John Vivolo, Director, Online and Virtual Learning for New York University (NYU). Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics Day 3 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). https://www.slideshare.net/alexandrapickett/patsy-moskal-panel-presentation-learning-analytics-examining-the-hype-and-reality-of-learning-analytics 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. Riding the Storm:
Pocket Data Analytics – Getting Started
with Practical In-Semester Course
Analytics
John Vivolo
Director of Online and Virtual Learning

3. Order from Chaos
• Can
we
find
order
in
the
chaos
of
the
online
learning
data
storm?
• Are
there
prac7cal
uses
of
course
analy7cs
to
improve
interac7on
and
performance
during
a
semester?

4. 3 Types of Practical Analytics
• Time
based
-­‐
when
students
access
the
course
as
a
whole
• Individual
assignments/content
-­‐
how
o<en
a
student
“hits”
an
assignment/content
• Discussion
Board
how/when
are
students
ac7ve
in
discussion
boards

5. Weekly
Analysis
Small
Data
Sets
Early
Interven7on
Understanding
student
paHerns
Improved
student
engagement
Improved
student
performance
Pocket Data Analytics: Getting Started

6. Course analytics can show when students are engaging with the
course as a whole, individual content, discussion boards and other
engagement tools. What is your goal once you see this data?
a) To
accommodate
student
schedules
b) Influence
when
students
engage
c) Avoid
the
“wait
and
see”
approach
d) All
of
the
above
e) None
of
the
above
f) I
have
no
idea!

7. Time-based Activity
• Days
of
the
week,
7mes
of
day,
weeks
of
the
month,
etc.
• Analy7cs
can
influence
improvements
in
content
availability,
assignment
due
dates,
webinars,
virtual
office
hours,
etc.

8. Course 1: Days of the Week
Fully Online Course: 20 Students (One Week of Activity)

9. Course 1: Hours of the Day
Fully Online Course: 20 Students (One Week of Activity)

10. Course #2: Days of the Week
Fully Online Course: 25 students (One week of activity)

11. Course #2: Days of the Week
Fully Online Course: 25 students (One week of activity)

12. Course 1: Days of the Week
Fully Online Course: 20 Students (One Week of Activity)

13. Single Content Item (Video): One Week of Activity

14. Single Content Item (Video): One Week of Activity

15. Course # 1: Discussion Board Activity
(One Week of Activity)
M T W Th F S Su

16. Course # 1: Discussion Board Activity
(One Week of Activity)
M T W Th F S Su

17. Other Proactive Methods: “Alerts”
• A8endance
Alerts
Student/faculty
don’t
access
course
• Assignment/Content
Alerts
Student
does
not
access
content
• Due
Date
Alerts
Student
has
not
submiHed
assignment
(before/a<er)
due
date

18. Questions?
John Vivolo
Director of Online and Virtual Learning
Email: John.Vivolo@nyu.edu
Twitter: @vivolojohn
Linkedin: /johnvivolo
Skype: john.vivolo