What Big Data Can Tell Us About the Psychology of Learning and Teaching
Author: Ronnel B. King
Publisher: Frontiers Media SA
Published: 2022-03-09
Total Pages: 170
ISBN-13: 2889746321
DOWNLOAD EBOOK →Lanterna Rally
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Big Data in Education
Author: Ben Williamson
Publisher: SAGE
Published: 2017-07-24
Total Pages: 257
ISBN-13: 1526416344
DOWNLOAD EBOOK →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.
Big Data in Psychological Research
Author: Sang Eun Woo
Publisher: American Psychological Association (APA)
Published: 2020
Total Pages: 0
ISBN-13: 9781433831676
DOWNLOAD EBOOK →Big Data in Psychological Research provides an overview of big data theory, research design and analysis, collection methods, applications, ethical concerns, best practices, and future research directions for psychologists.
Learning Technology for Education in Cloud - MOOC and Big Data
Author: Lorna Uden
Publisher: Springer
Published: 2014-07-29
Total Pages: 203
ISBN-13: 3319106716
DOWNLOAD EBOOK →This book constitutes the refereed proceedings of the Third International Workshop on Learning Technology for Education in Cloud, LTEC 2014, held in Santiago, Chile, in September 2014. The 20 revised full papers presented were carefully reviewed and selected from 31 submissions. The papers are organized in topical sections on MOOC for learning; learning technologies; learning in higher education; case study in learning.
Contemporary Technologies in Education
Author: Olusola O. Adesope
Publisher: Springer
Published: 2018-11-08
Total Pages: 268
ISBN-13: 3319896806
DOWNLOAD EBOOK →This edited volume provides a critical discussion of theoretical, methodological, and practical developments of contemporary forms of educational technologies. Specifically, the book discusses the use of contemporary technologies such as the Flipped Classroom (FC), Massive Open Online Course (MOOC), Social Media, Serious Educational Games (SEG), Wikis, innovative learning software tools, and learning analytic approach for making sense of big data. While some of these contemporary educational technologies have been touted as panaceas, researchers and developers have been faced with enormous challenges in enhancing the use of these technologies to arouse student attention and improve persistent motivation, engagement, and learning. Hence, the book examines how contemporary technologies can engender student motivation and result in improved engagement and learning. Each chapter also discusses the road ahead and where appropriate, uses the current trend to predict future affordances of technologies.
Adoption of Data Analytics in Higher Education Learning and Teaching
Author: Dirk Ifenthaler
Publisher: Springer Nature
Published: 2020-08-10
Total Pages: 464
ISBN-13: 3030473929
DOWNLOAD EBOOK →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.
Data Mining and Learning Analytics
Author: Samira ElAtia
Publisher: John Wiley & Sons
Published: 2016-09-20
Total Pages: 320
ISBN-13: 1118998219
DOWNLOAD EBOOK →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.
Application of Big Data, Blockchain, and Internet of Things for Education Informatization
Author: Mian Ahmad Jan
Publisher: Springer Nature
Published: 2023-01-11
Total Pages: 682
ISBN-13: 3031239474
DOWNLOAD EBOOK →The three-volume set LNICST 465, 466 and 467 constitutes the proceedings of the Second EAI International Conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022, held as virtual event, in July 29–31, 2022. The 204 papers presented in the proceedings were carefully reviewed and selected from 550 submissions. BigIoT-EDU aims to provide international cooperation and exchange platform for big data and information education experts, scholars and enterprise developers to share research results, discuss existing problems and challenges, and explore cutting-edge science and technology. The conference focuses on research fields such as “Big Data” and “Information Education. The use of Artificial Intelligence (AI), Blockchain and network security lies at the heart of this conference as we focused on these emerging technologies to excel the progress of Big Data and information education.
Applications of Big Data Analytics
Author: Mohammed M. Alani
Publisher: Springer
Published: 2018-07-23
Total Pages: 214
ISBN-13: 3319764721
DOWNLOAD EBOOK →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.
How People Learn
Author: National Research Council
Publisher: National Academies Press
Published: 2000-08-11
Total Pages: 384
ISBN-13: 0309131979
DOWNLOAD EBOOK →First released in the Spring of 1999, How People Learn has been expanded to show how the theories and insights from the original book can translate into actions and practice, now making a real connection between classroom activities and learning behavior. This edition includes far-reaching suggestions for research that could increase the impact that classroom teaching has on actual learning. Like the original edition, this book offers exciting new research about the mind and the brain that provides answers to a number of compelling questions. When do infants begin to learn? How do experts learn and how is this different from non-experts? What can teachers and schools do-with curricula, classroom settings, and teaching methods--to help children learn most effectively? New evidence from many branches of science has significantly added to our understanding of what it means to know, from the neural processes that occur during learning to the influence of culture on what people see and absorb. How People Learn examines these findings and their implications for what we teach, how we teach it, and how we assess what our children learn. The book uses exemplary teaching to illustrate how approaches based on what we now know result in in-depth learning. This new knowledge calls into question concepts and practices firmly entrenched in our current education system. Topics include: How learning actually changes the physical structure of the brain. How existing knowledge affects what people notice and how they learn. What the thought processes of experts tell us about how to teach. The amazing learning potential of infants. The relationship of classroom learning and everyday settings of community and workplace. Learning needs and opportunities for teachers. A realistic look at the role of technology in education.
