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Learning Analytics: Fundaments, Applications, and Trends

Learning Analytics: Fundaments, Applications, and Trends
Author: Alejandro Peña-Ayala
Publisher: Springer
Total Pages: 303
Release: 2017-02-17
Genre: Technology & Engineering
ISBN: 3319529773
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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.

A Beginner’s Guide to Learning Analytics

A Beginner’s Guide to Learning Analytics
Author: Srinivasa K G
Publisher: Springer Nature
Total Pages: 326
Release: 2021-04-19
Genre: Education
ISBN: 3030702588
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This book A Beginner’s Guide to Learning Analytics is designed to meet modern educational trends’ needs. It is addressed to readers who have no prior knowledge of learning analytics and functions as an introductory text to learning analytics for those who want to do more with evaluation/assessment in their organizations. The book is useful to all who need to evaluate their learning and teaching strategies. It aims to bring greater efficiency and deeper engagement to individual students, learning communities, and educators. Covered here are the key concepts linked to learning analytics for researchers and practitioners interested in learning analytics. This book helps those who want to apply analytics to learning and development programs and helps educational institutions to identify learners who require support and provide a more personalized learning experience. Like chapters show diverse uses of learning analytics to enhance student and faculty performance. It presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different educational domains. This book provides educators and researchers with the tools and frameworks to effectively make sense of and use data and analytics in their everyday practice. This book will be a valuable addition to researchers’ bookshelves.

Emerging Trends in Learning Analytics

Emerging Trends in Learning Analytics
Author: Myint Swe Khine
Publisher: Brill
Total Pages: 0
Release: 2019
Genre: Education
ISBN: 9789004399266
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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.

Data Mining and Learning Analytics

Data Mining and Learning Analytics
Author: Samira ElAtia
Publisher: John Wiley & Sons
Total Pages: 320
Release: 2016-09-20
Genre: Computers
ISBN: 1118998219
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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.

Educational Data Mining

Educational Data Mining
Author: Alejandro Peña-Ayala
Publisher: Springer
Total Pages: 477
Release: 2013-11-08
Genre: Technology & Engineering
ISBN: 3319027387
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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.

Emerging Trends in Learning Analytics

Emerging Trends in Learning Analytics
Author:
Publisher: BRILL
Total Pages: 297
Release: 2019-05-07
Genre: Education
ISBN: 9004399275
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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.

Data Mining and Learning Analytics

Data Mining and Learning Analytics
Author: Samira ElAtia
Publisher: John Wiley & Sons
Total Pages: 320
Release: 2016-09-26
Genre: Computers
ISBN: 1118998235
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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.

Applications of Big Data Analytics

Applications of Big Data Analytics
Author: Mohammed M. Alani
Publisher: Springer
Total Pages: 214
Release: 2018-07-23
Genre: Computers
ISBN: 3319764721
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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 and Knowledge Analytics in Open Education

Learning and Knowledge Analytics in Open Education
Author: Feng-Qi Lai
Publisher: Springer
Total Pages: 230
Release: 2016-07-26
Genre: Education
ISBN: 3319389564
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This book explores trends in learning and knowledge analytics in open education, as explored in proceedings papers from AECT-LKAOE 2015 International Research Symposium. The chapters investigate various issues surrounding open education in all disciplines, such as learning design in open-ended learning environments, MOOCs (Massive Open Online Courses), learning analytics studies and applications, and technology and new media. The chapter authors provide guidance for how to design and develop most effective, efficient, and appealing instruction as well as suggesting learning strategies relevant to the open education era.

Emergent Practices of Learning Analytics in K-12 Classrooms

Emergent Practices of Learning Analytics in K-12 Classrooms
Author: Kavakl? Uluta?, Nurdan
Publisher: IGI Global
Total Pages: 290
Release: 2023-12-29
Genre: Education
ISBN:
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In the world of education, technology has revolutionized teaching and learning practices globally. However, the widespread adoption of technology, especially in response to Covid-19, has led to a deluge of data from students' digital footprints. The challenge lies in effectively using this data through learning analytics (LA) to optimize K-12 classroom practices. Emergent Practices of Learning Analytics in K-12 Classrooms, edited by Dr. Nurdan Kavakl? Uluta? and Dr. Devrim Höl offers a comprehensive solution to these challenges. This book gathers academic scholars to explore practical perspectives on applying learning analytics in K-12 classrooms, combining qualitative and quantitative methodologies. Addressing topics such as predictive analytics, ethical considerations, and future directions, it empowers educators to make data-driven decisions, creating engaging learning experiences for improved student outcomes. By embracing the insights and recommendations presented in this book, academic scholars can confidently navigate the realm of learning analytics and shape the future of K-12 education.