Data Analytics Applications In Education

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Data Analytics Applications in Education

Jan Vanthienen,Kristof De Witte
Data Analytics Applications in Education Book PDF

Author : Jan Vanthienen,Kristof De Witte
Publisher : CRC Press
Page : 259 pages
File Size : 53,8 Mb
Release : 2017-09-29
Category : Business & Economics
ISBN : 9781351650182

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Data Analytics Applications in Education by Jan Vanthienen,Kristof De Witte Pdf

The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience. As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student’ s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely. Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom.

Data Analytics Applications in Education

Jan Vanthienen,Kristof De Witte
Data Analytics Applications in Education Book PDF

Author : Jan Vanthienen,Kristof De Witte
Publisher : CRC Press
Page : 265 pages
File Size : 41,5 Mb
Release : 2017-09-29
Category : Business & Economics
ISBN : 9781498769280

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Data Analytics Applications in Education by Jan Vanthienen,Kristof De Witte Pdf

The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience. As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student’ s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely. Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom.

Data Mining and Learning Analytics

Samira ElAtia,Donald Ipperciel,Osmar R. Zaïane
Data Mining and Learning Analytics Book PDF

Author : Samira ElAtia,Donald Ipperciel,Osmar R. Zaïane
Publisher : John Wiley & Sons
Page : 320 pages
File Size : 54,6 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.

Intelligent Systems and Learning Data Analytics in Online Education

Santi Caballé,Stavros N. Demetriadis,Eduardo Gómez-Sánchez,Pantelis M. Papadopoulos,Armin Weinberger
Intelligent Systems and Learning Data Analytics in Online Education Book PDF

Author : Santi Caballé,Stavros N. Demetriadis,Eduardo Gómez-Sánchez,Pantelis M. Papadopoulos,Armin Weinberger
Publisher : Academic Press
Page : 427 pages
File Size : 49,5 Mb
Release : 2021-06-15
Category : Computers
ISBN : 9780128231272

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Intelligent Systems and Learning Data Analytics in Online Education by Santi Caballé,Stavros N. Demetriadis,Eduardo Gómez-Sánchez,Pantelis M. Papadopoulos,Armin Weinberger Pdf

Intelligent Systems and Learning Data Analytics in Online Education provides novel artificial intelligence (AI) and analytics-based methods to improve online teaching and learning. This book addresses key problems such as attrition and lack of engagement in MOOCs and online learning in general. This book explores the state of the art of artificial intelligence, software tools and innovative learning strategies to provide better understanding and solutions to the various challenges of current e-learning in general and MOOC education. In particular, Intelligent Systems and Learning Data Analytics in Online Education shares stimulating theoretical and practical research from leading international experts. This publication provides useful references for educational institutions, industry, academic researchers, professionals, developers, and practitioners to evaluate and apply. Presents the application of innovative AI techniques to collaborative learning activities Offers strategies to provide automatic and effective tutoring to students’ activities Offers methods to collect, analyze and correctly visualize learning data in educational environments

Data Mining and Learning Analytics

Samira ElAtia,Donald Ipperciel,Osmar R. Zaïane
Data Mining and Learning Analytics Book PDF

Author : Samira ElAtia,Donald Ipperciel,Osmar R. Zaïane
Publisher : John Wiley & Sons
Page : 320 pages
File Size : 52,9 Mb
Release : 2016-09-26
Category : Computers
ISBN : 9781118998236

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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.

Teaching Data Analytics

Susan A Vowels,Katherine Leaming Goldberg
Teaching Data Analytics Book PDF

Author : Susan A Vowels,Katherine Leaming Goldberg
Publisher : CRC Press
Page : 202 pages
File Size : 55,9 Mb
Release : 2019-06-17
Category : Computers
ISBN : 9781351721455

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Teaching Data Analytics by Susan A Vowels,Katherine Leaming Goldberg Pdf

The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and career opportunities. The book features: A variety of perspectives ranging from the scholarly theoretical to the practitioner applied An in-depth look into a wide breadth of skills from closely technology-focused to robustly soft human connection skills Resources for existing faculty to acquire and maintain additional analytics-relevant skills that can enrich their current course offerings. Acknowledging the dichotomy between data analytics and data science, this book emphasizes data analytics rather than data science, although the book does touch upon the data science realm. Starting with industry perspectives, the book covers the applied world of data analytics, covering necessary skills and applications, as well as developing compelling visualizations. It then dives into pedagogical and program design approaches in data analytics education and concludes with ideas for program design tactics. This reference is a launching point for discussions about how to connect industry’s need for skilled data analysts to higher education’s need to design a rigorous curriculum that promotes student critical thinking, communication, and ethical skills. It also provides insight into adding new elements to existing data analytics courses and for taking the next step in adding data analytics offerings, whether it be incorporating additional analytics assignments into existing courses, offering one course designed for undergraduates, or an integrated program designed for graduate students.

Applications of Big Data Analytics

Mohammed M. Alani,Hissam Tawfik,Mohammed Saeed,Obinna Anya
Applications of Big Data Analytics Book PDF

Author : Mohammed M. Alani,Hissam Tawfik,Mohammed Saeed,Obinna Anya
Publisher : Springer
Page : 214 pages
File Size : 44,9 Mb
Release : 2018-07-23
Category : Computers
ISBN : 9783319764726

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Applications of Big Data Analytics by Mohammed M. Alani,Hissam Tawfik,Mohammed Saeed,Obinna Anya Pdf

This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.

Data Science in Education Using R

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

Author : Ryan A. Estrellado,Emily A. Freer,Jesse Mostipak,Joshua M. Rosenberg,Isabella C. Velásquez
Publisher : Routledge
Page : 315 pages
File Size : 43,8 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.

Formative Assessment, Learning Data Analytics and Gamification

Santi Caballé,Robert Clarisó
Formative Assessment Learning Data Analytics and Gamification Book PDF

Author : Santi Caballé,Robert Clarisó
Publisher : Morgan Kaufmann
Page : 382 pages
File Size : 49,6 Mb
Release : 2016-05-10
Category : Computers
ISBN : 9780128036679

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Formative Assessment, Learning Data Analytics and Gamification by Santi Caballé,Robert Clarisó Pdf

Formative Assessment, Learning Data Analytics and Gamification: An ICT Education discusses the challenges associated with assessing student progress given the explosion of e-learning environments, such as MOOCs and online courses that incorporate activities such as design and modeling. This book shows educators how to effectively garner intelligent data from online educational environments that combine assessment and gamification. This data, when used effectively, can have a positive impact on learning environments and be used for building learner profiles, community building, and as a tactic to create a collaborative team. Using numerous illustrative examples and theoretical and practical results, leading international experts discuss application of automatic techniques for e-assessment of learning activities, methods to collect, analyze, and correctly visualize learning data in educational environments, applications, benefits and challenges of using gamification techniques in academic contexts, and solutions and strategies for increasing student participation and performance. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS Discusses application of automatic techniques for e-assessment of learning activities Presents strategies to provide immediate and useful feedback on students’ activities Provides methods to collect, analyze, and correctly visualize learning data in educational environments Explains the applications, benefits, and challenges of using gamification techniques in academic contexts Offers solutions to increase students’ participation and performance while lowering drop-out rates and retention levels

Adoption of Data Analytics in Higher Education Learning and Teaching

Dirk Ifenthaler,David Gibson
Adoption of Data Analytics in Higher Education Learning and Teaching Book PDF

Author : Dirk Ifenthaler,David Gibson
Publisher : Springer Nature
Page : 464 pages
File Size : 55,9 Mb
Release : 2020-08-10
Category : Education
ISBN : 9783030473921

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Adoption of Data Analytics in Higher Education Learning and Teaching by Dirk Ifenthaler,David Gibson Pdf

The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.

Learning Analytics in Higher Education

Jaime Lester,Carrie Klein,Aditya Johri,Huzefa Rangwala
Learning Analytics in Higher Education Book PDF

Author : Jaime Lester,Carrie Klein,Aditya Johri,Huzefa Rangwala
Publisher : Routledge
Page : 200 pages
File Size : 55,7 Mb
Release : 2018-08-06
Category : Education
ISBN : 9781351400527

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Learning Analytics in Higher Education by Jaime Lester,Carrie Klein,Aditya Johri,Huzefa Rangwala Pdf

Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment. Well-known contributors provide empirical, theoretical, and practical perspectives on the current use and future potential of learning analytics for student learning and data-driven decision-making, ways to effectively evaluate and research learning analytics, integration of learning analytics into practice, organizational barriers and opportunities for harnessing Big Data to create and support use of these tools, and ethical considerations related to privacy and consent. Designed to give readers a practical and theoretical foundation in learning analytics and how data can support student success in higher education, this book is a valuable resource for scholars and administrators.

Big Data in Education: Pedagogy and Research

Theodosia Prodromou
Big Data in Education Pedagogy and Research Book PDF

Author : Theodosia Prodromou
Publisher : Springer Nature
Page : 249 pages
File Size : 45,7 Mb
Release : 2021-10-04
Category : Education
ISBN : 9783030768416

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Big Data in Education: Pedagogy and Research by Theodosia Prodromou Pdf

This book discusses how Big Data could be implemented in educational settings and research, using empirical data and suggesting both best practices and areas in which to invest future research and development. It also explores: 1) the use of learning analytics to improve learning and teaching; 2) the opportunities and challenges of learning analytics in education. As Big Data becomes a common part of the fabric of our world, education and research are challenged to use this data to improve educational and research systems, and also are tasked with teaching coming generations to deal with Big Data both effectively and ethically. The Big Data era is changing the data landscape for statistical analysis, the ways in which data is captured and presented, and the necessary level of statistical literacy to analyse and interpret data for future decision making. The advent of Big Data accentuates the need to enable citizens to develop statistical skills, thinking and reasoning needed for representing, integrating and exploring complex information. This book offers guidance to researchers who are seeking suitable topics to explore. It presents research into the skills needed by data practitioners (data analysts, data managers, statisticians, and data consumers, academics), and provides insights into the statistical skills, thinking and reasoning needed by educators and researchers in the future to work with Big Data. This book serves as a concise reference for policymakers, who must make critical decisions regarding funding and applications.

From Data to Decisions in Music Education Research

Brian C. Wesolowski
From Data to Decisions in Music Education Research Book PDF

Author : Brian C. Wesolowski
Publisher : Routledge
Page : 662 pages
File Size : 46,9 Mb
Release : 2022-02-22
Category : Music
ISBN : 9781000534764

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From Data to Decisions in Music Education Research by Brian C. Wesolowski Pdf

From Data to Decisions in Music Education Research provides a structured and hands-on approach to working with empirical data in the context of music education research. Using step-by-step tutorials with in-depth examples of music education data, this book draws upon concepts in data science and statistics to provide a comprehensive framework for working with a variety of data and solving data-driven problems. All of the skills presented here use the R programming language, a free, open-source statistical computing and graphics environment. Using R enables readers to refine their computational thinking abilities and data literacy skills while facilitating reproducibility, replication, and transparency of data analysis in the field. The book offers: A clear and comprehensive framework for thinking about data analysis processes in a music education context. An overview of common data structures and data types used in statistical programming and data analytics. Techniques for cleaning, preprocessing, manipulating, aggregating, and mining data in ways that facilitate organization and interpretation. Methods for summarizing and visualizing data to help identify structures, patterns, and trends within data sets. Detailed applications of descriptive, diagnostic, and predictive analytics processes. Step-by-step code for all concepts and analyses. Direct access to all data sets and R script files through the accompanying eResource. From Data to Decisions in Music Education Research offers a reference "cookbook" of code and programming recipes written with the graduate music education student in mind and breaks down data analysis processes and skills in an approachable fashion. It can be used across a wide range of graduate music education courses that rely on the application of empirical data analyses and will be useful to all music education scholars and professionals seeking to enhance their use of quantitative data.

Big Data and Learning Analytics in Higher Education

Ben Kei Daniel
Big Data and Learning Analytics in Higher Education Book PDF

Author : Ben Kei Daniel
Publisher : Springer
Page : 272 pages
File Size : 54,9 Mb
Release : 2018-04-21
Category : Education
ISBN : 3319791516

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Big Data and Learning Analytics in Higher Education by Ben Kei Daniel Pdf

​This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning​. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns​.

Data Analytics Applications in Latin America and Emerging Economies

Eduardo Rodriguez
Data Analytics Applications in Latin America and Emerging Economies Book PDF

Author : Eduardo Rodriguez
Publisher : CRC Press
Page : 272 pages
File Size : 44,6 Mb
Release : 2017-07-28
Category : Business & Economics
ISBN : 9781351673174

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Data Analytics Applications in Latin America and Emerging Economies by Eduardo Rodriguez Pdf

This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions. The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners. This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.