Muutke küpsiste eelistusi

E-raamat: Learning Analytics Goes to School: A Collaborative Approach to Improving Education

  • Formaat: 190 pages
  • Ilmumisaeg: 12-Jan-2018
  • Kirjastus: Routledge
  • Keel: eng
  • ISBN-13: 9781317307877
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 46,79 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: 190 pages
  • Ilmumisaeg: 12-Jan-2018
  • Kirjastus: Routledge
  • Keel: eng
  • ISBN-13: 9781317307877
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Learning Analytics for Educational Improvement presents a framework for understanding how to conduct new forms of education research and enact new approaches to improving education practice made possible by big data. Although application of big data techniques to learning and education is quite new, learning analytics and educational data mining have been growing rapidly, fueled by the visible successes of applications of data analytics in the commercial and political realms. This book serves as a one-stop reference for a variety of learning analytics tools and techniques and makes those methods meaningful by describing their application in a wide range of real-world education contexts.

Arvustused

"Learning Analytics Goes to School provides a clear and practical overview of how to harness excitement over big data and learning analytics in education for educational improvement at scale. The approach outlined by the authors provides concrete guidance for how research-practice partnerships can use large data sets, new analytic techniques, and methods of improvement science to design and test solutions to problems of practice. It is a must read and great reference book for those new to educational data science, as well as those seeking to embrace a more collaborative approach to education research."

William R. Penuel, Professor of Learning Sciences and Human Development, University of Colorado, USA

"Learning Analytics Goes to School is for anyone interested in understanding the growing use of data pertaining to students and their digitally-mediated learning activities. This book provides a thorough and thoughtful discussion of the primary issues related to educational data, and a step-by-step guide to addressing these issues by implementing a process called Collaborative Data-intensive Improvement (CDI). The authors demystify jargon, lay out the basic concepts of data science for education, and provide a roadmap for creating research-practice partnerships aimed at producing reliably positive outcomes for all students. Written in a style that is both professional and accessible, this will be a valuable resource for teachers and administrators as well as researchers."

Stephanie D. Teasley, Research Professor in the School of Information at the University of Michigan, and President of the Society for Leaning Analytics Research (SoLAR), USA

List of figures
vi
List of tables
vii
List of boxes
viii
Preface ix
Acknowledgements xii
1 Introduction
1(15)
2 Data Used in Educational Data-Intensive Research
16(22)
3 Methods Used in Educational Data-Intensive Research
38(24)
4 Legal and Ethical Issues in Using Educational Data
62(18)
5 Foundations of Collaborative Applications of Educational Data Mining and Learning Analytics
80(28)
6 Supporting Conditions for Collaborative Data-Intensive Improvement
108(27)
7 Five Phases of Collaborative Data-Intensive Improvement
135(21)
8 Lessons Learned and Prospects for the Future
156(13)
Glossary 169(6)
Index 175
Dr. Andrew Krumm is Director of Learning Analytics Research at Digital Promise, a nonprofit organization that brings together the expertise of educators, researchers, and technology developers in the interest of improving teaching and learning. Dr. Krumm has launched multiple research-practice partnerships and his research addresses the use of data-intensive research techniques to improve learning environments.

Dr. Barbara Means is Executive Director for Learning Sciences Research at Digital Promise. Formerly the founder and director of the Center for Technology in Learning at SRI International, Dr. Means is a nationally recognized expert in defining issues and approaches for evaluating the implementation and efficacy of technology-supported educational innovations.

Dr. Marie Bienkowski is Director of the Center for Technology in Learning at SRI International, a nonprofit research and development organization based in Silicon Valley that takes innovative ideas and technologies from the laboratory to the end-user and marketplace. Dr. Bienkowski is a computer scientist and education researcher leading efforts to improve student learning, effective teaching, and meaningful assessment.