Regression And Machine Learning For Education Sciences Using R
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Regression And Machine Learning For Education Sciences Using R
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Author : Cody Dingsen
language : en
Publisher: Taylor & Francis
Release Date : 2024-11-01
Regression And Machine Learning For Education Sciences Using R written by Cody Dingsen and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-01 with Psychology categories.
This book provides a conceptual introduction to regression analysis and machine learning and their applications in education research. It discusses their 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 is 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 are from an educational setting, and students will find that this text provides a good preparation ground for studying more statistical and data analytical materials.
Data Science In Education Using R
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Author : Ryan A. Estrellado
language : en
Publisher: Routledge
Release Date : 2020-10-26
Data Science In Education Using R written by Ryan A. Estrellado and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-26 with Education categories.
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
Machine Learning In Educational Sciences
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Author : Myint Swe Khine
language : en
Publisher: Springer Nature
Release Date : 2024-02-24
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 2024-02-24 with Education categories.
This comprehensive volume investigates the untapped potential of machine learning in educational settings. It examines the profound impact machine learning can have on reshaping educational research. Each chapter delves into specific applications and advancements, sheds light on theory-building, and multidisciplinary research, and identifies areas for further development. It encompasses various topics, such as machine-based learning in psychological assessment. It also highlights the power of machine learning in analyzing large-scale international assessment data and utilizing natural language processing for science education. With contributions from leading scholars in the field, this book provides a comprehensive, evidence-based framework for leveraging machine-learning approaches to enhance educational outcomes. The book offers valuable insights and recommendations that could help shape the future of educational sciences.
Applying Machine Learning In Science Education Research
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Author : Peter Wulff
language : en
Publisher: Springer Nature
Release Date : 2025-02-28
Applying Machine Learning In Science Education Research written by Peter Wulff and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-28 with Science categories.
This open access textbook offers science education researchers a hands-on guide for learning, critically examining, and integrating machine learning (ML) methods into their science education research projects. These methods power many artificial intelligence (AI)-based technologies and are widely adopted in science education research. ML can expand the methodological toolkit of science education researchers and provide novel opportunities to gain insights on science-related learning and teaching processes, however, applying ML poses novel challenges and is not suitable for every research context. The volume first introduces the theoretical underpinnings of ML methods and their connections to methodological commitments in science education research. It then presents exemplar case studies of ML uses in both formal and informal science education settings. These case studies include open-source data, executable programming code, and explanations of the methodological criteria and commitments guiding ML use in each case. The textbook concludes with a discussion of opportunities and potential future directions for ML in science education. This textbook is a valuable resource for science education lecturers, researchers, under-graduate, graduate and postgraduate students seeking new ways to apply ML in their work.
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.
Hands On Machine Learning With R
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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07
Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Data Science And Algorithms In Systems
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Author : Radek Silhavy
language : en
Publisher: Springer Nature
Release Date : 2023-01-03
Data Science And Algorithms In Systems written by Radek Silhavy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-03 with Technology & Engineering categories.
This book offers real-world data science and algorithm design topics linked to systems and software engineering. Furthermore, articles describing unique techniques in data science, algorithm design, and systems and software engineering are featured. This book is the second part of the refereed proceedings of the 6th Computational Methods in Systems and Software 2022 (CoMeSySo 2022). The CoMeSySo 2022 conference, which is being hosted online, is breaking down barriers. CoMeSySo 2022 aims to provide a worldwide venue for debate of the most recent high-quality research findings.
Proceedings Of The Third International Conference On Trends In Computational And Cognitive Engineering
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Author : M. Shamim Kaiser
language : en
Publisher: Springer Nature
Release Date : 2022-02-28
Proceedings Of The Third International Conference On Trends In Computational And Cognitive Engineering written by M. Shamim Kaiser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-28 with Technology & Engineering categories.
This book presents various computational and cognitive modeling approaches in the areas of health, education, finance, environment, engineering, commerce, and industry. It is a collection of selected conference papers presented at the 3rd International Conference on Trends in Cognitive Computation Engineering (TCCE 2021), hosted online by Universiti Tun Hussein Onn Malaysia (UTHM) during October 21–22, 2021. It shares cutting-edge insights and ideas from mathematicians, engineers, scientists, and researchers and discusses fresh perspectives on problem solving in a range of research areas.
Artificial Intelligence For Education
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Author : Mario Allegra
language : en
Publisher: Frontiers Media SA
Release Date : 2023-11-27
Artificial Intelligence For Education written by Mario Allegra and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-27 with Education categories.
What learning, teaching, and education will be in the next future is an open question. Nevertheless, believing that an increasing prevalence of AI may not influence the education field seems objectively unlikely. In recent years, the new renaissance of AI has stimulated discussion on how advances in AI can influence the educational sector and the future educational policies and the impact of AI on Technology-Enhanced Learning (TEL). On the other side, the attention of the education sector in artificial intelligence is complemented by the consideration that, since the early days of AI, researchers have shown for the education sector, which has often seen education as one of the preferred application areas. The interaction between the AI and TEL research fields led to the investigation of how the advance in AI could support the development of flexible, inclusive, personalized, engaging, and effective learning tools. Besides, research in this area could be a powerful tool to open the "learning black box" by providing a deeper understanding of how learning occurs. The proposed Research Topic aims to gather contributions that provide a comprehensive picture of how AI is changing educational practices and how the key stakeholders in the educational community (i.e., students, teachers, faculty, and families) perceive this ongoing change. Relevant topics include (but are not limited to): ● AI applications in real-world educational settings ● Intelligent Tutoring Systems ● Adaptive learning environments ● Learning design and AI ● Students profiling: definition of the student model and ethical implications ● Intelligent techniques for objective and integrated students evaluation in TEL ● Teachers' competencies for effective integration of AI into Education ● Teachers’ perceptions of AI: prejudices and attitudes ● The role of cognitive architectures in Education ● Serious games and AI ● Social robotics in Education
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results