Learning Analytics Panel Moderator: Dr. Jill Buban, Senior Director of Research and Innovation, Online Learning Consortium. https://www.slideshare.net/alexandrapickett/olc-panel-presentation-learning-analytics Panelists: Patsy Moskal, Associate Director, Research Initiative for Teaching Effectiveness (RITE), University of Central Florida (UCF). John Vivolo, Director, Online and Virtual Learning for New York University (NYU). Scott James, Data Scientist, PAR Framework, a division of Hobsons. https://www.slideshare.net/alexandrapickett/scott-james-panel-presentation-analytics-in-higher-education-a-closer-look-at-online-learning Open SUNY COTE Summit 2017 - Open SUNY Center for Online Teaching Excellence (https://commons.suny.edu/cotehub/) Annual conference for the SUNY online teaching and learning community of practice. March 8-10, 2017, Syracuse, NY. Conference website: http://opensunycotesummit2017.edublogs.org/ Program: http://opensunycotesummit2017.edublogs.org/about/program/#.WMvhhP21vZs Recordings: http://opensunycotesummit2017.edublogs.org/mediasite/#.WMvkv_21vZs Materials: http://opensunycotesummit2017.edublogs.org/mediasite/#.WMvkv_21vZs Open SUNY COTE: http://commons.suny.edu/cote/

1. Examining the Hype and Reality of
Learning Analytics"
Patsy
Moskal
University
of
Central
Florida

2. “the use of data, statistical analysis, and
explanatory and predictive models to gain insight
and act on complex issues.”
Analytics!
!
The
Analy8cs
Landscape
in
Higher
Educa8on,
2015
(ECAR).
Retrieved
from
hHps://
library.educause.edu/~/media/files/library/2015/5/ers1504cl.pdf

6. Learning Analytics
“The measurement, collection, analysis and report of
data about learners and their contexts, for purposes of
understanding and optimizing learning and the
environments in which it occurs.”
hHps://solaresearch.org/

7. “Learning analytics collects and analyzes the
‘digital breadcrumbs’ that students leave as they
interact with various computer systems to look for
correlations between those activities and learning
outcomes.”
Learning Analytics!
!
7
Things
you
should
know
about
first-­‐genera8on
learning
analy8cs.
EDUCAUSE
Learning
Ini3a3ve
(ELI).
2011.
Retrieved
from
hHps://net.educause.edu/ir/library/
pdf/ELI7079.pdf

8. Check it out…!
hHps://library.educause.edu/
resources/2015/5/analy8cs-­‐
in-­‐higher-­‐educa8on-­‐2015

11. Major
Priority
Not
a
Priority

12. Students’ interest in analytics applications

13. Student perspectives on using analytics
Helpful
“big mother”
Creepy
“big brother”
Progress
toward
degree
or
cer8ficate
Performance
in
current
courses
Performance
in
past
courses
Performance
in
courses
compared
to
other
students
Ac8vity
in
university
applica8on
or
service
Ac8vity
on
a
college
or
university
website
Campus
ac8vi8es
logged
through
student
ID/smart
card
Campus
ac8vi8es
logged
through
smartphone
Proximity
to
a
college
building,
office,
or
resource
Loca8on
on
campus
Social
media
ac8vi8es

14. Predictive Analytics in Higher
Education:!
Five Guiding Practices for Ethical Use!
• Have a vision and plan
• Build a supportive infrastructure
• Work to ensure proper use of data
• Design predictive models and algorithms that avoid bias
• Meet institutional goals and improve student outcomes by
intervening with care.
Reported
in
Inside
Higher
Ed
3/6/17
–
‘Conversa8on
Starter’
on
Ethical
Data
Use
For
more
informa8on,
visit
newamerica.org/dataethics.

15. Learning analytics in
practice!

16. Available Data!
!SIS
• Demographics
• Socio-­‐economic
status
• High
school
informa8on
• Placement
tests
(SAT/ACT)
LMS
• Students’
behavior/interac8on
with
system
• Access
(views,
8me)
• Gradebook
data
Other
data
(varies
widely!)
• Use
of
advising
&
tutoring
• Interac8on
with
various
campus
services
(library,
wri8ng
center,
dining
hall,
etc)
• AHendance
at
large
lecture
classes
• Financial
behavior
• Orienta8on
par8cipa8on

17. The big issue is…!

18. Who
is
at
risk???
hHp://www.youthareawesome.com/wp-­‐content/uploads/2010/10/wheres-­‐waldo1.jpg

19. A more difficult task
is…!

20. hHp://matrix.wikia.com/wiki/File:Matrix-­‐neo-­‐stops-­‐bullets-­‐wallpaper.jpg
How
do
we
stop
them
from
failing?
How
do
we
stop
them
from
failing?

21. High Level to !
Home Grown !
Learning Analytics
Approaches!

22. § SIS, LMS and other data
§ Model trends across time to identify and ultimately predict
which students are at risk
Trawl net approach!

23. UCF Student
Performance
Dashboard!

24. UMBC
Check My
Activity
(John Fritz)!
Fritz & Whitmer (2017) Learning Analytics Research for LMS Course Design: Two
Studies.
http://er.educause.edu/articles/2017/2/learning-analytics-research-for-lms-course-
design-two-studies

25. Some Prototype !
Adaptive Learners!

27. Students’ adaptive learning behaviors!

28. How Students Behave in Personalized
Adaptive Courses!
• Colm Howlin, Principal Researcher
• March 16, 2017
• Realizeit Webinar http://realizeitlearning.com/
community/

29. Questions still being asked…!
" " " " " "…your mileage may vary!
Issues/strategies
with
implementaCon
Sources
of
data
to
use
Cost
issues
InsCtuConal
approaches
Improving
student
outcomes
Governance
strategy
and
milestones
Details,
benefits,
challenges
Security
and
ethics
concerns

30. Final Resources to
Check Out…!

31. Special Learning Analytics issue of Online Learning
http://onlinelearningconsortium.org/read/online-learning-journal/
Editors:
Karen Vignare, Patsy Moskal, Matthew Pistilli and Alyssa Wise

32. Learning Analytics Bibligraphy "!
• Online Learning list of articles
• Must read articles
• General bibliography
• Early alert system articles
• Articles focused on ethics

33. Patsy Moskal!
FPatsy.Moskal@ucf.edu!
For
more
informa8on,
contact:
Director,!
Research Initiative for Teaching Effectiveness!
Center for Distributed Learning!
University of Central Florida!