Understanding Applied Learning

Understanding Applied Learning enables teachers, lecturers and educators to facilitate applied learning effectively with learners in schools, colleges and universities. It introduces teachers to the concept of applied learning in practice, cutting across any vocational and academic divide to show how this approach supports high-quality and effective outcomes for learners. Applied learning prepares and equips learners for life in the twenty-first century and lifelong learning. Offering practical guidance on why and how to adopt applied learning in all post-primary settings, this practical resource introduces and explores the core concepts, practices and benefits of using this approach. Illustrated with real-life scenarios, it examines why applied learning is relevant today, how it enables learners to connect knowledge with new situations, how to navigate and solve intellectual and skills-based problems and how to work collaboratively and develop higher-level thinking skills. Key topics covered include: A range of applied learning theories and strategies Relevant, Engaging, Active Learning (REAL) for successful knowledge and skills development The relevance of applied learning to employers Overcoming issues in embedding applied learning approaches How to embed creativity into learning experiences. Understanding Applied Learning is an authoritative, down-to-earth guide to facilitate applied learning effectively and successfully with students in secondary schools, colleges and universities. It is a source of support and inspiration for all those committed to high-quality and effective outcomes for learners.

The field of education is in constant flux as new theories and practices emerge to engage students and improve the learning experience. Research advances help to make these improvements happen and are essential to the continued improvement of education. The Handbook of Research on Applied Learning Theory and Design in Modern Education provides international perspectives from education professors and researchers, cyberneticists, psychologists, and instructional designers on the processes and mechanisms of the global learning environment. Highlighting a compendium of trends, strategies, methodologies, technologies, and models of applied learning theory and design, this publication is well-suited to meet the research and practical needs of academics, researchers, teachers, and graduate students as well as curriculum and instructional design professionals.

Today, “all institutions of higher education almost everywhere in the world have been influenced by the concept of globalisation. The resulting policy changes in each nation state have, of course, reflected the degree of the impact of globalisation on the country, hence the changes in higher education.” (Banya, 2005, p.147). This points to globalisation shaping knowledge production as well as the spread of intentional and continuous waves of innovation. The effects of globalisation on education can be seen through a) the changing paradigm from a closed system to a more open system, and b) the changing approach from a teacher-centred learning environment to that of a learner-centred environment. This changing approach culminates in the broader ideas of ‘applied learning’ through a) a productive view of learning versus reproductive view of learning, b) constructivist versus behaviourist, c) learning facilitation versus teaching, and d) process-based assessment versus outcome-based assessment (Rudic, 2016).

A comprehensive look at the promise and potential of online learning In our digital age, students have dramatically new learning needs and must be prepared for the idea economy of the future. In Getting Smart, well-known global education expert Tom Vander Ark examines the facets of educational innovation in the United States and abroad. Vander Ark makes a convincing case for a blend of online and onsite learning, shares inspiring stories of schools and programs that effectively offer "personal digital learning" opportunities, and discusses what we need to do to remake our schools into "smart schools." Examines the innovation-driven world, discusses how to combine online and onsite learning, and reviews "smart tools" for learning Investigates the lives of learning professionals, outlines the new employment bargain, examines online universities and "smart schools" Makes the case for smart capital, advocates for policies that create better learning, studies smart cultures

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This volume discusses the need for a major paradigm shift in educational practice in the current digital and globalized world. It establishes a bridge between theory and praxis and revisits the objectives of learning and its modalities within the context of a rapidly evolving global world order. This volume includes perspectives from different countries on creating a dynamic and adaptive education system that encourages creativity, leadership, flexibility, and working in virtual as well as inclusive environments. The four sections include chapters that discuss creating meaningful learning environments, preparing teachers for new age classrooms, the digital learning space, fostering change in classrooms, and importantly also includes cases and experiments from schools. The authors are teacher educators, teachers and researchers, and each chapter, while being deeply rooted in theory, is juxtaposed with informed practice, making the suggestions easy to implement in different settings. This is an important resource for researchers and practitioners associated with education systems in creating engaging, meaningful and future-ready education practices.

'Dr Bob was our pediatrician. If we could trust him with our kids, you can trust him with how to learn better. I discovered many surprising truths about learning in this book, so follow the scientific research and become a better learner.'Guy KawasakiBestselling book author, chief evangelist of Canva and creator of the 'Remarkable People' podcast Most 'how to learn' books focus only on study techniques. However, knowing these study techniques doesn't guarantee they will be successfully implemented. Based on Professor Kamei's popular undergraduate course at the National University of Singapore, the author shares his unique perspective as an educator and physician to provide a strategic approach to learning that will benefit all students looking to optimize their learning.His extensive experience with helping students with their studies has identified a wide variety of reasons why they were not learning properly. What throws many learners off is that they hold certain myths that result in poor study habits. Learners who understand the truth behind these myths can use this knowledge to better plan their study and have an advantage over others that don't.Furthermore, modifying your study methods won't make much difference if you are too sleepy to learn or lack the motivation and self-discipline to pick up a book. These foundational aspects of learning also need to be solidly in place. This book takes learners through the author's holistic method to help students learn better and meet whatever learning challenges they face.Learners of all ages who wish to optimize their learning will benefit from this book, as well as educators seeking an approach to help their students learn better.Related Link(s)

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will Learn Implement advanced techniques in the right way in Python and TensorFlow Debug and optimize advanced methods (such as dropout and regularization) Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on) Set up a machine learning project focused on deep learning on a complex dataset Who This Book Is For Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.