John Vivolo - Panel Presentation: Learning Analytics - Pocket Data Analytics – Getting Started...

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Riding the Storm: Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics John Vivolo Director of Online and Virtual Learning

Transcript of John Vivolo - Panel Presentation: Learning Analytics - Pocket Data Analytics – Getting Started...

Page 1: John Vivolo - Panel Presentation: Learning Analytics - Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics

Riding the Storm: Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics

John Vivolo Director of Online and Virtual Learning

Page 2: John Vivolo - Panel Presentation: Learning Analytics - Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics
Page 3: 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?  

Page 4: John Vivolo - Panel Presentation: Learning Analytics - Pocket Data Analytics – Getting Started with Practical In-Semester Course Analytics

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  

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Weekly  Analysis    

Small  Data  Sets  

Early  Interven7on  

Understanding  student  paHerns  Improved  student  engagement    Improved  student  performance  

Pocket Data Analytics: Getting Started

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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!    

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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.  

     

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

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

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

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

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

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Single Content Item (Video): One Week of Activity

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Single Content Item (Video): One Week of Activity

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Course # 1: Discussion Board Activity (One Week of Activity)

M T W Th F S Su

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Course # 1: Discussion Board Activity (One Week of Activity)

M T W Th F S Su

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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  

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Questions? John Vivolo Director of Online and Virtual Learning Email: [email protected] Twitter: @vivolojohn Linkedin: /johnvivolo Skype: john.vivolo