Presentation from Penn State University Web Conference. June 23, 2015

1. Learning Analytics: Today, Tomorrow, and When
We Get Flying Cars
Megan Bowe
@meganbowe

2. Learning Analytics, the definition(s)
1. Learning analytics is 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.
2. Learning Analytics use techniques from information
science, sociology, psychology, statistics, machine
learning, and data mining used to analyze data collected
during education administration and services, teaching
and learning to create applications that directly influence
educational practice.
1. https://en.wikipedia.org/wiki/Learning_analytics
2. https://sites.google.com/a/umail.iu.edu/iuncc/whatis

3. Educational Data Mining
Developing methods and using techniques from statistics,
machine learning, and data mining to analyze data
collected during teaching and learning (e.g., from a
courseware platform) to test learning theories and inform
educational practice
https://sites.google.com/a/umail.iu.edu/iuncc/whatis

4. So. Many. Words.
• Data mining is digging through
data sets to clean up and find
patterns
• Analytics take those patterns into
reality to find meaning and give
insights

5. Great! How do we do this?
My students use
X and I want to
know Y
We’ve been using
this LMS and SIS for
decades, there must be
something to learn in
there The most
business critical
issue is student
retention, tell me who
is at risk of dropping
out
Define a goal, then…

6. Uh, thanks Internet. This is totally clear now…

7. Better.
by Timothy Hartfield http://timothyharfield.com/blog/2014/09/11/learning-analytics-what-is-it-why-do-it-and-how/

8. Define your data sources
Activity 1 Activity 3 Activity 5
Learning experience design
Activity 2 Activity 4 Activity 6

9. Get all of the data together
Data
Warehouse?
Data Mart?
Database?
Learning Record
Store?

10. Dig around, mine if you must.
• What activity creates what data?
• Is there an ideal path through the activities?
• What data or sequences are missing or unexpected?
• What clean-up work needs to be done?

11. Design ways to show
others your insights!
How do people use pdfs?

12. Design ways to show
others your insights!
How do people use pdfs?

13. Today, most of us have an LMS (like it or not)
• Grades
• Scores
• Duration
• Logins
• Page/Course access

14. Tomorrow, we get more holistic views
LMS
Flashcard
app
Youtube
Class
Attendance
Report!
eBook
Degree
Progress

15. Standards and standard units
of measurement
The Experience API and
matching equivalent data

16. Past Present Future
Information
What happened?
(Reporting)
What is happening
now?
(Alerts)
What will happen?
(Extrapolation)
Insight
How and why did
it happen?
(Modeling and
experiment
design)
What’s the next
best action?
(Recommendations)
What could
happen?
(Prediction,
optimization,
simulation)
Davenport et al “Analytics at Work”

17. …and the flying cars.
(it gets creepy)

18. –Tony Shan
“All in all, Fast Data, Actionable Data, Relevant
Data, and Smart Data (FARS) are well poised
today to replace Big Data for the new
paradigm.”

19. “Big Data is Really Dead”
• Fast Data - processing a large
amount of data in real time to find
immediate insights and patterns
• Actionable Data - a combination
of predictive analytics and
hypotheses to make
recommendations and use
feedback to make decisions
• Relevant Data - relationships in
data help to identify patterns that
seem unrelated on the surface
• Smart Data - using meaning and
algorithms to make predictions
and support decision making

20. Megan Bowe
megan@makingbetter.us
@meganbowe
Image: nolnet/Flickr