Data Analytics In E Learning Approaches And Applications

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Data Analytics in e-Learning: Approaches and Applications

Marian Cristian Mihăescu
Data Analytics in e Learning Approaches and Applications Book PDF

Author : Marian Cristian Mihăescu
Publisher : Springer Nature
Page : 167 pages
File Size : 42,9 Mb
Release : 2022-03-22
Category : Computers
ISBN : 9783030966447

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Data Analytics in e-Learning: Approaches and Applications by Marian Cristian Mihăescu Pdf

This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.

Data Analytics in E-Learning: Approaches and Applications

Marian Cristian Mihăescu
Data Analytics in E Learning Approaches and Applications Book PDF

Author : Marian Cristian Mihăescu
Publisher : Unknown
Page : 0 pages
File Size : 45,5 Mb
Release : 2022
Category : Electronic
ISBN : 3030966453

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Data Analytics in E-Learning: Approaches and Applications by Marian Cristian Mihăescu Pdf

This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.

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 : 52,8 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

Computational Learning Approaches to Data Analytics in Biomedical Applications

Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch
Computational Learning Approaches to Data Analytics in Biomedical Applications Book PDF

Author : Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch
Publisher : Academic Press
Page : 312 pages
File Size : 40,8 Mb
Release : 2019-11-20
Category : Technology & Engineering
ISBN : 9780128144831

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Computational Learning Approaches to Data Analytics in Biomedical Applications by Khalid Al-Jabery,Tayo Obafemi-Ajayi,Gayla Olbricht,Donald Wunsch Pdf

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor

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 : 42,9 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 : 47,9 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.

Learning Analytics: Fundaments, Applications, and Trends

Alejandro Peña-Ayala
Learning Analytics Fundaments Applications and Trends Book PDF

Author : Alejandro Peña-Ayala
Publisher : Springer
Page : 303 pages
File Size : 44,7 Mb
Release : 2017-02-17
Category : Technology & Engineering
ISBN : 9783319529776

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Learning Analytics: Fundaments, Applications, and Trends by Alejandro Peña-Ayala Pdf

This book provides a conceptual and empirical perspective on learning analytics, its goal being to disseminate the core concepts, research, and outcomes of this emergent field. Divided into nine chapters, it offers reviews oriented on selected topics, recent advances, and innovative applications. It presents the broad learning analytics landscape and in-depth studies on higher education, adaptive assessment, teaching and learning. In addition, it discusses valuable approaches to coping with personalization and huge data, as well as conceptual topics and specialized applications that have shaped the current state of the art. By identifying fundamentals, highlighting applications, and pointing out current trends, the book offers an essential overview of learning analytics to enhance learning achievement in diverse educational settings. As such, it represents a valuable resource for researchers, practitioners, and students interested in updating their knowledge and finding inspirations for their future work.

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches Book PDF

Author : K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
Publisher : CRC Press
Page : 255 pages
File Size : 54,9 Mb
Release : 2020-10-08
Category : Computers
ISBN : 9781000179538

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Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches by K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh Pdf

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Fundamentals of Machine Learning for Predictive Data Analytics second edition Book PDF

Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher : MIT Press
Page : 853 pages
File Size : 46,7 Mb
Release : 2020-10-20
Category : Computers
ISBN : 9780262361101

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Fundamentals of Machine Learning for Predictive Data Analytics, second edition by John D. Kelleher,Brian Mac Namee,Aoife D'Arcy Pdf

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.

Learning Analytics Goes to School

Andrew Krumm,Barbara Means,Marie Bienkowski
Learning Analytics Goes to School Book PDF

Author : Andrew Krumm,Barbara Means,Marie Bienkowski
Publisher : Routledge
Page : 275 pages
File Size : 45,7 Mb
Release : 2018-01-12
Category : Education
ISBN : 9781317307860

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Learning Analytics Goes to School by Andrew Krumm,Barbara Means,Marie Bienkowski Pdf

Learning Analytics Goes to School presents a framework for engaging in education research and improving education practice through the use of newly available data sources and analytical approaches. The application of data-intensive research techniques to understanding and improving learning environments has been growing at a rapid pace. In this book, three leading researchers convey lessons from their own experiences—and the current state of the art in educational data mining and learning analytics more generally—by providing an explicit set of tools and processes for engaging in collaborative data-intensive improvement.

Emerging Trends in Learning Analytics

Anonim
Emerging Trends in Learning Analytics Book PDF

Author : Anonim
Publisher : BRILL
Page : 297 pages
File Size : 55,6 Mb
Release : 2019-05-07
Category : Education
ISBN : 9789004399273

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Emerging Trends in Learning Analytics by Anonim Pdf

This book documents recent attempts to conduct systematic, prodigious and multidisciplinary research in learning analytics and present their findings and identify areas for further research and development. The book also unveils the distinguished and exemplary works by educators and researchers in the field highlighting the current trends, privacy and ethical issues, creative and unique approaches, innovative methods, frameworks, and theoretical and practical aspects of learning analytics.

Machine Learning Paradigms

Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain
Machine Learning Paradigms Book PDF

Author : Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain
Publisher : Springer
Page : 223 pages
File Size : 54,5 Mb
Release : 2019-03-16
Category : Technology & Engineering
ISBN : 9783030137434

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Machine Learning Paradigms by Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain Pdf

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

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 : 53,6 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.

Learning Analytics in Education

David Niemi,Roy D. Pea,Bror Saxberg,Richard E. Clark
Learning Analytics in Education Book PDF

Author : David Niemi,Roy D. Pea,Bror Saxberg,Richard E. Clark
Publisher : IAP
Page : 128 pages
File Size : 48,7 Mb
Release : 2018-08-01
Category : Education
ISBN : 9781641133715

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Learning Analytics in Education by David Niemi,Roy D. Pea,Bror Saxberg,Richard E. Clark Pdf

This book provides a comprehensive introduction by an extraordinary range of experts to the recent and rapidly developing field of learning analytics. Some of the finest current thinkers about ways to interpret and benefit from the increasing amount of evidence from learners’ experiences have taken time to explain their methods, describe examples, and point out new underpinnings for the field. Together, they show how this new field has the potential to dramatically increase learner success through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings. Learning analytics is much more than “analyzing learning data”—it is about deeply understanding what learning activities work well, for whom, and when. Learning Analytics in Education provides an essential framework, as well as guidance and examples, for a wide range of professionals interested in the future of learning. If you are already involved in learning analytics, or otherwise trying to use an increasing density of evidence to understand learners’ progress, these leading thinkers in the field may give you new insights. If you are engaged in teaching at any level, or training future teachers/faculty for this new, increasingly technology-enhanced learning world, and want some sense of the potential opportunities (and pitfalls) of what technology can bring to your teaching and students, these forward-thinking leaders can spark your imagination. If you are involved in research around uses of technology, improving learning measurements, better ways to use evidence to improve learning, or in more deeply understanding human learning itself, you will find additional ideas and insights from some of the best thinkers in the field here. If you are involved in making administrative or policy decisions about learning, you will find new ideas (and dilemmas) coming your way from inevitable changes in how we design and deliver instruction, how we measure the outcomes, and how we provide feedback to students, teachers, developers, administrators, and policy-makers. For all these players, the trick will be to get the most out of all the new developments to efficiently and effectively improve learning performance, without getting distracted by “shiny” technologies that are disconnected from how human learning and development actually work.

Challenges and Applications of Data Analytics in Social Perspectives

Sathiyamoorthi, V.,Elci, Atilla
Challenges and Applications of Data Analytics in Social Perspectives Book PDF

Author : Sathiyamoorthi, V.,Elci, Atilla
Publisher : IGI Global
Page : 324 pages
File Size : 46,5 Mb
Release : 2020-12-04
Category : Computers
ISBN : 9781799825685

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Challenges and Applications of Data Analytics in Social Perspectives by Sathiyamoorthi, V.,Elci, Atilla Pdf

With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.