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Based on the updated National Occupational Standards for Supporting Teaching and Learning in Schools, this new edition of A Teaching Assistant's Guide to Completing NVQ Level 2 caters directly to the criteria of the course, providing the necessary 'Knowledge and Understanding' required as well as invaluable information regarding evidence collection.
Incorporating the changed guidelines regarding evidence collection this comprehensive guide demonstrates the role of the assessor in observing and questioning the candidate and that of the candidate asking colleagues to provide witness statements.
As well as providing in-depth underpinning knowledge for all mandatory units and a vast array of optional units, this book offers a range of tried-and-tested materials and practical advice for NVQ Level 2 candidates. The authors have included numerous self-assessment activities, case studies and quizzes to enable candidates to check their understanding of key concepts, to make connections from theory to practice and to assist them in their observation and assessment sessions.
Written in an engaging and approachable manner and illustrated with many cartoons, this book aims to give the candidate the knowledge necessary to embark on this qualification with confidence.
A wide range of chapters provides essential advice for NVQ Level 2 candidates, including how to:
Highly practical and rooted in everyday classroom practice, this book is specifically aimed at teaching assistants enrolled on, or embarking upon, NVQ courses that support the government's National Occupational Standards. In addition this book will be of benefit to schools and teachers who are supporting teaching assistants taking this course.
Policy search is a subfield of Reinforcement Learning (RL) that focuses on finding good parameters for a given policy parameterization. It is well suited tor robotics as it can cope with high-dimensional state and action spaces, which is one of the main challenges in robot learning. A Survey on Policy Search for Robotics reviews recent successes of both model-free and model-based policy search in robot learning. Model-free policy search is a general approach to learn policies based on sampled trajectories. This text classifies model-free methods based on their policy evaluation, policy update, and exploration strategies, and presents a unified view of existing algorithms. Learning a policy is often easier than learning an accurate forward model, and, hence, model-free methods are more frequently used in practice. However, for each sampled trajectory, it is necessary to interact with the robot, which can be time consuming and challenging in practice. Model-based policy search addresses this problem by first learning a simulator of the robot's dynamics from data. Subsequently, the simulator generates trajectories that are used for policy learning. For both model-free and model-based policy search methods, A Survey on Policy Search for Robotics reviews their respective properties and their applicability to robotic systems. It is an invaluable reference for anyone working in the area.
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