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Online Learning Analytics [Kõva köide]

Edited by (Harrisburg University of Science and Technology, Pennsylvania, USA)
  • Formaat: Hardback, 232 pages, kõrgus x laius: 234x156 mm, kaal: 639 g
  • Sari: Data Analytics Applications
  • Ilmumisaeg: 14-Dec-2021
  • Kirjastus: Auerbach
  • ISBN-10: 1032200979
  • ISBN-13: 9781032200972
Teised raamatud teemal:
Online Learning AnalyticsSuurem pilt
  • Formaat: Hardback, 232 pages, kõrgus x laius: 234x156 mm, kaal: 639 g
  • Sari: Data Analytics Applications
  • Ilmumisaeg: 14-Dec-2021
  • Kirjastus: Auerbach
  • ISBN-10: 1032200979
  • ISBN-13: 9781032200972
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032200972.html
Märksõnad:
"In our increasingly digitally enabled education world, analytics used ethically, strategically, and with care holds the potential to help more and more diverse students be more successful on higher education journeys than ever before. Jay Liebowitz and a cadre of the fields best good trouble makers in this space help shine a light on the possibilities, potential challenges, and the power of learning together in this work."

Mark David Milliron, Ph.D., Senior Vice President and Executive Dean of the Teachers College, Western Governors University

Due to the COVID-19 pandemic and its aftereffects, we have begun to enter the "new normal" of education. Instead of online learning being an "added feature" of K12 schools and universities worldwide, it will be incorporated as an essential feature in education. There are many questions and concerns from parents, students, teachers, professors, administrators, staff, accrediting bodies, and others regarding the quality of virtual learning and its impact on student learning outcomes.

Online Learning Analytics is conceived on trying to answer the questions of those who may be skeptical about online learning. Through better understanding and applying learning analytics, we can assess how successful learning and student/faculty engagement, as examples, can contribute towards producing the educational outcomes needed to advance student learning for future generations. Learning analytics has proven to be successful in many areas, such as the impact of using learning analytics in asynchronous online discussions in higher education. To prepare for a future where online learning plays a major role, this book examines:













Data insights for improving curriculum design, teaching practice, and learning Scaling up learning analytics in an evidence-informed way The role of trust in online learning.









Online learning faces very real philosophical and operational challenges. This book addresses areas of concern about the future of education and learning. It also energizes the field of learning analytics by presenting research on a range of topics that is broad and recognizes the humanness and depth of educating and learning.
Dedication vii
List of Figures xv
List of Tables xvii
Foreword xix
Pete Smith
Preface xxv
Contributing Authors xxvii
About the Editor xxxvii
Chapter 1 Leveraging Learning Analytics for Assessment and Feedback 1(18)
Dirk Ifenthaler
Samuel Greiff
Abstract
1(1)
Introduction
2(1)
Current State of Educational Assessment
3(2)
Harnessing Data and Analytics for Assessment
5(3)
Benefits of Analytics-Enhanced Assessment
8(1)
Analytics-Enhanced Assessment Framework
9(2)
Conclusion
11(1)
References
12(7)
Chapter 2 Desperately Seeking the Impact of Learning Analytics in Education at Scale: Marrying Data Analysis with Teaching and Learning 19(14)
Olga Viberg
Ake Gronlund
Abstract
19(1)
Introduction
20(1)
Critical Aspects of LA in a Human-Centered Perspective
21(6)
Focus on Teachers' Needs and Goals
22(2)
Teachers' Data Literacy Skills
24(1)
Data
25(2)
Conclusions
27(1)
References
28(5)
Chapter 3 Designing for Insights: An Evidenced-Centered Approach to Learning Analytics 33(24)
Kara N. McWilliams
Kristen Herrick
K. Becca Runyon
Andreas Oranje
Abstract
33(1)
Introduction
34(1)
Current Issues in Learning Analytics
35(3)
Learning Theory and Learning Analytics
35(1)
Availability and Validity of Learner Data
36(1)
Contextual Gaps in Data Footprints
37(1)
Ethical Considerations
37(1)
Conclusion
38(1)
An Evidenced-Centered Design Approach to Yielding Valid and Reliable Learning Analytics
38(13)
ELAborate
40(1)
User-Centered Design in Discovery
40(1)
Learning Outcomes, Theory of Action, Theory of Change, and a Learning Model
41(2)
Learner Data Footprint
43(4)
Construct Validity and Meaningful Insights
47(3)
Ethics-Informed Learning Analytics
50(1)
Conclusion
51(1)
References
52(5)
Chapter 4 Implementing Learning Analytics at Scale in an Online World: Lessons Learned from the Open University UK 57(22)
Bart Rienties
Abstract
57(1)
Introduction
58(1)
Making Use of Learning Analytics Data
59(1)
The Rise of the Learning Analytics Community
60(11)
Case Study 1: The Analytics4Action Project
61(4)
Case Study 2: Learning Design to Understand Learning Analytics
65(6)
Discussion
71(2)
References
73(6)
Chapter 5 Realising the Potential of Learning Analytics: Reflections from a Pandemic 79(16)
Mohammad Khalil
Paul Prinsloo
Sharon Slade
Abstract
79(1)
Introduction
80(1)
Some Notes on the Nature of Conceptual Exploration
81(1)
Glimpses of Learning Analytics During the Pandemic
82(2)
Implications and (Un)Realised Potential of Learning Analytics
84(1)
Conceptual Operations
85(5)
Conclusions
90(1)
References
91(4)
Chapter 6 Using Learning Analytics and Instructional Design to Inform, Find, and Scale Quality Online Learning 95(20)
John Fritz
Mariann Hawken
Sarah Shin
Abstract
95(1)
Introduction
96(1)
Selected Research and Practice About Online Learning Quality
97(2)
Learning Analytics in Higher Ed and at UMBC
99(2)
UMBC's Pandemic PIVOT
101(1)
Theory and Practice
102(1)
Adoption
103(1)
Impact
103(4)
Faculty
104(1)
Students
105(2)
Lessons Learned
107(3)
Conclusion
110(1)
References
111(4)
Chapter 7 Democratizing Data at a Large R1 Institution: Supporting Data-Informed Decision Making for Advisers, Faculty, and Instructional Designers 115(30)
Chris Millet
Jessica Resig
Bart Pursel
Abstract
115(1)
Introduction
116(1)
Dimensions of Learning Analytics
116(3)
Learning Analytics Project Dimensions
117(2)
Organizational Considerations: Creating Conditions for Success
119(6)
Security, Privacy, and Ethics
120(5)
Advancing Analytics Initiatives at Your Institution
125(2)
Iterating Toward Success
125(1)
Consortium, Research Partnerships, and Standards
126(1)
Penn State Projects
127(14)
Penn State Projects: Analytical Design Model
128(1)
Penn State Projects: Elevate
129(7)
Penn State Projects: Spectrum
136(5)
Conclusion
141(1)
References
142(3)
Chapter 8 The Benefits of the 'New Normal': Data Insights for Improving Curriculum Design, Teaching Practice, and Learning 145(20)
Deborah West
Pablo Munguia
Abstract
145(1)
Introduction
146(4)
Testing the Benefits of the New Normal
150(3)
Variables and Proxies
153(2)
Digging Deeper: How to Separate Curriculum, Assessment, and Teacher Effects on Learning
155(2)
Conclusion
157(1)
References
157(8)
Chapter 9 Learning Information, Knowledge, and Data Analysis in Israel: A Case Study 165(12)
Moria Levy
Ronit Nehemia
Abstract
165(1)
Introduction: The 21st-Century Skills
166(1)
Developing the Digital Information Discovery and Detection Programs
167(1)
Upgrading the Program: Data and Information
168(5)
COVID-19
173(1)
Current Situation
174(1)
Summary
174(1)
References
174(3)
Chapter 10 Scaling Up Learning Analytics in an Evidence-Informed Way 177(20)
Justian Knobbout
Esther van der Stappen
Abstract
177(1)
Introduction
178(1)
A Capability Model for Learning Analytics
179(6)
Capabilities for Learning Analytics
179(5)
Design Process
184(1)
Using the Learning Analytics Capability Model in Practice
185(5)
Evaluation of the Learning Analytics Capability Model
186(1)
Phases of Learning Analytics Implementation
186(4)
Measuring Impact on Learning
190(3)
Conclusion and Recommendations
193(1)
References
194(3)
Chapter 11 The Role of Trust in Online Learning 197(16)
Joanna Paliszkiewicz
Edyta Skarzyriska
Abstract
197(1)
Introduction
198(1)
Trust and Online Learning-Literature Review
198(3)
Research Method
201(1)
Characteristics of the Research Sample
201(1)
The Instrument and Data Analysis
201(1)
Research Results
201(7)
Demographic Characteristics of Respondents
201(1)
Technological Availability and Software Used
202(1)
Benefits of Learning Online
202(1)
Bottlenecks in Online Learning
203(1)
Factors Affecting Online Learning
204(4)
Discussion
208(1)
Conclusion
209(1)
References
210(3)
Chapter 12 Face Detection with Applications in Education 213(16)
Juan Carlos Bonilla-Robles
Jose Alberto Hernandez Aguilar
Guillermo Santamaria-Bonfil
Abstract
213(1)
Introduction
214(2)
Problem Statement
215(1)
Literature Review
215(1)
Face Detection Techniques
216(1)
Geometric Approach
216(1)
Machine Learning Approach
217(1)
Methodology
217(4)
Image
218(1)
Preprocessing
219(1)
Integral Image
220(1)
Removing Haar Features
221(1)
Experimentation
221(5)
Creating the Haar Cascading Classifier
221(2)
Tuning Parameters
223(1)
Experimentation Results
223(2)
Results Metrics
225(1)
Results Comparison Table
225(1)
Conclusions and Future Work
226(1)
Acknowledgments
226(1)
References
227(2)
Index 229
Dr. Jay Liebowitz is a Visiting Professor in the Stillman School of Business and the MS-Business Analytics Capstone & Co-Program Director (External Relations) at Seton Hall University. He previously served as the Distinguished Chair of Applied Business and Finance at Harrisburg University of Science and Technology. Before HU, he was the Orkand Endowed Chair of Management and Technology in the Graduate School at the University of Maryland University College (UMUC). He served as a Professor in the Carey Business School at Johns Hopkins University. He is also the Series Book Editor of the Data Analytics Applications book series (Taylor & Francis), as well as the Series Book Editor of the new Digital Transformation: Accelerating Organizational Intelligence book series (World Scientific Publishing).