ACTEX Learning

During the past twenty-five years, researchers have made impressive advances in pinpointing effective learning strategies (namely, activities the learner engages in during learning that are intended to improve learning). In Learning as a Generative Activity: Eight Learning Strategies that Promote Understanding, Logan Fiorella and Richard E. Mayer share eight evidence-based learning strategies that promote understanding: summarizing, mapping, drawing, imagining, self-testing, self-explaining, teaching, and enacting. Each chapter describes and exemplifies a learning strategy, examines the underlying cognitive theory, evaluates strategy effectiveness by analyzing the latest research, pinpoints boundary conditions, and explores practical implications and future directions. Each learning strategy targets generative learning, in which learners actively make sense out of the material so they can apply their learning to new situations. This concise, accessible introduction to learning strategi

Learning, Unlearning and Re-learning Curves (Volume IV of the Working Guides to Estimating & Forecasting series) focuses in on Learning Curves, and the various tried and tested models of Wright, C

E-Learning or electronic learning refers to the practice of teaching and learning using digital technology and multimedia. E-Learning technologies use flash and PowerPoint software to develop interact

Today's workers need a blended approach to learning that offers flexibility and personalization. Blended Learning is packed with easy-to-apply techniques to ensure your blended learning program is

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.Key FeaturesExplore machine learning techniques with prominent ope

This book focuses on developing methods for constructing learning paths in terms of “learning resources” (learning contents), “learning approaches” (learning method), and “learning quality” (learning

Early Learning: ABC Early Learning: 123 Early Learning: Everyday Words Early Learning: Colours and Shapes My Home Early Learning: Big and Little Early Learning: What's the Time?

During the past twenty-five years, researchers have made impressive advances in pinpointing effective learning strategies (namely, activities the learner engages in during learning that are intended to improve learning). In Learning as a Generative Activity: Eight Learning Strategies that Promote Understanding, Logan Fiorella and Richard E. Mayer share eight evidence-based learning strategies that promote understanding: summarizing, mapping, drawing, imagining, self-testing, self-explaining, teaching, and enacting. Each chapter describes and exemplifies a learning strategy, examines the underlying cognitive theory, evaluates strategy effectiveness by analyzing the latest research, pinpoints boundary conditions, and explores practical implications and future directions. Each learning strategy targets generative learning, in which learners actively make sense out of the material so they can apply their learning to new situations. This concise, accessible introduction to learning strategi

Interactive Student Centered Learning: A Cooperative Approach to Learning concentrates on Student Centered Learning (SCL) which encompasses Active Learning (AL), Cooperative Learning (COL), Collaborat

Interactive Student Centered Learning: A Cooperative Approach to Learning concentrates on Student Centered Learning (SCL) which encompasses Active Learning (AL), Cooperative Learning (COL), Collaborat

Bernice Wong's Learning about Learning Disabilities was the first text to give equal attention to the intellectual, conceptual, and practical aspects of learning disabilities. The Third Edition of thi

Learning How to Learn: Basic Techniques for Self Study, provides a basic learning foundation and system designed to increase learning and comprehension skills. Applying the learning techniques present

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

This practical guide provides teachers with ideas for stimulating effective learning in the classroom. It is designed to help teachers reflect on effective learning and highlight the learning experie

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

E-learning is now an essential component of education. Using examples from around the globe, the authors of E-Learning provide an in-depth examination of past and future e-learning approaches, and exp

Learning disability nurses play a leading role in the care and support of people with learning disabilities. Learning Disability Nursing explores the theory and practice of learning disability nursing

This Reader examines the ways in which learning is organized in a diverse range of lifelong learning environments. If we are to harness the full potential of this learning, the structures of organizat

Exploring the influence of AI technologies on theories of reason, cognition, learning, and educationLearning Under Algorithmic Conditions presents twenty-seven concise essays that collectively chart the shifting terrain of learning in the age of artificial intelligence. Providing historical and philosophical context, this innovative volume features prominent scholars from the fields of media studies, philosophy, and education research, who shed light on how learning has become newly envisioned, machinic, and more-than-human. The contributors unravel various histories of machine intelligence and elucidate the current impact of machine learning technologies on practices of knowledge production. Teeming with theoretical and practical insights, Learning Under Algorithmic Conditions is an interdisciplinary guide for those working across the humanities and social sciences as well as anyone interested in understanding our changing social, political, and technical infrastructures.Contributors:

Language is integral to human doing, being, becoming and belonging, and its acquisition is naturally distributed in and across activity spaces over time. Different learning experiences form 'a dialectical unity', where one brings the others into existence, and the capacities fostered in one inform and transform those in the others. Thus, connected learning across time and space is fundamental to the coherence, relevance, and meaningfulness of language learning, yet is not given sufficient attention in second language education. Connected learning is particularly relevant in the GenAI era where learner-driven lifelong and lifewide learning is much emphasized. This Element hence conceptualizes the framework of connected language learning with technology, and discusses how the framework could be operationalized and implemented in teaching and learning. It further foregrounds four concepts in this learning framework - agential literacy, interest development, self-regulation support, and id

As the largest U.S. employer, the Department of Defense (DoD) is a major provider of training and development. Given the complex and increasingly unpredictable operational landscape within which the military operates across land, sea, air, cyberspace, and space, continuous learning is vital in the military. Military learners must acquire diverse skills, from communication to using advanced technology, and retain those skills for use when necessary. Success in learning - and the ability to retain and transfer what is learned to military missions - is directly linked with military innovation, operational success, and the success of our nation. Adult Learning in the Military Context examines motivations shaping learning, contextual and equity factors, emerging learning technologies, effective approaches to assessment, and provides a research agenda. This report highlights that adult learners are most motivated when learning aligns with personal goals but notes that military structures may

Data-Driven Learning (DDL) can be broadly defined as the use of corpus tools and techniques for learners and teachers of foreign or second language, typically in the form of concordances derived from authentic texts for inductive learning of lexicogrammar. This Element is a practical guide for language teachers and graduate students intending to explore or upgrade their use of corpora in the language classroom and beyond. In today's context, where advances in computing and information processing dominate our social and professional interactions, the use of corpora emerges as a prime resource with which to approach data-driven language learning and teaching, developing language awareness, noticing skills and critical thinking for learning that generative AI cannot do for you.

This enthusiastic introduction to the fundamentals of information theory builds from classical Shannon theory through to modern applications in statistical learning. Includes over 210 student exercises, emphasising practical applications in statistics, machine learning and modern communication theory. Accompanied by online instructor solutions.

This book explores teaching, learning, and leadership in higher education following the Covid-19 pandemic. It examines opportunities that currently exist within higher education as they relate to innovative teaching and learning strategies, from instructional modalities to new models of transformative learning to meet students "where they are" in terms of career development and lifelong learning. Emphasis is placed on educational leadership and management skills, faculty and teaching acumen, and students and their quest for knowledge and understanding as we navigate past a global health crisis towards a future of hope and solutions to some of today's most pressing issues using collaboration, community, and an inquiry-oriented approach. The current state of education is reimagined with emphasis on higher education as a learning organization. A sense of urgency in higher education is underscored to instill knowledge and competency, encourage innovation, and help the next generation of st

With Iterative Learning Control Algorithms and Experimental Benchmarking the authors discuss the main methods of iterative learning control (ILC) and its interactions, as well as comparator performanc

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves as both a starting point for researchers and graduate students in mathematics trying to get into the field, as well as an invaluable reference for future research.

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slide

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

This early-learning workbook is full of fun activities to help develop your child's fine motor skills.Improve your preschooler's fine-motor skills with Priddy Learning: Scissor Skills, a fun activity book from Priddy Books.This 80-page paperback workbook is designed to encourage safe scissor practice, with varying activities to engage children aged three and over. The exercises increase in difficulty as you go through the book and also include ideas for pasting and creating your own pictures so there is something for every ability level.Each page is perforated which helps for easy-cutting and so that your child's proud artwork can be displayed afterwards. Activities such as shapes, puzzles, mazes, and more are bright and appealing and the quality paper is easy for children to handle and cut without tearing.Part of the Priddy Learning Workbook series which includes; Priddy Learning Tracing & Pen Control, Priddy Learning: Lowercase Alphabet, Priddy Learning Uppercase Alphabet, Priddy

A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

The interdisciplinary field of the learning sciences encompasses educational psychology, cognitive science, computer science, and anthropology, among other disciplines. The Cambridge Handbook of the Learning Sciences, first published in 2006, is the definitive introduction to this innovative approach to teaching, learning, and educational technology. In this significantly revised third edition, leading scholars incorporate the latest research to provide seminal overviews of the field. This research is essential in developing effective innovations that enhance student learning - including how to write textbooks, design educational software, prepare effective teachers, and organize classrooms. The chapters illustrate the importance of creating productive learning environments both inside and outside school, including after school clubs, libraries, and museums. The Handbook has proven to be an essential resource for graduate students, researchers, consultants, software designers, and poli

Evolution of Learning and Memory Mechanisms is an exploration of laboratory and field research on the many ways that evolution has influenced learning and memory processes, such as associative learning, social learning, and spatial, working, and episodic memory systems. This volume features research by both outstanding early-career scientists as well as familiar luminaries in the field. Learning and memory in a broad range of animals are explored, including numerous species of invertebrates (insects, worms, sea hares), as well as fish, amphibians, birds, rodents, bears, and human and nonhuman primates. Contributors discuss how the behavioral, cognitive, and neural mechanisms underlying learning and memory have been influenced by evolutionary pressures. They also draw connections between learning and memory and the specific selective factors that shaped their evolution. Evolution of Learning and Memory Mechanisms should be a valuable resource for those working in the areas of experiment

Evolution of Learning and Memory Mechanisms is an exploration of laboratory and field research on the many ways that evolution has influenced learning and memory processes, such as associative learning, social learning, and spatial, working, and episodic memory systems. This volume features research by both outstanding early-career scientists as well as familiar luminaries in the field. Learning and memory in a broad range of animals are explored, including numerous species of invertebrates (insects, worms, sea hares), as well as fish, amphibians, birds, rodents, bears, and human and nonhuman primates. Contributors discuss how the behavioral, cognitive, and neural mechanisms underlying learning and memory have been influenced by evolutionary pressures. They also draw connections between learning and memory and the specific selective factors that shaped their evolution. Evolution of Learning and Memory Mechanisms should be a valuable resource for those working in the areas of experiment

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the ne

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod