This document provides an introduction and overview of learning analytics. It defines learning analytics as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning. The document outlines what learning analytics is, what its uses are, and how to get started with learning analytics. Learning analytics can benefit both teachers and learners by helping to identify at-risk students, increase engagement, provide feedback on course design, and support increased achievement. While issues around privacy, responsibility, and defining success must be considered, learning analytics tools in learning management systems and do-it-yourself options are available to help educators get started with learning analytics.

1. Laney Graduate School TATTO Extension Brown Bag
September 10, 2014 | 12:00 AM – 1:00 PM
Learning Analytics
What is it? Why do it? And How?
Prepared by Timothy Harfield, Scholar in Residence
Libraries & Information Technology Services
Updated 2014-09-10

2. An Introduction to Learning Analytics
Outline
I. What is Learning Analytics?
II. What’s the Use?
III. Getting Started

3. I. What is Learning Analytics?

4. I. What is Learning Analytics?
Definitions
ANALYTICS
Decision support for a specific knowledge domain
Information
Wisdom
Data
Data Warehousing
Data Mining Analytics

5. I. What is Learning Analytics?
Definitions
Statistics Computer Science
Data Science
Graphic Design Domain Expertise

6. I. What is Learning Analytics?
Definitions
LEARNING ANALYTICS
Decision support for learning and learning environments
“the measurement, collection, analysis and reporting of data about
learners and their contexts, for purposes of understanding and optimizing
learning and the environments in which it occurs.”
http://www.solaresearch.org/mission/about/

7. II. What is Learning Analytics?
Definitions
Academic Analytics – extracted analytics meant to support institutional decision making.
Address issues related to college rankings (ex. student retention, time to degree,
failure/success rates, hiring, resource usage), recruitment, and fundraising.
Learning Analytics – analytics that aim at increasing student success within specific
learning environments.
extracted analytics – information about user activity extracted from learning
environment, in order to make decisions about interventions to optimize student
performance. Analytics are separate from, and for the sake of, interventions.
embedded analytics – information about user activity incorporated into the
learning environment itself, as part of reflective teaching and learning practices.
Analytics are interventions in themselves.

8. II. What’s the Use?

9. II. What’s the Use?

10. II. What’s the Use?
For Teachers
1. Early identification of at-risk students
2. Increased student engagement
3. Timely feedback on course design
4. Digital Citizenship

11. II. What’s the Use?
For Learners
1. Increased understanding of online learning behaviors
• 70% of students who initially encounter an activity checking tool state that they
are intrigued, if not surprised, by the results
2. Increased levels of achievement
• Students who chose to regularly check their activity in a class are twice as likely to
earn a grade of C or higher in that class
• Students with high levels of course activity in a class are not only more likely to
pass, but have been seen to achieve a half-grade higher in subsequent classes
that require it.
SOURCES:
Fritz, J (2011) “Classroom walls that talk: Using online course activity data of successful students to raise self-awareness
of underperforming peers” in The Internet and Higher Education. 14(2)

12. II. What’s the Use?
Analytics in the Humanities
Principles for Pedagogical Learning Analytics Intervention Design
Using data as a reflective and dialogical tool between instructor and students
1. Integration
1. Agency
1. Reference Frame
1. Dialogue
SOURCE:
Alyssa Friend Wise (2014) “Designing Pedagogical Interventions to Support Student Use of Learning Analytics”

13. II. What’s the Use?
Ethical Debates
1. Student Privacy
• Family Educational Rights and Privacy Act (FERPA)
• Institutional Review Boards (IRB)
2. Student Responsibility
• Who is responsible for student success?
• Who owns student data?
3. Student Success
• Who defines success?
• Competing views

14. III. Getting Started

15. III. Getting Started
Blackboard Analytics

16. III. Getting Started
Blackboard Analytics

17. III. Getting Started
ECHO 360

18. III. Getting Started
SNAPP: Social Networks Adapting Pedagogical Practice

19. III. Getting Started
Do It Yourself

20. III. Getting Started
Do It Yourself

21. III. Getting Started
Do It Yourself

22. III. Getting Started
Additional Resources
ALE: Analytics for Learning at Emory
https://scholarblogs.emory.edu/ALE
• Learning Analytics Speaker Series
• Tools
• Examples
MOOC: Data, Analytics and Learning (EdX)
Begins 20 October 2014 | https://www.edx.org/course/utarlingtonx/utarlingtonx-link5-10x-data-analytics-2186
SoLAR: Society for Learning Analytics Research
http://solaresearch.org/
• Learning Analytics and Knowledge Conference
• Learning Analytics Summer Institutes
• Journal of Learning Analytics

23. THANKS!
Timothy D. Harfield
Scholar in Residence (Learning Analytics)
timothy.harfield@emory.edu
@tdharfield