Machine Learning In Educational Sciences
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Machine Learning In Educational Sciences
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Author : Myint Swe Khine
language : en
Publisher: Springer Nature
Release Date :
Machine Learning In Educational Sciences written by Myint Swe Khine and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Regression And Machine Learning For Education Sciences Using R
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Author : Cody Dingsen
language : en
Publisher: Routledge
Release Date : 2024-11
Regression And Machine Learning For Education Sciences Using R written by Cody Dingsen and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11 with Business & Economics categories.
This book provides a conceptual introduction to regression and machine learning and its applications in education research. The book discusses its diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur. These identified important predictors provide data-based evidence for educational and psychological decision-making. Offering an applications-oriented approach while mapping out fundamental methodological developments, this book lays a sound foundation for understanding essential regression and machine learning concepts for data analytics. The first part of the book discusses regression analysis and provides a sturdy foundation to understand the logic of machine learning. With each chapter, the discussion and development of each statistical concept and data analytical technique are presented from an applied perspective, with the statistical results providing insights into decisions and solutions to problems using R. Based on practical examples, and written in a concise and accessible style, the book is learner-centric and does a remarkable job in breaking down complex concepts. Regression and Machine Learning for Education Sciences Using R is primarily for students or practitioners in education and psychology, although individuals from other related disciplines can also find the book beneficial. The dataset and examples used in the book will be from the educational setting, and students will find that this text provides good preparation for studying more statistical and data analytical materials.
Encyclopedia Of The Sciences Of Learning
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Author : Norbert M. Seel
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-10-05
Encyclopedia Of The Sciences Of Learning written by Norbert M. Seel and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-05 with Education categories.
Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.
Machine Learning Paradigms
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Author : George A. Tsihrintzis
language : en
Publisher: Springer Nature
Release Date : 2020-07-23
Machine Learning Paradigms written by George A. Tsihrintzis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-23 with Computers categories.
At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
Data Mining And Learning Analytics
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Author : Samira ElAtia
language : en
Publisher: John Wiley & Sons
Release Date : 2016-09-26
Data Mining And Learning Analytics written by Samira ElAtia and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-26 with Computers categories.
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
Artificial Intelligence Supported Educational Technologies
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Author : Niels Pinkwart
language : en
Publisher: Springer Nature
Release Date : 2020-04-29
Artificial Intelligence Supported Educational Technologies written by Niels Pinkwart and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-29 with Education categories.
This book includes a collection of expanded papers from the 2019 Sino-German Symposium on AI-supported educational technologies, which was held in Wuhan, China, March, 2019. The contributors are distinguished researchers from computer science and learning science. The contributions are organized in four sections: (1) Overviews and systematic perspectives , (2) Example Systems, (3) Algorithms, and (4) Insights gained from empirical studies. For example, different data mining and machine learning methods to quantify different profiles of a learner in different learning situations (including interaction patterns, cognitive modes, knowledge skills, interests and emotions etc.) as well as connections to measurements in psychology and learning sciences are discussed in the chapters.
Guide To Teaching Data Science
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Author : Orit Hazzan
language : en
Publisher: Springer Nature
Release Date : 2023-03-20
Guide To Teaching Data Science written by Orit Hazzan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-20 with Computers categories.
Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.
Applications Of Machine Learning And Artificial Intelligence In Education
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Author : Seda Khadimally
language : en
Publisher: Information Science Reference
Release Date : 2021
Applications Of Machine Learning And Artificial Intelligence In Education written by Seda Khadimally and has been published by Information Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Artificial intelligence categories.
Focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Topics covered include data coding and social networking technology.
In Order To Learn
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Author : Frank E. Ritter
language : en
Publisher: Oxford University Press
Release Date : 2007-07-30
In Order To Learn written by Frank E. Ritter and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-30 with Psychology categories.
Order affects the results you get: Different orders of presenting material can lead to qualitatively and quantitatively different learning outcomes. These differences occur in both natural and artificial learning systems. In Order to Learn shows how order effects are crucial in human learning, instructional design, machine learning, and both symbolic and connectionist cognitive models. Each chapter explains a different aspect of how the order in which material is presented can strongly influence what is learned by humans and theoretical models of learning in a variety of domains. In addition to data, models are provided that predict and describe order effects and analyze how and when they will occur. The introductory and concluding chapters compile suggestions for improving learning through better sequences of learning materials, including how to take advantage of order effects that encourage learning and how to avoid order effects that discourage learning. Each chapter also highlights questions that may inspire further research. Taken together, these chapters show how order effects in different areas can and do inform each other. In Order to Learn will be of interest to researchers and students in cognitive science, education, machine learning.
Machine Learning For Education
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Author : Dr. Amit Dua
language : en
Publisher: BPB Publications
Release Date : 2023-05-11
Machine Learning For Education written by Dr. Amit Dua and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-11 with Computers categories.
A guide to understand the basics of machine learning and its application in the field of education KEY FEATURES ● Create a more efficient and effective learning environment that meets the needs of all students. ● Learn how to use the profound Machine learning advancements in the field of education. ● Understand how students can use machine learning tools to improve their learning and track their progress. DESCRIPTION "Machine Learning for Education" is a book that explores the application of machine learning techniques in the field of education. It covers a wide range of topics, including personalizing learning using machine learning, improving student outcomes, and optimizing educational resources. The book delves into various machine learning techniques such as supervised and unsupervised learning, deep learning, and reinforcement learning, and how they can be applied to different areas of education such as curriculum design, assessment, and student engagement. The book takes a novel approach to explain the applications of machine learning in education through relatable real-life examples, existing problems and proposing thought-provoking solutions to the same. At the end of each chapter, there are crossword puzzles, word grids, and comprehensive exercises designed to reinforce the concepts discussed in the chapter and ensure a thorough understanding. After reading the book, you will have gained the ability to implement the various features and solutions offered by machine learning within the field of education. WHAT YOU WILL LEARN ● Get familiar with the fundamentals of Machine Learning. ● Explore different Machine learning solutions for primary and secondary education. ● Discover ways to use Machine learning enhanced teaching and learning. ● Understand how AI can be used to assess your child's academic progress. ● Understand how Machine learning can assist educational administrators. WHO THIS BOOK IS FOR The book is suitable for anyone who is directly or indirectly involved in the current education landscape, including students, parents, teachers, and educational administrators. No previous knowledge of machine learning is required. TABLE OF CONTENTS 1. Basics of Machine Learning 2. Machine Learning for Students 3. Machine Learning for Parents 4. Machine Learning for Teachers 5. Machine Learning for Educational Administrators