overall processes to develop SLO and smart educational environment (SEE) and integrates both into the real education setting to provide teaching in CS using constructivist and project-based approaches along with evaluation of pedagogic outcomes. Smart Learning Objects for Smart Education in Computer Science will appeal to researchers in CS education particularly those interested in using robots in teaching, course designers and educational software and tools developers. With research and exercise questions at the end of each chapter students studying CS related courses will find this work informative and valuable too.
This book constitutes the strictly refereed post-workshop proceedings of the second International Workshop on Object Representation in Computer Vision, held in conjunction with ECCV '96 in Cambridge, UK, in April 1996. The 15 revised full papers contained in the book were selected from 45 submissions for presentation at the workshop. Also included are three invited contributions based on the talks by Takeo Kanade, Jan Koenderink, and Ram Nevatia as well as a workshop report by the volume editors summarizing several panel discussions and the general state of the art in the area.
Human beings experience a world of objects: bounded entities that occupy space and persist through time. Our actions are directed toward objects, and our language describes objects. We categorize objects into kinds that have different typical properties and behaviors. We regard some kinds of objects – each other, for example – as animate agents capable of independent experience and action, while we regard other kinds of objects as inert. We re-identify objects, immediately and without conscious deliberation, after days or even years of non-observation, and often following changes in the features, locations, or contexts of the objects being re-identified. Comparative, developmental and adult observations using a variety of approaches and methods have yielded a detailed understanding of object detection and recognition by the visual system and an advancing understanding of haptic and auditory information processing. Many fundamental questions, however, remain unanswered. What, for example, physically constitutes an “object”? How do specific, classically-characterizable object boundaries emerge from the physical dynamics described by quantum theory, and can this emergence process be described independently of any assumptions regarding the perceptual capabilities of observers? How are visual motion and feature information combined to create object information? How are the object trajectories that indicate persistence to human observers implemented, and how are these trajectory representations bound to feature representations? How, for example, are point-light walkers recognized as single objects? How are conflicts between trajectory-driven and feature-driven identifications of objects resolved, for example in multiple-object tracking situations? Are there separate “what” and “where” processing streams for haptic and auditory perception? Are there haptic and/or auditory equivalents of the visual object file? Are there equivalents of the visual object token? How are object-identification conflicts between different perceptual systems resolved? Is the common assumption that “persistent object” is a fundamental innate category justified? How does the ability to identify and categorize objects relate to the ability to name and describe them using language? How are features that an individual object had in the past but does not have currently represented? How are categorical constraints on how objects move or act represented, and how do such constraints influence categorization and the re-identification of individuals? How do human beings re-identify objects, including each other, as persistent individuals across changes in location, context and features, even after gaps in observation lasting months or years? How do human capabilities for object categorization and re-identification over time relate to those of other species, and how do human infants develop these capabilities? What can modeling approaches such as cognitive robotics tell us about the answers to these questions? Primary research reports, reviews, and hypothesis and theory papers addressing questions relevant to the understanding of perceptual object segmentation, categorization and individual identification at any scale and from any experimental or modeling perspective are solicited for this Research Topic. Papers that review particular sets of issues from multiple disciplinary perspectives or that advance integrative hypotheses or models that take data from multiple experimental approaches into account are especially encouraged.
