Learning Analytics Explained 1st Edition by Niall Sclater ISBN 113893173X 9781138931732

PART I: Background

1. The Evolution of a New Field

The Opportunities Presented by Big Data and Analytics

Data-Informed Decision Making

Pressures on Institutions

Influences

A Confusing Mix of Disciplines and Terminology

Focus of Activity

Developing the Community

Further Evolution of the Field

References

2. Expert Motivations

Understanding the Learning Process

Enhancing Learning

A Fascination with Data

Personalisation

Education and the Labour Market

Involvement in a New, Interdisciplinary Field

Institutional Motivations

Reference

PART II: Applications

3. Early Alert and Student Success

Models to Predict Student Success

Validity of the Models

Effectiveness of the Interventions

References

4. Course Recommendation

Approaches to Recommendation

Conclusion

References

5. Adaptive Learning

Definitions

Types of Adaptive Learning System

Benefits

Integrating Adaptive Learning Systems with other Data and Systems

Conclusion

References

6. Curriculum Design

The Importance of Theory

Informing Learning Design with Learning Analytics

Representing Learning Designs and Learning Analytics

Analytics Tools to Help Assess Effectiveness of the Curriculum

Faculty Skills in Interpreting the Analytics

Conclusion

References

7. Expert Thoughts on Applications

Adaptive Learning

Understanding the Learning Process

Linking Learning Analytics to Curriculum and Learning Design

PART III: Logistics

8. Data

Types of Data used for Learning Analytics

Data Sources

Incorporating Data from Outside the Institution

Preparing the Data for Use in Learning Analytics

Conclusion

References

9. Metrics and Predictive Modelling

Developing Metrics from the Data

Linear Regression

Logistic Regression

Naïve Bayes

Conclusion

References

10. Visualisation

Dashboards

Other Cohort Overview Visualisations

Representing Trends and Changes over Time

Conclusion

References

11. Intervention

Feedback

Triggers

Types of Intervention

Timing and Frequency

Wording

Adoption

Evaluating the Interventions

Conclusion

References

12. Student-Facing Analytics

The Quantified Self

Dashboards

Apps

Conclusion

References

13. Expert Thoughts on Logistics

Improving the Data Sources

Improving the Analytics

Enhancing Feedback

PART IV: Technologies

14. Architecture

Extraction and Transformation

Loading and Storing

Processing

Alerting

Displaying

Software Components

Conclusion

References

15. Standards

Benefits

Learning Technology Standards

Emerging Standards for Learning Analytics

Predictive Model Markup Languages

Student Data

Learning Activity Data

Conclusion

References

16. Products

LMS Engagement Reporting Tools

LMS-Centric Analytics Systems

SIS-Centric Analytics Systems

Business Intelligence Systems

Platform-Independent Solutions

Application-Specific Products

Niche Components

Learning Activity Provider Systems

Conclusion

References

17. Expert Thoughts on Technologies

Data Access

Standards, Open Architectures and Open-Source Systems

Tools

Reference

PART V: Deployment

18. Institutional Readiness

Leadership

Culture and Vision

Strategy and Investment

Structure and Governance

Technology and Data

Skills

Conclusion

References

19. Project Planning

Objectives

Stakeholders

Concerns of Stakeholders

Communication and Awareness Raising

Workstreams

Budget

Timescales

Risks

Conclusion

References

20. Ethics

Ethical Approaches

Obligation to Use Learning Analytics

Flawed or Inadequate Data

Invalid Predictions

Loss of Autonomy

Demotivation

Negative Impacts of Continual Monitoring

Manipulation of the Analytics by Students

Human versus Computer Mediation of the Results

Obligations Resulting from the Analytics

Prejudicial Categorisation and Treatment of Students

Reduction of the Individual to a Metric

Triage

Conclusion

References

21. Transparency and Consent

Transparency

Student Input to Analytics Processes

Informed Consent

Consent and the Law

Privacy Policies

The Logistics of Requesting Consent

Consent for Interventions

Opting Out

New Uses for the Data

Unknown Future Uses of the Data

Conclusion

References

22. Privacy and Data Protection

The Legal Context

Sensitive Data

Publicly Available Data

MOOC Data

Anonymisation

Restricting Access to Student Data

External Access

Data Ownership

Student Access to their Analytics

Data Stewardship

References

23. Expert Thoughts on Deployment

Organisational Culture

Data Literacy

Leadership

Funding

Ethics and Legal Issues

The Need for Systemic Deployment

PART VI: Future Directions

24. Emerging Techniques

Discourse Analytics

Social Network Analysis

Sentiment and Emotion Analytics

Conclusion

References

25. Expert Visions

Exploring New Data Sources

Analytics as Normal Practice

Personalisation

Linking Research to Practice

Reference

Index