Big Data in Education: Pedagogy and Research (Policy Implications of Research in Education, 13)

✍ Scribed by Theodosia Prodromou (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 249
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book discusses how Big Data could be implemented in educational settings and research, using empirical data and suggesting both best practices and areas in which to invest future research and development. It also explores: 1) the use of learning analytics to improve learning and teaching; 2) the opportunities and challenges of learning analytics in education.

As Big Data becomes a common part of the fabric of our world, education and research are challenged to use this data to improve educational and research systems, and also are tasked with teaching coming generations to deal with Big Data both effectively and ethically.

The Big Data era is changing the data landscape for statistical analysis, the ways in which data is captured and presented, and the necessary level of statistical literacy to analyse and interpret data for future decision making. The advent of Big Data accentuates the need to enable citizens to develop statistical skills, thinking and reasoning needed for representing, integrating and exploring complex information.

This book offers guidance to researchers who are seeking suitable topics to explore. It presents research into the skills needed by data practitioners (data analysts, data managers, statisticians, and data consumers, academics), and provides insights into the statistical skills, thinking and reasoning needed by educators and researchers in the future to work with Big Data. This book serves as a concise reference for policymakers, who must make critical decisions regarding funding and applications.

✦ Table of Contents

Introduction
Description and Purpose of Work
Primary Audience of the Book
The Organization of the Book
Theoretical and Ethical Concerns
Teaching People to Use Big Data Effectively
Using Big Data to Improve Teaching
Educational Systems that Use Big Data
Policy/Curriculum Development
Contents
Part I: Theoretical and Ethical Concerns
Chapter 1: Big Data in Academic Research: Challenges, Pitfalls, and Opportunities
Introduction
A Brief Overview of the Historical Development of Big Data
Where Did the Concept of “Big Data” Originate?
The Custodians of Big Data
Big Data as a Source for Academic Research
Challenges of Big Data for Academic Research
The Big-Ness of Big Data
Big Data are Not Research Data
Data Linkage
Big Data May Still Not Deliver Generalizable Results
Spurious Correlations, Sub-Populations, and Other Confounders
Big Data Gathering Is Not Passive
Ethical Considerations with Big Data
Opportunities of Big Data for Academic Research
Some Examples of the Application of Big Data
Openly Available Sources of Data
Conclusion
References
Part II: Teaching People to Use Big Data Effectively
Chapter 2: Big Data for Early Learners
Introduction
Key Resources Referenced Within the Chapter
About iNZight
About StatsTuneUp
Why Big Data, Statistics and Data Science?
Stimulating Young Minds – Making it Accessible and Appealing
Making the Connection
A Bag of Leaves Brings Statistics and Data Science to the Classroom
The X + STATS Initiative: What’s Your X?
iNZight
Concluding Remarks
References
Chapter 3: Using Big Data in a Master of Applied Statistics Unit
Introduction
Background
Student Demographics
Graduate Capabilities and Learning Outcomes
Topics
Assessments in the Data Mining Unit
Formative Assessments
Summative Assessments
Alignment of Formative and Summative Assessments
Case Studies – Students’ Directed Knowledge Discovery Projects
Case Study 1: Predicting Airbnb Availability
Case Study 2: Predicting the Popularity of Mashable Online Posts
Conclusion
References
Chapter 4: Statistical Education as Part of the Digital Transformation of Statistical Offices
Introduction – The Digital Revolution Means Opportunities and Challenges for Statistical Offices
Upskilling of Statisticians Via Internal Training Courses
Training Must Include Modern Subjects in Order to Get the Right Upskilling of Staff
Training at Destatis Is in Line with its Digital Agenda
The European Statistical Training Programme (ESTP) for NSIs Inside the ESS
Competence and Training Profiles and Opportunities to Study at Universities Help Destatis with Systematic Upskilling
E-Learning Can Help to Make Training more Flexible and Attractive
Cooperation with Academic, Public, and Private Entities to Further Develop Statistical Skills
The European Master in Official Statistics (EMOS) as a Way to Combine Training and Recruitment Activities
Enhanced Cooperation with Universities Can Help to Integrate New Digital Data Sources into Official Statistics
Cooperation with Private Firms Is Important for Staying Competitive
Conclusions
References
Part III: Using Big Data to Improve Teaching
Chapter 5: Big Data, Analytics and Education: Challenges, Opportunities and an Example from a Large University Unit
Introduction
Literature Review
Research Background
InfoQ Framework
Research Goal and Questions
The Case Study
Context and Participants
Data Sources
Data Analysis
Conclusions
Future Research
References
Chapter 6: Enhancing Learning Outcomes with ‘Big Data’ from Pedagogy for Conceptual Thinking with Meaning Equivalence Reusable Learning Objects (MERLO) and Interactive Concept Discovery (INCOD)
Big Data and Learning Analytics
What Is MERLO?
Diagnostics of Misconceptions with Boundary of Meaning (BOM)
Weekly Quizzes with Merlo Formative Assessments
Interactive Concept Discovery (InCoD)
Big Data Evidence-Based Learning Analytics for Merlo and INCOD
Conclusion
References
Chapter 7: Employing Authentic Analytics for More Authentic Tasks
Introduction
Rich Modelling in Tailored Environments
Reactive Learning Environments and Cognitive Apprenticeship
Model Tracing
Intelligent Books: A Project Reflection
The Rise of Learning Analytics
Authentic Learning: An Opportunity for Authentic Analytics
Beyond Computing
Conclusion
References
Chapter 8: Learning from Learning Analytics: How Much Do We Know About Patterns of Student Engagement?
Introduction
Course Data
Results
Patterns of Student Engagement with Course Virtual Environment
Clustering Weekly Engagement Patterns
Clustering Weekly Engagement Patterns by Cohort Classification
Conclusions
References
Part IV: Educational Systems That Use Big Data
Chapter 9: Museum Big Data: Perceptions and Practices
Introduction
Big Data: Definitions and Thoughts
Big Data in Museums, Libraries, Arts, and Humanities: Connections and Characteristics
Museum Big Data Projects: Some Examples
The Pen Project (Cooper-Hewitt, Smithsonian Design Museum, New York, USA)
Culture Counts and Culture Metric (Victoria, Australia & Manchester, UK)
The DMA Friends Project (the Dallas Museum of Art, USA)
The API of the Spencer Museum of Art (University of Kansas, USA)
The MET API (Metropolitan Museum of Art, New York, USA)
The Museum Big Data (MBD) Qatar Project (University College London in Qatar, Doha, Qatar)
Conclusions: Towards a Fourth V (for Visitors)
References
Chapter 10: Analysing Aspects of Brazilian Curricula for Teaching Statistics Involving Big Data
Introduction
Australian Curriculum
Brazilian National Guidelines
State Curricular Documents
Methods
Word Frequencies in the Documents
Contextual Analysis of the Words “Data”, “Research”, and “Technology” in the Documents
Conclusions
References
Part V: Conclusion
Chapter 11: Concluding Comments
References