This book explores and further expands on the rich history of theoretical and empirical research in open and distributed learning, and addresses the impact of the “data revolution” and the emergence of learning analytics on this increasingly diverse form of educational delivery. Following an introductory chapter that maps the book’s conceptual rationale, the book discusses the potential, challenges and practices of learning analytics in various open and distributed contexts. A concluding chapter briefly summarises the chapters before providing a tentative future research agenda for learning analytics in open and distributed environments.
Author(s): Paul Prinsloo, Sharon Slade, Mohammad Khalil
Series: SpringerBriefs in Education
Publisher: Springer
Year: 2022
Language: English
Pages: 128
City: Singapore
Foreword
Contents
1 Introduction: Learning Analytics in Open and Distributed Learning—Potential and Challenges
Background to the Rationale of the Book
Rationale for the Book
Target Audience
Structure of the Book
References
2 The Potential of Learning Analytics for Intervention in ODL
Introduction
Intervention in ODL
Related Models and Theories
Intervention Practices Over the Years
Method
Learning Issues
Learning Needs
Interpersonal Interaction
Online Discussion
Collaborative Learning
Student Engagement
Student Dropout
The Potential of Learning Analytics
Changing Modes of ODL with Technological Advances
Cost-Effectiveness of Intervention
Personalisation in Intervention
Summary and Future Work
References
3 A Global South Perspective on Learning Analytics in an Open Distance E-learning (ODeL) Institution
Introduction
Context
Evolving Student Profiles
Learning Analytics
Case Study for Predictive Analytics at UNISA
Scoping the Available Data and Choosing an Analytical Technique
Available Data
An Analytical Technique
The Resulting (Learning) Analytics
Understanding the (Learning) Analytics and Its Limitations
Indistinct Results
Oversimplification of the Outcome Variable
Lack of LMS Data
Future Directions
Conclusion: The Value of the Case Study Highlighted
References
4 Learning Analytics in Open and Distance Higher Education: The Case of the Open University UK
Introduction
Settings: The Open University (OU) UK
Analytics for Action (A4A)
Early Alert Indicators (EAI)
Lessons Learnt from the Implementation of Learning Analytics at the Open University UK
Conclusions
References
5 Mobile Multimodal Learning Analytics Conceptual Framework to Support Student Self-Regulated Learning (MOLAM)
Introduction
Background
Mobile Multimodal Learning Analytics Approach
Learning Settings
Learner Data
Analytics and Measurement
Action and Support
Privacy Principles and MOLAM
Conclusions
References
6 Designing a Social Learning Analytics Tool for Open Annotation and Collaborative Learning
Introduction
Open and Social: Activity, Annotation, and Analytics
Reporting Crowd Annotation, Encouraging Discourse Layers
Design Context
Dashboard Traits and Design Trade-offs
Conclusion
References
7 Situating Learning Analytics for Course Design in Online Secondary Contexts
Challenges of Online Learning
Learning Analytics Challenges
Developments Toward Solutions
Situative Theories of Learning in Learning Analytics
Site Context and Methods
Participants and Analysis
Procedures
Preliminary Developments
Identifying Types of Participation
Concluding Thoughts
References
8 Ethical Considerations of Artificial Intelligence in Learning Analytics in Distance Education Contexts
Introduction
AI and Learning Analytics in Context
Definition(s) and Current Uses of AI in Higher Education
Profiling and Prediction
Intelligent Tutoring Systems (ITS)
Assessment and Evaluation
Adaptive Systems and Personalisation
Concerns and Ethical Issues in AIED
Human Rights Concerns
Data Ownership
Data Privacy and Consent
Digital Exclusion Due to Algorithmic Biases
Student and Staff Views
Conclusion
References
9 Conclusion
References
