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.

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

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).

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.

Albert J. Kearney explains the kinds of learning and reinforcement processes that form the basis of ABA programs. He then describes how the science of behavior analysis can be applied to real life problems. He looks at how behavior is assessed and various intervention techniques that are often employed with children who have special needs.

This book takes a fresh look at programs for advanced studies for high school students in the United States, with a particular focus on the Advanced Placement and the International Baccalaureate programs, and asks how advanced studies can be significantly improved in general. It also examines two of the core issues surrounding these programs: they can have a profound impact on other components of the education system and participation in the programs has become key to admission at selective institutions of higher education. By looking at what could enhance the quality of high school advanced study programs as well as what precedes and comes after these programs, this report provides teachers, parents, curriculum developers, administrators, college science and mathematics faculty, and the educational research community with a detailed assessment that can be used to guide change within advanced study programs.