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Riding the Storm:
Pocket Data Analytics – Getting Started
with Practical In-Semester Course
Analytics
John Vivolo
Director...
Order from Chaos
•  Can	
  we	
  find	
  order	
  in	
  the	
  chaos	
  of	
  the	
  online	
  learning	
  data	
  storm?	
...
3 Types of Practical Analytics
•  Time	
  based	
  -­‐	
  when	
  students	
  access	
  the	
  course	
  as	
  a	
  
whole...
Weekly	
  
Analysis	
  	
  
Small	
  
Data	
  
Sets	
  
Early	
  
Interven7on	
  
Understanding	
  
student	
  paHerns	
  ...
Course analytics can show when students are engaging with the
course as a whole, individual content, discussion boards and...
Time-based Activity
•  Days	
  of	
  the	
  week,	
  7mes	
  of	
  day,	
  weeks	
  of	
  the	
  month,	
  etc.	
  
	
  
•...
Course 1: Days of the Week
Fully Online Course: 20 Students (One Week of Activity)
Course 1: Hours of the Day
Fully Online Course: 20 Students (One Week of Activity)
Course #2: Days of the Week
Fully Online Course: 25 students (One week of activity)
Course #2: Days of the Week
Fully Online Course: 25 students (One week of activity)
Course 1: Days of the Week
Fully Online Course: 20 Students (One Week of Activity)
Single Content Item (Video): One Week of Activity
Single Content Item (Video): One Week of Activity
Course # 1: Discussion Board Activity
(One Week of Activity)
M T W Th F S Su
Course # 1: Discussion Board Activity
(One Week of Activity)
M T W Th F S Su
Other Proactive Methods: “Alerts”
•  A8endance	
  Alerts	
  Student/faculty	
  don’t	
  access	
  course	
  	
  
	
  
•  A...
Questions?
John Vivolo
Director of Online and Virtual Learning
Email: John.Vivolo@nyu.edu
Twitter: @vivolojohn
Linkedin: /...
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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/

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John Vivolo - Panel Presentation: Learning Analytics - Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics

  1. 1. Riding the Storm: Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics John Vivolo Director of Online and Virtual Learning
  2. 2. 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?  
  3. 3. 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  
  4. 4. Weekly   Analysis     Small   Data   Sets   Early   Interven7on   Understanding   student  paHerns   Improved  student   engagement     Improved  student   performance   Pocket Data Analytics: Getting Started
  5. 5. 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!    
  6. 6. 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.        
  7. 7. Course 1: Days of the Week Fully Online Course: 20 Students (One Week of Activity)
  8. 8. Course 1: Hours of the Day Fully Online Course: 20 Students (One Week of Activity)
  9. 9. Course #2: Days of the Week Fully Online Course: 25 students (One week of activity)
  10. 10. Course #2: Days of the Week Fully Online Course: 25 students (One week of activity)
  11. 11. Course 1: Days of the Week Fully Online Course: 20 Students (One Week of Activity)
  12. 12. Single Content Item (Video): One Week of Activity
  13. 13. Single Content Item (Video): One Week of Activity
  14. 14. Course # 1: Discussion Board Activity (One Week of Activity) M T W Th F S Su
  15. 15. Course # 1: Discussion Board Activity (One Week of Activity) M T W Th F S Su
  16. 16. 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  
  17. 17. Questions? John Vivolo Director of Online and Virtual Learning Email: John.Vivolo@nyu.edu Twitter: @vivolojohn Linkedin: /johnvivolo Skype: john.vivolo  

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/

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