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

Alexandra M. Pickett
Alexandra M. PickettAssociate Director
Riding the Storm:
Pocket Data Analytics – Getting Started
with Practical In-Semester Course
Analytics
John Vivolo
Director of Online and Virtual Learning
John Vivolo - Panel Presentation: Learning Analytics - Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics
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 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	
  
Weekly	
  
Analysis	
  	
  
Small	
  
Data	
  
Sets	
  
Early	
  
Interven7on	
  
Understanding	
  
student	
  paHerns	
  
Improved	
  student	
  
engagement	
  	
  
Improved	
  student	
  
performance	
  
Pocket Data Analytics: Getting Started
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!	
  
	
  
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.	
  
	
  
	
  
	
  
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	
  	
  
	
  
•  Assignment/Content	
  Alerts	
  Student	
  does	
  not	
  access	
  content	
  
	
  
•  Due	
  Date	
  Alerts	
  Student	
  has	
  not	
  submiHed	
  assignment	
  (before/a<er)	
  
due	
  date	
  
Questions?
John Vivolo
Director of Online and Virtual Learning
Email: John.Vivolo@nyu.edu
Twitter: @vivolojohn
Linkedin: /johnvivolo
Skype: john.vivolo
	
  
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John Vivolo - Panel Presentation: Learning Analytics - Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics

  • 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