-PDF Download- Data Analytics And Adaptive Learning EBOOK

Data Analytics and Adaptive Learning

Author: Patsy D. Moskal

Publisher: Taylor & Francis

Published: 2023-08-25

Total Pages: 364

ISBN-13: 100092923X

Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K–12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.

Advancing the Power of Learning Analytics and Big Data in Education

Author: Azevedo, Ana

Publisher: IGI Global

Published: 2021-03-19

Total Pages: 296

ISBN-13: 1799871045

DOWNLOAD EBOOK →

The term learning analytics is used in the context of the use of analytics in e-learning environments. Learning analytics is used to improve quality. It uses data about students and their activities to provide better understanding and to improve student learning. The use of learning management systems, where the activity of the students can be easily accessed, potentiated the use of learning analytics to understand their route during the learning process, help students be aware of their progress, and detect situations where students can give up the course before its completion, which is a growing problem in e-learning environments. Advancing the Power of Learning Analytics and Big Data in Education provides insights concerning the use of learning analytics, the role and impact of analytics on education, and how learning analytics are designed, employed, and assessed. The chapters will discuss factors affecting learning analytics such as human factors, geographical factors, technological factors, and ethical and legal factors. This book is ideal for teachers, administrators, teacher educators, practitioners, stakeholders, researchers, academicians, and students interested in the use of big data and learning analytics for improved student success and educational environments.

Learning Analytics Explained

Author: Niall Sclater

Publisher: Taylor & Francis

Published: 2017-02-17

Total Pages: 278

ISBN-13: 1317394569

DOWNLOAD EBOOK →

Learning Analytics Explained draws extensively from case studies and interviews with experts in order to discuss emerging applications of the new field of learning analytics. Educational institutions increasingly collect data on students and their learning experiences, a practice that helps enhance courses, identify learners who require support, and provide a more personalized learning experience. There is, however, a corresponding need for guidance on how to carry out institutional projects, intervene effectively with students, and assess legal and ethical issues. This book provides that guidance while also covering the evolving technical architectures, standards, and products within the field.

Data for students

Author: Katherine Brittany Leu

Publisher: RTI Press

Published: 2020-03-26

Total Pages: 6

ISBN-13:

DOWNLOAD EBOOK →

Data analytics and data science can address challenges to student success. Postsecondary institutions have already creatively used analytics to address the problem of college completion through innovations such as academic early warning systems and adaptive learning technologies. A diverse array of data on postsecondary education exists both within and outside of institutions and can be used in analytics to provide a richer view of student success and improve equity. Increased data collection and analysis open up the challenges of data linkage across units and the risk of ethical and privacy violations, which deserve more attention.

Learning Analytics

Author: Johann Ari Larusson

Publisher: Springer

Published: 2014-07-04

Total Pages: 203

ISBN-13: 1461433053

DOWNLOAD EBOOK →

In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics. Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world. Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to: Enhance student and faculty performance. Improve student understanding of course material. Assess and attend to the needs of struggling learners. Improve accuracy in grading. Allow instructors to assess and develop their own strengths. Encourage more efficient use of resources at the institutional level. Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success.

Data Analytics for Beginners:

Author: Florino Alfeche

Publisher:

Published: 2017-10-08

Total Pages: 82

ISBN-13: 9781549946417

DOWNLOAD EBOOK →

Data Analytics For Beginners: The Ultimate Guide To Learn and Master Data Analysis, How to Integrate Analytics into Your Business.Working with big data using data mining and analytics is rapidly becoming common in the commercial sector. Tools and techniques once confined to research laboratories are being adopted by forward-looking industries, most notably those serving end users through online systems. Higher education institutions are applying learning analytics to improve the services they provide and to improve visible and measurable targets such as grades and retention. K-12 schools and school districts are starting to adopt such institution-level analyses for detecting areas for improvement, setting policies, and measuring results. Now, with advances in adaptive learning systems, possibilities exist to harness the power of feedback loops at the level of individual teachers and students. Measuring and making visible students' learning and assessment activities open up the possibility for students to develop skills in monitoring their own learning and to see directly how their effort improves their success. Teachers gain views into students' performance that help them adapt their teaching or initiate interventions in the form of tutoring, tailored assignments, and the like. Adaptive learning systems enable educators to quickly see the effectiveness of their adaptations and interventions, providing feedback for continuous improvement. Researchers and developers can more rapidly compare versions A and B of designs, products, and approaches to teaching and learning, enabling the state of the art and the state of the practice to keep pace with the rapid pace of adoption of online and blended learning environments. Open source tools for adaptive learning systems, commercial offerings, and increased understanding of what data reveal are leading to fundamental shifts in teaching and learning systems. As content moves online and mobile devices for interacting with content enable teaching to be always on, educational data mining and learning analytics will enable learning to be always assessed. Educators at all levels will benefit from understanding the possibilities of the developments described in the use of big data herein.

The Master Adaptive Learner

Author: William Cutrer

Publisher: Elsevier Health Sciences

Published: 2019-09-29

Total Pages: 242

ISBN-13: 032371112X

DOWNLOAD EBOOK →

Tomorrow’s best physicians will be those who continually learn, adjust, and innovate as new information and best practices evolve, reflecting adaptive expertise in response to practice challenges. As the first volume in the American Medical Association’s MedEd Innovation Series, The Master Adaptive Learner is an instructor-focused guide covering models for how to train and teach future clinicians who need to develop these adaptive skills and utilize them throughout their careers. Explains and clarifies the concept of a Master Adaptive Learner: a metacognitive approach to learning based on self-regulation that fosters the success and use of adaptive expertise in practice. Contains both theoretical and practical material for instructors and administrators, including guidance on how to implement a Master Adaptive Learner approach in today’s institutions. Gives instructors the tools needed to empower students to become efficient and successful adaptive learners. Helps medical faculty and instructors address gaps in physician training and prepare new doctors to practice effectively in 21st century healthcare systems. One of the American Medical Association Change MedEd initiatives and innovations, written and edited by members of the ACE (Accelerating Change in Medical Education) Consortium – a unique, innovative collaborative that allows for the sharing and dissemination of groundbreaking ideas and projects.

Big Data in Education

Author: Ben Williamson

Publisher: SAGE

Published: 2017-07-24

Total Pages: 290

ISBN-13: 1526416328

DOWNLOAD EBOOK →

Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include: · The role of learning analytics and educational data science in schools · A critical appreciation of code, algorithms and infrastructures · The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments · Important digital research methods issues for researchers This is essential reading for anyone studying or working in today′s education environment!

Machine Learning for Data Streams

Author: Albert Bifet

Publisher: MIT Press

Published: 2023-05-09

Total Pages: 289

ISBN-13: 026254783X

DOWNLOAD EBOOK →

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Radical Solutions and Learning Analytics

Author: Daniel Burgos

Publisher: Springer Nature

Published: 2020-05-08

Total Pages: 233

ISBN-13: 981154526X

DOWNLOAD EBOOK →

Learning Analytics become the key for Personalised Learning and Teaching thanks to the storage, categorisation and smart retrieval of Big Data. Thousands of user data can be tracked online via Learning Management Systems, instant messaging channels, social networks and other ways of communication. Always with the explicit authorisation from the end user, being a student, a teacher, a manager or a persona in a different role, an instructional designer can design a way to produce a practical dashboard that helps him improve that very user’s performance, interaction, motivation or just grading. This book provides a thorough approach on how education, as such, from teaching to learning through management, is improved by a smart analysis of available data, making visible and useful behaviours, predictions and patterns that are hinder to the regular eye without the process of massive data.