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Data Analytics and Adaptive Learning : Research Perspectives
Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings.In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments.This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K–12, and adult/continuing education contexts.By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.
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Data Analytics and Adaptive Learning : Research Perspectives
Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings.In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments.This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K–12, and adult/continuing education contexts.By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.
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Knowledge Guided Machine Learning : Accelerating Discovery using Scientific Knowledge and Data
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines.Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios.As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks.This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing.Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field.Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers.KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
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Re-theorising Learning and Research Methods in Learning Research
Re-Theorising Learning and Research Methods in Learning Research explores the latest developments in the field of learning theory, offering an overview of emerging methods and demonstrating how recent research contributes to furthering understanding of learning.This book illustrates how theory and methods inform one another, facilitating advancements in the field, while addressing the ways in which societal and technological change create a need for adapting approaches to examining learning.Drawing on an international team of contributors, this book comprises 17 chapters and three commentaries, thematically organised into three broad sections:emerging theories and conceptualisations of learning and how they drive methodological developmentnew methods or innovative use of existing methods and their contribution to theory developmenttheories and methods that emerge in connection with societal changesBoth novice researchers and more experienced scholars will benefit from an overview of recent theoretical and methodological advances in the learning research field.This is an invaluable resource for researchers in the learning and educational research field and will also support Masters and PhD students to understand how learning theories and research methodology in the field have been evolving in recent years.
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What exactly does a specialist in information technology for data and process analysis do?
A specialist in information technology for data and process analysis is responsible for analyzing and interpreting data to identify trends, patterns, and insights that can be used to improve business processes and decision-making. They use various tools and techniques to collect, clean, and analyze data, and then present their findings to stakeholders in a clear and understandable manner. They also work to identify areas for process improvement and develop strategies to optimize workflows and increase efficiency. Overall, their role is to leverage technology and data to drive informed decision-making and improve business operations.
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What is the difference between learning and acquiring knowledge?
Learning involves the process of gaining knowledge or skills through study, experience, or teaching. It is an active process that requires engagement and understanding of the information being acquired. Acquiring knowledge, on the other hand, refers to simply obtaining information or facts without necessarily understanding or internalizing it. Learning involves a deeper level of understanding and application of knowledge, while acquiring knowledge can be more passive and superficial.
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What is the best learning method for memorizing information?
The best learning method for memorizing information varies from person to person, as everyone has different learning styles. However, some effective methods for memorization include using mnemonic devices, practicing active recall, spaced repetition, and teaching the information to someone else. It is important to experiment with different techniques to find what works best for you and to incorporate a variety of methods for optimal retention of information.
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How can I obtain information on learning C programming?
You can obtain information on learning C programming by accessing online resources such as tutorials, forums, and websites dedicated to programming. There are also many books available on the topic that can provide comprehensive information and guidance on learning C programming. Additionally, you can consider enrolling in a programming course or workshop either in-person or online to receive structured instruction and support in learning C programming.
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The Teaching and Learning of Social Research Methods : Developments in Pedagogical Knowledge
The importance of the teaching and learning of social research methods is increasingly recognised by research councils and policy bodies as crucial to the drive to increase capacity amongst the research community.The need for greater scholarly engagement with how research methods are taught and learnt is also driven by the realisation that epistemological and methodological developments have not been accompanied by a pedagogical literature or culture.Training initiatives need this pedagogic input if they are to realise the educational aspirations for methodologically skilled and competent researchers, able to apply, adapt and reflect on a range of high-level research methods and approaches.The contributors to this collection have fully engaged with this need to develop and share pedagogical knowledge in relation to the teaching of research methods.Together they span qualitative, quantitative and mixed methods, a range of disciplinary and national contexts, and face-to-face and blended teaching and learning.Through detailed examples, the collection addresses how best teaching practices develop in response to distinctive challenges that will resonate with readers; in so doing it will inspire and inform their own development.This book was originally published as a special issue of the International Journal of Social Research Methodology.
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Multivariate Analysis and Machine Learning Techniques : Feature Analysis in Data Science Using Python
This book offers a comprehensive first-level introduction to data analytics.The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python.The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques – probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS.However, there is a dearth of textbooks that provide a comprehensiveintroduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language.The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets.It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics.The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics.It can be also used as a reference book by data analytics professionals.
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Information Technology and Organizational Learning : Managing Behavioral Change in the Digital Age
Because digital and information technology (IT) has become a more significant part of strategic advantage and workplace operations, information systems personnel have become key to the success of corporate enterprises, particularly with the pursuit of becoming more "digital." This book focuses on the vital role that technology must play in the course of organizational development and learning and on the growing need to integrate technology, particularly digital technology, fully into the culture of all organizations.Fundamentally this fourth edition takes an even stronger position than the previous editions that organizational learning is crucial to the success of what has been coined "digital transformation." Companies are struggling to understand what it means to "be digital." Their technology personnel go far beyond the traditional IT staff into areas such as artificial intelligence (AI), machine learning (ML), and natural language processing (NL).These three functions now fall under the auspices of "data science," which is now at the center of allowing companies to become more data dominant as is necessary for survival.While traditional IT personnel have long been criticized for their inability to function as part of the business, they are now vital to assist in the leadership of digital transformation.It could be a costly error to underestimate the technical skills needed by IT staff to ensure successful digital transformation.In fact, subsequent chapters will highlight the technical challenges needed to build new architectures based on 5G, blockchain, cloud computing, and eventually quantum processing.The challenge then is to integrate business and technical IT staff via cultural assimilation and to strategically integrate advanced computing architectures. This fourth edition includes new topics such as the future of work that addresses the challenges of assimilating multiple generations of employees and how to establish working cultures that are more resilient and adaptive and can be configured as a platform driven by data assets.
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The Shape Of Data : Geometry-Based Machine Learning and Data Analysis in R
The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning.Focused on practical applications rather than dense mathematical concepts, the book progresses through coding examples using social network data, text data, medical data, and education data.Readers will come away with an entirely new toolkit to use in their own machine-learning work, as well as with a solid understanding of some of the most exciting algorithms being used in the field today.
Price: 37.99 £ | Shipping*: 0.00 £
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Can you start learning to drive without any prior knowledge?
Yes, it is possible to start learning to drive without any prior knowledge. Many driving schools offer beginner courses for individuals who have never driven before. These courses typically start with the basics of operating a vehicle, understanding traffic laws, and practicing in a controlled environment. It's important to find a reputable driving school or instructor to ensure that you receive proper instruction and guidance as you begin your journey to becoming a safe and confident driver.
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Why deep learning compared to machine learning?
Deep learning is a subset of machine learning that uses neural networks to learn from data. It is more powerful than traditional machine learning techniques because it can automatically discover and learn from complex patterns and features in the data without the need for explicit feature engineering. Deep learning can handle large amounts of data and is capable of learning from unstructured data such as images, audio, and text, making it more versatile and effective for a wide range of applications. Additionally, deep learning models can continuously improve their performance with more data, making them more adaptable and scalable compared to traditional machine learning models.
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Does one absorb more information when listening than when learning?
The effectiveness of absorbing information depends on the individual's learning style. Some people may absorb more information when listening, as they are auditory learners and process information better through hearing. Others may absorb more information when learning through visual or kinesthetic methods. It ultimately comes down to personal preference and the individual's learning style.
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Which books are suitable for learning hacking, programming, and basic knowledge?
There are several books suitable for learning hacking, programming, and basic knowledge. For hacking, "The Web Application Hacker's Handbook" by Dafydd Stuttard and Marcus Pinto is a comprehensive guide to understanding web application security. For programming, "Python Crash Course" by Eric Matthes is a great resource for beginners to learn Python programming. For basic knowledge, "Computer Science Distilled" by Wladston Ferreira Filho provides a clear and concise introduction to computer science concepts. These books are suitable for individuals looking to gain knowledge and skills in hacking, programming, and basic computer science.
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