The document discusses learning analytics and why they are important for educational institutions. It begins by defining analytics and explaining why they are useful for optimizing online experiences by analyzing user data and behavior. It then discusses how educational institutions can apply analytics to improve retention by mining data from sources like learning management systems and course surveys. Finally, it provides tips for implementing analytics, noting that analytics can help improve learning outcomes but institutions need to have the right skills and use data responsibly to ensure it benefits students.

1. Making Learning Analytics Matter in
the Educational Enterprise
Ellen Wagner
Partner and Sr. Analyst , Sage Road Solutions, LLC
Executive Director, WICHE Cooperative for
Educational Technologies (WCET)
Sage Road Solutions LLC 1

2. Are You in the Right Place?
• You have been hearing a lot about “analytics” lately and are
wondering what the buzz is all about
• You are worried that “analytics” is a code word for “statistics”
• You just want someone to explain what analytics are, why
they matter and what you need to know

3. What I will be covering in today’s session
• What analytics are and why they are taking the world by storm
• Tips for navigating the analytics ecosystem
• Why learning analytics are particularly interesting
• Things to keep in mind about making learning analytics matter
in your educational enterprise

4. WHY ANALYTICS ARE TAKING THE
WORLD BY STORM

5. Data Are Optimizing Online Experience
The digital “breadcrumbs” that online technology
users leave behind about viewing, engagement and
behaviors, interests and preferences provide massive
amounts of information that can be mined to better
optimize online experiences.
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6. DATA IN DAILY LIFE:
LOTS OF DATA, ALL THE TIME
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7. Major Trends at Play
• Data Warehouses and “the Cloud” make it possible to collect,
manage and maintain massive numbers of records.
• Sophisticated technology platforms provide computing power
necessary to grind through calculations and turn the mass of
numbers into meaningful patterns.
• Data mining uses descriptive and inferential statistics —
moving averages, correlations, regressions, graph analysis,
market basket analysis, and tokenization – to look inside
patterns for actionable information.
• Predictive techniques, such as neural networks and decision
trees, help anticipate behavior and events.
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8. Gartner Pattern Based Strategy, 2010:
From reacting to events that had major effects on business
strategy to proactively seeking patterns that might indicate an
impending event.
The interest in Pattern-Based Strategy is likely to grow as we
understand the technologies that are emerging to seek patterns
– from both traditional (financial information, customer order data,
inventory, etc.)
– nontraditional sources of information (social media, news, blogs).
Gartner Research, Inc. 3 August 2010
ID Number: G00205744. p.4

9. Emergence of Business Intelligence
• Research typically reports empirical evidence to prove the
tenability of ideas concepts and constructs.
• Business Intelligence uses analytical techniques to mine data
to make decisions and create action plans.
• Techniques for analyses include many of the same tools, but
the focus on structuring the research question is very
different.

10. Putting Your Information to Work
Courtesy Phil Ice, American Public University System

11. Learning Organizations and Data Analytics
• Analytics have ramped up everyone’s expectations for
accountability, transparency and quality.
• Learning and development organizations simply cannot live
outside the enterprise focus on measurable, tangible results
driving IT, operations, finance and other mission critical
applications.
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12. The Case for Analytics in Learning
• The learning world is starting to discover what Internet
marketers have known for years.
• The digital “breadcrumbs” that learners leave behind about
their engagement behaviors and interests provide massive
amounts of data that can be mined to improve and
personalize educational experiences
• This is making learning pros very, very nervous
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13. Will Data REALLY Optimize
Educational Experience?
RETENTION
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14. Where to Begin?
• Uncertainty about where to start
– No established industry best practice about what to measure
– No established industry best practice around methodology
• Organizational Culture, Learning Culture and Status Quo
– Enterprise concern about what the data will show
– Competing priorities and lack of incentive for collaboration between
different groups
• Siloed data across the enterprise sure doesn’t help.

15. Institutional Data Sources: One Example
Courtesy Phil Ice, American Public University System, 2012

16. Where Learning Data Typically Live
 ERPs and SISs
 Demographics, financials, operations
 Macro level transactions
 Learning Management System (LMS)
 Learning transactions
 Learning outcomes
 Latent data
 End of Course Survey
 Perceptual data

17. “The LMS Problem”
 LMSs have messy data bases
 The primary function was not data collection per
se, but learning (artifact) management and
tracking
 Years of additions have created the equivalent of
a bowl of “data spaghetti”
 Good analytical solutions will pay attention to
how data is extracted

18. Learning Analytics Applications in
the .edu Enterprise

19. Lessons from Moneyball
Moneyball: The Art of Winning an
Unfair Game (ISBN 0-393-05765-8)
Michael Lewis, 2003
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20. SOME THINGS TO REMEMBER WHEN
PUTTING ANALYTICS TO WORK
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21. (1) Analytics are here
today, and they are here to
stay. Get on board or get
left behind!

22. (2) SOMEONE on your
team needs to know
statistics, databases
and research
techniques.

23. (3) Doing research on
analytics is fundamentally
different than applying
analytics results to help
learners succeed.

24. (4) It’s what we do with
the analytical findings
that really matter.

25. (5) We already have
more data than we can
handle. That means we
need to find better ways
to handle it.

26. (6) Even more
interesting data
collecting opportunities
await.

27. (7) We need to be
prepared to live under
the “sword of data.”

28. (8) There's no such
thing as “sort of”
transparent.

29. (9) We have just started to
understand the true power
that analytics bring to the
learning enterprise.

30. THANKS for your interest
Ellen Wagner
edwsonoma@gmail.com
http://wcet.wiche.edu
(9) We haven't even begun to scratch the
www.sageroadsolutions.com
surface of the possibilities.
http://twitter.com/edwsonoma
+1.415.613.2690 mobile