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Predicting Student Performance Using Machine Learning Analytics
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Book Synopsis Predicting Student Performance Using Machine Learning Analytics by : Tatenda T. Taodzera
Download or read book Predicting Student Performance Using Machine Learning Analytics written by Tatenda T. Taodzera and published by . This book was released on 2018 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence Applications and Innovations by : Lazaros S. Iliadis
Download or read book Artificial Intelligence Applications and Innovations written by Lazaros S. Iliadis and published by Springer. This book was released on 2013-11-27 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th International Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 International Conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. The 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. The second volume includes the papers that were accepted for presentation at the AIAI 2011 conference. They are organized in topical sections on computer vision and robotics, classification/pattern recognition, financial and management applications of AI, fuzzy systems, learning and novel algorithms, recurrent and radial basis function ANN, machine learning, generic algorithms, data mining, reinforcement learning, Web applications of ANN, medical applications of ANN and ethics of AI, and environmental and earth applications of AI. The volume also contains the accepted papers from the First Workshop on Computational Intelligence in Software Engineering (CISE 2011) and the Workshop on Artificial Intelligence Applications in Biomedicine (AIAB 2011).
Book Synopsis 1st International Conference on Learning Analytics and Knowledge by : George Siemens
Download or read book 1st International Conference on Learning Analytics and Knowledge written by George Siemens and published by . This book was released on 2011-02-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 1st International Conference on Learning Analytics and Knowledge Feb 27, 2011-Mar 01, 2011 Banff, Canada. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Book Synopsis Machine Learning Paradigms by : George A. Tsihrintzis
Download or read book Machine Learning Paradigms written by George A. Tsihrintzis and published by Springer Nature. This book was released on 2020-07-23 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Mining and Learning Analytics by : Samira ElAtia
Download or read book Data Mining and Learning Analytics written by Samira ElAtia and published by John Wiley & Sons. This book was released on 2016-09-20 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Improving Student Retention in Higher Education by : Glenda Crosling
Download or read book Improving Student Retention in Higher Education written by Glenda Crosling and published by Routledge. This book was released on 2008-11-19 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Underpinned by research this book provides best practice examples of innovative and inclusive curriculum designined to improve student retention in HE.
Book Synopsis Evolution in Computational Intelligence by : Vikrant Bhateja
Download or read book Evolution in Computational Intelligence written by Vikrant Bhateja and published by Springer Nature. This book was released on 2020-09-08 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), which aims to bring together researchers, scientists, engineers and practitioners to share new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. The book is divided into two volumes: Evolution in Computational Intelligence (Volume 1) and Intelligent Data Engineering and Analytics (Volume 2). Covering a broad range of topics in computational intelligence, the book features papers on theoretical as well as practical aspects of areas such as ANN and genetic algorithms, computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, and multi-agent systems. As such, it is a valuable reference resource for postgraduate students in various engineering disciplines.
Book Synopsis Data Science in Education Using R by : Ryan A. Estrellado
Download or read book Data Science in Education Using R written by Ryan A. Estrellado and published by Routledge. This book was released on 2020-10-26 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Innovative Data Communication Technologies and Application by : Jennifer S. Raj
Download or read book Innovative Data Communication Technologies and Application written by Jennifer S. Raj and published by Springer Nature. This book was released on 2021-02-02 with total page 872 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest research in the fields of computational intelligence, ubiquitous computing models, communication intelligence, communication security, machine learning, informatics, mobile computing, cloud computing and big data analytics. The best selected papers, presented at the International Conference on Innovative Data Communication Technologies and Application (ICIDCA 2020), are included in the book. The book focuses on the theory, design, analysis, implementation and applications of distributed systems and networks.
Book Synopsis Data Mining and Learning Analytics by : Olugbenga Wilson Adejo
Download or read book Data Mining and Learning Analytics written by Olugbenga Wilson Adejo and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fundamentals of Machine Learning for Predictive Data Analytics, second edition by : John D. Kelleher
Download or read book Fundamentals of Machine Learning for Predictive Data Analytics, second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Book Synopsis Learning Analytics in Higher Education by : John Zilvinskis
Download or read book Learning Analytics in Higher Education written by John Zilvinskis and published by John Wiley & Sons. This book was released on 2017-10-16 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this volume is to introduce the reader to a basic understanding of learning analytics and the types of projects and initiatives that several leading practitioners have adopted and adapted, providing substantive examples of implementation, and expert learnings on some of the more nuanced issues related to this topic"--Page 5.
Book Synopsis The Future of Innovation and Technology in Education by : Anna Visvizi
Download or read book The Future of Innovation and Technology in Education written by Anna Visvizi and published by Emerald Group Publishing. This book was released on 2018-11-30 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the effective use of information and communication technology (ICT) in teaching and learning. Concept-laden and practice-driven discussions offer insights into the art and practice of employing virtual and augmented reality (VR/AR), electronic devices, social networks and massive open online courses (MOOCs) in education.
Book Synopsis Handbook of Research on Emerging Trends and Applications of Machine Learning by : Solanki, Arun
Download or read book Handbook of Research on Emerging Trends and Applications of Machine Learning written by Solanki, Arun and published by IGI Global. This book was released on 2019-12-13 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.
Book Synopsis Data Analytics and Machine Learning to Understand and Predict Student Performance by : Azhan Rashid
Download or read book Data Analytics and Machine Learning to Understand and Predict Student Performance written by Azhan Rashid and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Big Data on Campus by : Karen L. Webber
Download or read book Big Data on Campus written by Karen L. Webber and published by Johns Hopkins University Press. This book was released on 2020-11-03 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Webber, Henry Y. Zheng, Ying Zhou
Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.