Predicting Student Performance Using Machine Learning Analytics
Author: Tatenda T. Taodzera
Publisher:
Published: 2018
Total Pages: 72
ISBN-13:
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Artificial Intelligence Applications and Innovations
Author: Lazaros S. Iliadis
Publisher: Springer
Published: 2013-11-27
Total Pages: 496
ISBN-13: 9783642269653
DOWNLOAD EBOOKThe 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).
1st International Conference on Learning Analytics and Knowledge
Author: George Siemens
Publisher:
Published: 2011-02-27
Total Pages:
ISBN-13: 9781450310574
DOWNLOAD EBOOK1st 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.
Machine Learning Paradigms
Author: George A. Tsihrintzis
Publisher: Springer Nature
Published: 2020-07-23
Total Pages: 429
ISBN-13: 3030497240
DOWNLOAD EBOOKAt 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
Author: Samira ElAtia
Publisher: John Wiley & Sons
Published: 2016-09-20
Total Pages: 351
ISBN-13: 1118998219
DOWNLOAD EBOOKAddresses 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.
Research Design & Statistical Analysis
Author: Arnold D. Well
Publisher: Psychology Press
Published: 2003-01-30
Total Pages: 871
ISBN-13: 1135641080
DOWNLOAD EBOOK"Free CD contains several real and artificial data sets used in the book in SPSS, SYSTAT, and ASCII formats"--Cover
Data Science in Education Using R
Author: Ryan A. Estrellado
Publisher: Routledge
Published: 2020-10-26
Total Pages: 315
ISBN-13: 1000200906
DOWNLOAD EBOOKData 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.
Evolution in Computational Intelligence
Author: Vikrant Bhateja
Publisher: Springer Nature
Published: 2020-09-08
Total Pages: 780
ISBN-13: 9811557888
DOWNLOAD EBOOKThis 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.
Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Author: John D. Kelleher
Publisher: MIT Press
Published: 2020-10-20
Total Pages: 853
ISBN-13: 0262361108
DOWNLOAD EBOOKThe 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.
Improving Student Retention in Higher Education
Author: Glenda Crosling
Publisher: Routledge
Published: 2008-11-19
Total Pages: 207
ISBN-13: 1134149778
DOWNLOAD EBOOKUnderpinned by research this book provides best practice examples of innovative and inclusive curriculum designined to improve student retention in HE.
