Educational Data

Author : Alejandro Peña-Ayala
Publisher : Springer
Page : 477 pages
File Size : 45,8 Mb
Release : 2013-11-08
Category : Technology & Engineering
ISBN : 9783319027388

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Educational Data Mining by Alejandro Peña-Ayala Pdf

This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the student's academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data. · Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks. This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.

Author : Ann A. O'Connell,D. Betsy McCoach
Publisher : IAP
Page : 541 pages
File Size : 42,6 Mb
Release : 2008-04-01
Category : Education
ISBN : 9781607527299

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Multilevel Modeling of Educational Data by Ann A. O'Connell,D. Betsy McCoach Pdf

(sponsored by the Educational Statisticians, SIG) Multilevel Modeling of Educational Data, co-edited by Ann A. O’Connell, Ed.D., and D. Betsy McCoach, Ph.D., is the next volume in the series: Quantitative Methods in Education and the Behavioral Sciences: Issues, Research and Teaching (Information Age Publishing), sponsored by the Educational Statisticians' Special Interest Group (Ed-Stat SIG) of the American Educational Research Association. The use of multilevel analyses to examine effects of groups or contexts on individual outcomes has burgeoned over the past few decades. Multilevel modeling techniques allow educational researchers to more appropriately model data that occur within multiple hierarchies (i.e.- the classroom, the school, and/or the district). Examples of multilevel research problems involving schools include establishing trajectories of academic achievement for children within diverse classrooms or schools or studying school-level characteristics on the incidence of bullying. Multilevel models provide an improvement over traditional single-level approaches to working with clustered or hierarchical data; however, multilevel data present complex and interesting methodological challenges for the applied education research community. In keeping with the pedagogical focus for this book series, the papers this volume emphasize applications of multilevel models using educational data, with chapter topics ranging from basic to advanced. This book represents a comprehensive and instructional resource text on multilevel modeling for quantitative researchers who plan to use multilevel techniques in their work, as well as for professors and students of quantitative methods courses focusing on multilevel analysis. Through the contributions of experienced researchers and teachers of multilevel modeling, this volume provides an accessible and practical treatment of methods appropriate for use in a first and/or second course in multilevel analysis. A supporting website links chapter examples to actual data, creating an opportunity for readers to reinforce their knowledge through hands-on data analysis. This book serves as a guide for designing multilevel studies and applying multilevel modeling techniques in educational and behavioral research, thus contributing to a better understanding of and solution for the challenges posed by multilevel systems and data.

Author : Ryan A. Estrellado,Emily A. Freer,Jesse Mostipak,Joshua M. Rosenberg,Isabella C. Velásquez
Publisher : Routledge
Page : 315 pages
File Size : 40,9 Mb
Release : 2020-10-26
Category : Education
ISBN : 9781000200904

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Data Science in Education Using R by Ryan A. Estrellado,Emily A. Freer,Jesse Mostipak,Joshua M. Rosenberg,Isabella C. Velásquez Pdf

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.

Author : Cristobal Romero,Sebastian Ventura,Mykola Pechenizkiy,Ryan S.J.d. Baker
Publisher : CRC Press
Page : 528 pages
File Size : 40,7 Mb
Release : 2010-10-25
Category : Business & Economics
ISBN : 9781439804582

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Handbook of Educational Data Mining by Cristobal Romero,Sebastian Ventura,Mykola Pechenizkiy,Ryan S.J.d. Baker Pdf

This handbook provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It helps education experts understand what types of questions EDM can address and helps data miners understand what types of questions are important to educational design and educational decision making.

Author : Grace I. Krumwiede
Publisher : Unknown
Page : 16 pages
File Size : 47,5 Mb
Release : 1962
Category : Education
ISBN : MINN:31951D035177940

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Educational Data by Grace I. Krumwiede Pdf

Author : Margaret Lumpkin King
Publisher : Unknown
Page : 12 pages
File Size : 50,5 Mb
Release : 1964
Category : Education
ISBN : MINN:31951D03517798S

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Educational Data by Margaret Lumpkin King Pdf

Author : Bhatt, Chintan,Sajja, Priti Srinivas,Liyanage, Sidath
Publisher : IGI Global
Page : 166 pages
File Size : 50,9 Mb
Release : 2019-08-02
Category : Education
ISBN : 9781799800125

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Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities by Bhatt, Chintan,Sajja, Priti Srinivas,Liyanage, Sidath Pdf

Modern education has increased its reach through ICT tools and techniques. To manage educational data with the help of modern artificial intelligence, data and web mining techniques on dedicated cloud or grid platforms for educational institutes can be used. By utilizing data science techniques to manage educational data, the safekeeping, delivery, and use of knowledge can be increased for better quality education. Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities is a critical scholarly resource that explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, academicians, and technology experts.

Author : Soni Sweta
Publisher : Springer Nature
Page : 117 pages
File Size : 55,8 Mb
Release : 2021-01-22
Category : Technology & Engineering
ISBN : 9789813346819

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Modern Approach to Educational Data Mining and Its Applications by Soni Sweta Pdf

This book emphasizes that learning efficiency of the learners can be increased by providing personalized course materials and guiding them to attune with suitable learning paths based on their characteristics such as learning style, knowledge level, emotion, motivation, self-efficacy and many more learning ability factors in e-learning system. Learning is a continuous process since human evolution. In fact, it is related to life and innovations. The basic objective of learning to grow, aspire and develop ease of life remains the same despite changes in the learning methodologies. Introduction of computers empowered us to attain new zenith in knowledge domain, developed pragmatic approach to solve life’s problem and helped us to decipher different hidden patterns of data to get new ideas. Of late, computers are predominantly used in education. Its process has been changed from offline to online in view of enhancing the ease of learning. With the advent of information technology, e-learning has taken centre stage in educational domain. In e-learning context, developing adaptive e-learning system is buzzword among contemporary research scholars in the area of Educational Data Mining (EDM). Enabling personalized systems is meant for improvement in learning experience for learners as per their choices made or auto-detected needs. It helps in enhancing their performance in terms of knowledge, skills, aptitudes and preferences. It also enables speeding up the learning process qualitatively and quantitatively. These objectives are met only by the Personalized Adaptive E-learning Systems in this regard. Many noble frameworks were conceptualized, designed and developed to infer learning style preferences, and accordingly, learning materials were delivered adaptively to the learners. Designing frameworks help to measure learners’ preferences minutely and provide adaptive learning materials to them in a way most appropriately.

Author : Soni Sweta
Publisher : Springer Nature
Page : 116 pages
File Size : 40,9 Mb
Release : 2024-07-03
Category : Electronic
ISBN : 9789819724741

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Sentiment Analysis and its Application in Educational Data Mining by Soni Sweta Pdf

Author : Samira ElAtia,Donald Ipperciel,Osmar R. Zaïane
Publisher : John Wiley & Sons
Page : 320 pages
File Size : 44,7 Mb
Release : 2016-09-20
Category : Computers
ISBN : 9781118998212

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Data Mining and Learning Analytics by Samira ElAtia,Donald Ipperciel,Osmar R. Zaïane Pdf

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.

Author : Ryan A. Estrellado
Publisher : Taylor & Francis
Page : 195 pages
File Size : 45,7 Mb
Release : 2022-03-21
Category : Education
ISBN : 9781000552935

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The K-12 Educator’s Data Guidebook by Ryan A. Estrellado Pdf

The K-12 Educator’s Data Guidebook is a comprehensive field guide for school professionals learning to use data. “Non-data people,” rejoice! Requiring no prior proficiency in data tools and programming, this book validates the implicit challenges of learning to use data to empower educators and features original real-world examples from in-service educators to illustrate common problem-solving. Each chapter uses stories, humor, and a human approach to set the tone for a safe and fun learning experience. Through this highly practical foundation, everyday educators can better engage school initiatives, professional development, and instructional challenges that require competent data use for improving school systems.

Author : Barbara A. Feller
Publisher : Unknown
Page : 196 pages
File Size : 48,7 Mb
Release : 1976
Category : Education
ISBN : STANFORD:36105003537698

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Directory of Federal Agency Education Data Tapes by Barbara A. Feller Pdf

Author : National Forum on Education Statistics (U.S.). National Education Statistics Agenda Committee
Publisher : Unknown
Page : 184 pages
File Size : 43,5 Mb
Release : 1991
Category : Education
ISBN : UIUC:30112105066127

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A Guide to Improving the National Education Data System by National Forum on Education Statistics (U.S.). National Education Statistics Agenda Committee Pdf

Author : Ben Williamson
Publisher : SAGE
Page : 257 pages
File Size : 40,7 Mb
Release : 2017-07-24
Category : Education
ISBN : 9781526416346

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Big Data in Education by Ben Williamson Pdf

This cutting-edge overview explores big data and the related topic of computer code, examining the implications for education and schooling for today and the near future.

Author : Wang, John
Publisher : IGI Global
Page : 3296 pages
File Size : 47,9 Mb
Release : 2023-01-20
Category : Computers
ISBN : 9781799892212

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Encyclopedia of Data Science and Machine Learning by Wang, John Pdf

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.