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Remixing iRemix:
Data Visualizations to Understand
Learning and Development in Online
Social Learning Networks
Denise Nacu!                 Nichole Pinkard!                    Ruth Schmidt!                   Kiley Larson!
Urban Education Institute!   Digital Youth Network!              Institute of Design, IIT!       Digital Media and Learning Hub!
University of Chicago!       DePaul University!                  Doblin | Monitor Group!         Humanities Research Institute!
dcnacu@uchicago.edu!         npinkard@digitalyouthnetwork.org!   ruth_schmidt@doblin.com 	
  !   University of California!
                                                                                                 Klarson@hri.uci.edu!




                 2012 Digital Media and Learning Conference | March 1, 2012 | San Francisco, CA
Visualizations of Social Learning
Network (SLN) Data
•  Gather, analyze, and use data about activity
   in social learning networks!
•  Uses of SLN visualizations for stakeholders:!
  –  Program Leaders: Program evaluation and
     professional development!
  –  Teachers: Reflection on practice to adapt and
     improve!
  –  Student: Reflection on own learning !
  –  Researchers and Designers: Understanding
     learning and building SLNs!
Driving Questions
•  How are youth participating and interacting
   with others, and how do we know that their
   interactions are producing learning and
   development?!
•  How are teachers/mentors/experts
   facilitating learning? !
SLNs: New ways of learning,
New kinds of data
Examples:!
SLN	
  features	
                                           Kinds	
  of	
  data	
  
Structures	
  to	
  support	
  informal	
  and	
            Social	
  2es	
  
formal	
  interac2on	
  among	
  users	
  

Access	
  to	
  teachers/mentors/expert	
                   Interac2on	
  pa9erns	
  among	
  peers/
                                                            teachers/mentors/experts	
  
Asynchronous,	
  online	
  communica2on	
                   Access	
  and	
  par2cipa2on	
  pa9erns	
  

Crea2ng,	
  sharing,	
  and	
  discussing	
                 Engagement	
  with	
  content	
  
mul2media	
  content	
                                      Connec2ng	
  use	
  pa9erns	
  with	
  pa9erns	
  of	
  
                                                            learning	
  and	
  development	
  
Ability	
  to	
  structure	
  learning	
  ac2vi2es	
  and	
   Engagement	
  and	
  impact	
  of	
  specific	
  
projects	
                                                    learning	
  resources	
  
The need for shared frameworks
and tools for this kind of data
•  Lack of SLN models!
•  Need for models of practice in SLNs!

What should learning look like in social learning networks?!

What does effective teacher practice look like in social
 learning networks?!
Goals for this Session
•  Launch a working group!
•  Describe our R & D context!
•  Share our working analytical framework
   and visualization examples!
•  Start a discussion about key questions,
   data elements, and data visualization!
iRemix Platform
•    Closed social learning network!
•    Profile pages!
•    Groups!
•    Access to mentors!
•    Ability to share and comment on projects and have
     discussions!
•    Self-paced student curriculum!
•    Learning badges!
•    Assessment rubric!
•    Embedded flash-based design tools to enable
     students to do projects on their own time!
Use Context: C21
                            Curriculum!
              Writing!
              Consists of units built around key writing
              artifacts that enable students to write
              across genres for authentic audiences.
              Students learn that the structures used
              to tell a textual story extend to other
              modes of communication.!

              Digital Storytelling!
              Students will tell stories—stories
              important to them, about them, about
              their world. Students will tell these
              stories through different media types: !
              !   •    Photography!
                  •    Video!
                  •    Graphic Design !
                  •    Podcasting !
Performance Spaces
                     Final Collaborative
 Anchored “Texts”!       Deliverable!
Implementation Context
•    Writing/Digital Storytelling curriculum!
•    81 sixth graders!
                             Class	
  A	
     Class	
  B	
          Class	
  C	
  
•    3 Classes!
•    2 Writing teachers! take	
  home	
   iPads	
  to	
  take	
   during	
  class	
  
                            Laptops	
  to	
  
                                               home	
  
                                                                    Laptops	
  

                                                                   but	
  not	
  to	
  
•    2 Media mentors!                                             take	
  home	
  

•    2 Writing mentors!
Our Working Framework:
Conceptual Framework for Analyzing Social
Learning Networks
Par2cipa2on	
                  Consump2on	
                 Contribu2on	
                 Produc2on	
  
Access	
  and	
                Views	
  of	
  specific	
     Content	
  posted	
  to	
     Pos2ng	
  original	
  
membership	
                   items	
                      the	
  network	
              media	
  



Impact	
                       Rela2onships	
               Exper2se	
                    Interests	
  
Evidence	
  of	
  impact	
     Social	
  2es,	
             Evidence	
  of	
  skill	
     Emergence	
  and	
  
by	
  individuals	
            interac2ons,	
  and	
        development	
                 development	
  of	
  
                               group	
  affilia2ons	
                                       interests	
  
Data Table: Sample SLN Log Data
Summary of activity across classes, sorted by class and number of logins (one month of activity)!




        Sample Data Handout!
Bar Graphs: Comparing Technology Access
            Consumption and Contribution activity across                                                                                            Participation activity by time of day/day of week
            classes (one month of activity)!                                                                                                        across classes (one month of activity)!
100	
                                                                                                                                     80	
  

                                                                                                                                                                                                                                   72	
  
                                                                                   Average	
  of	
  Consump2on	
                                                                                                                            Average	
  of	
  Morning	
  
  90	
      86	
                                                                                                                          70	
                                                                                              Average	
  of	
  During	
  
                                                                                   Average	
  of	
  Original	
  Posts	
                                                                                                                     Average	
  of	
  AVerschool	
  
  80	
  
                                                                                                                                                                                                                                            Average	
  of	
  Night	
  
                                                       71	
                        Average	
  of	
  Average	
  views	
  of	
              60	
  
                                                                                                                                                                                       56	
                                                 Average	
  of	
  Weekend	
  
                                                                                   original	
  posts	
  
  70	
  
                                                                                   Average	
  of	
  Comments	
  on	
  
                                                                                   Original	
  Posts	
                                    50	
  
  60	
  
                                                                                   Average	
  of	
  Contribu2on	
  

                                                                                                                                                            39	
  
  50	
                                                                                                                                    40	
  


                                                                                                                                                                                                        31	
  
  40	
  
                                                                                                                                          30	
                                28	
                               27	
  
                                                                                                                                                                     26	
                                                 26	
  

  30	
                                                                                                                                                                                                                                             21	
  
                                                                                                                                          20	
  
  20	
                                17	
  
                             14	
                                                                  14	
  
                                                                                12	
                                                                                                                                                                                10	
   9	
  
                                                                                                                                                    9	
  
                     8	
                       8	
                      9	
                                                               10	
                                                                                                              8	
  
  10	
                                                          7	
                      7	
                        6	
                                                                         5	
  
                                                                                                            1	
             2	
   1	
  
    0	
                                                                                                                                     0	
  
                       Class	
  A	
                               Class	
  B	
                                Class	
  C	
                                     Class	
  A	
                                Class	
  B	
                               Class	
  C	
  
                       Laptops	
                                   iPads	
                              in-­‐class	
  only	
                                  Laptops	
                                     iPads	
                             in-­‐class	
  only	
  
Social Network Map: Social Connections
Commenting activity for one month, among students and teachers in Classes A, B, and C.!
Social Network Map: Social Connections
Same data, arranged differently!




                                     Naya	
  
Social Network Map: Social Connections
Showing two or more comments between individuals!




                                                    Naya	
  
Social Network Map: Social Connections
Showing three or more comments between individuals!




                                                      Naya	
  
Radar Graph: Individual Profiles
            Showing (1) time of day user participated, (2) breakdown of activities by type, and (3) activity relative to others in the network!



1	
                                                    3	
  




    2	
  
Radar Graph: Individual Profiles
Naya	
  (Female),	
  Class	
  C	
                               Taylor	
  (Female),	
  Class	
  A	
  




                                      Ryan	
  (Male),	
  Class	
  C	
  
Layered Timeline: Events and Multiple
Variables Over Time
Breakout Groups
•  Questions: What research areas
   do you want to explore?!
•  Unit of Analysis: Who/what is
   the focus of your interest?!
•  User Activities: What user
   behaviors can help us address
   this question?!
•  Data Elements: What trackable
   social network data can indicate
   these user activities?!
•  Visualizations: What
   mechanisms best express the
   relationships we are interested
   in?!
•  Supports: What additional data
   or information may be important?!
                                       Discussion Card Deck!
Thank you.
                  We are looking forward to connecting with you in the future!!




Denise Nacu!                 Nichole Pinkard!                    Ruth Schmidt!                   Kiley Larson!
Urban Education Institute!   Digital Youth Network!              Institute of Design, IIT!       Digital Media and Learning Hub!
University of Chicago!       DePaul University!                  Doblin | Monitor Group!         Humanities Research Institute!
dcnacu@uchicago.edu!         npinkard@digitalyouthnetwork.org!   ruth_schmidt@doblin.com 	
  !   University of California!
                                                                                                 Klarson@hri.uci.edu!

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Remixing Remix: Understanding Use of Social Learning Networks

  • 1. Remixing iRemix: Data Visualizations to Understand Learning and Development in Online Social Learning Networks Denise Nacu! Nichole Pinkard! Ruth Schmidt! Kiley Larson! Urban Education Institute! Digital Youth Network! Institute of Design, IIT! Digital Media and Learning Hub! University of Chicago! DePaul University! Doblin | Monitor Group! Humanities Research Institute! dcnacu@uchicago.edu! npinkard@digitalyouthnetwork.org! ruth_schmidt@doblin.com  ! University of California! Klarson@hri.uci.edu! 2012 Digital Media and Learning Conference | March 1, 2012 | San Francisco, CA
  • 2. Visualizations of Social Learning Network (SLN) Data •  Gather, analyze, and use data about activity in social learning networks! •  Uses of SLN visualizations for stakeholders:! –  Program Leaders: Program evaluation and professional development! –  Teachers: Reflection on practice to adapt and improve! –  Student: Reflection on own learning ! –  Researchers and Designers: Understanding learning and building SLNs!
  • 3. Driving Questions •  How are youth participating and interacting with others, and how do we know that their interactions are producing learning and development?! •  How are teachers/mentors/experts facilitating learning? !
  • 4. SLNs: New ways of learning, New kinds of data Examples:! SLN  features   Kinds  of  data   Structures  to  support  informal  and   Social  2es   formal  interac2on  among  users   Access  to  teachers/mentors/expert   Interac2on  pa9erns  among  peers/ teachers/mentors/experts   Asynchronous,  online  communica2on   Access  and  par2cipa2on  pa9erns   Crea2ng,  sharing,  and  discussing   Engagement  with  content   mul2media  content   Connec2ng  use  pa9erns  with  pa9erns  of   learning  and  development   Ability  to  structure  learning  ac2vi2es  and   Engagement  and  impact  of  specific   projects   learning  resources  
  • 5. The need for shared frameworks and tools for this kind of data •  Lack of SLN models! •  Need for models of practice in SLNs! What should learning look like in social learning networks?! What does effective teacher practice look like in social learning networks?!
  • 6. Goals for this Session •  Launch a working group! •  Describe our R & D context! •  Share our working analytical framework and visualization examples! •  Start a discussion about key questions, data elements, and data visualization!
  • 7. iRemix Platform •  Closed social learning network! •  Profile pages! •  Groups! •  Access to mentors! •  Ability to share and comment on projects and have discussions! •  Self-paced student curriculum! •  Learning badges! •  Assessment rubric! •  Embedded flash-based design tools to enable students to do projects on their own time!
  • 8.
  • 9.
  • 10. Use Context: C21 Curriculum! Writing! Consists of units built around key writing artifacts that enable students to write across genres for authentic audiences. Students learn that the structures used to tell a textual story extend to other modes of communication.! Digital Storytelling! Students will tell stories—stories important to them, about them, about their world. Students will tell these stories through different media types: ! ! •  Photography! •  Video! •  Graphic Design ! •  Podcasting !
  • 11. Performance Spaces Final Collaborative Anchored “Texts”! Deliverable!
  • 12. Implementation Context •  Writing/Digital Storytelling curriculum! •  81 sixth graders! Class  A   Class  B   Class  C   •  3 Classes! •  2 Writing teachers! take  home   iPads  to  take   during  class   Laptops  to   home   Laptops   but  not  to   •  2 Media mentors! take  home   •  2 Writing mentors!
  • 13. Our Working Framework: Conceptual Framework for Analyzing Social Learning Networks Par2cipa2on   Consump2on   Contribu2on   Produc2on   Access  and   Views  of  specific   Content  posted  to   Pos2ng  original   membership   items   the  network   media   Impact   Rela2onships   Exper2se   Interests   Evidence  of  impact   Social  2es,   Evidence  of  skill   Emergence  and   by  individuals   interac2ons,  and   development   development  of   group  affilia2ons   interests  
  • 14. Data Table: Sample SLN Log Data Summary of activity across classes, sorted by class and number of logins (one month of activity)! Sample Data Handout!
  • 15. Bar Graphs: Comparing Technology Access Consumption and Contribution activity across Participation activity by time of day/day of week classes (one month of activity)! across classes (one month of activity)! 100   80   72   Average  of  Consump2on   Average  of  Morning   90   86   70   Average  of  During   Average  of  Original  Posts   Average  of  AVerschool   80   Average  of  Night   71   Average  of  Average  views  of   60   56   Average  of  Weekend   original  posts   70   Average  of  Comments  on   Original  Posts   50   60   Average  of  Contribu2on   39   50   40   31   40   30   28   27   26   26   30   21   20   20   17   14   14   12   10   9   9   8   8   9   10   8   10   7   7   6   5   1   2   1   0   0   Class  A   Class  B   Class  C   Class  A   Class  B   Class  C   Laptops   iPads   in-­‐class  only   Laptops   iPads   in-­‐class  only  
  • 16. Social Network Map: Social Connections Commenting activity for one month, among students and teachers in Classes A, B, and C.!
  • 17. Social Network Map: Social Connections Same data, arranged differently! Naya  
  • 18. Social Network Map: Social Connections Showing two or more comments between individuals! Naya  
  • 19. Social Network Map: Social Connections Showing three or more comments between individuals! Naya  
  • 20. Radar Graph: Individual Profiles Showing (1) time of day user participated, (2) breakdown of activities by type, and (3) activity relative to others in the network! 1   3   2  
  • 21. Radar Graph: Individual Profiles Naya  (Female),  Class  C   Taylor  (Female),  Class  A   Ryan  (Male),  Class  C  
  • 22. Layered Timeline: Events and Multiple Variables Over Time
  • 23. Breakout Groups •  Questions: What research areas do you want to explore?! •  Unit of Analysis: Who/what is the focus of your interest?! •  User Activities: What user behaviors can help us address this question?! •  Data Elements: What trackable social network data can indicate these user activities?! •  Visualizations: What mechanisms best express the relationships we are interested in?! •  Supports: What additional data or information may be important?! Discussion Card Deck!
  • 24. Thank you. We are looking forward to connecting with you in the future!! Denise Nacu! Nichole Pinkard! Ruth Schmidt! Kiley Larson! Urban Education Institute! Digital Youth Network! Institute of Design, IIT! Digital Media and Learning Hub! University of Chicago! DePaul University! Doblin | Monitor Group! Humanities Research Institute! dcnacu@uchicago.edu! npinkard@digitalyouthnetwork.org! ruth_schmidt@doblin.com  ! University of California! Klarson@hri.uci.edu!

Notes de l'éditeur

  1. Overall Structure11:00 Intro: Denise, 8 mins11:08 Context and iRemix: Nichole, 10 mins11:18 Our working framework and example: Denise, 7 mins11:25 Social Network Map: Kiley,10 mins11:35 Other visualization examples: Ruth,10 mins11:45 Introduction to breakout activity and start of breakout: Ruth, 30 mins12:15 Whole group discussion: moderated by Nichole, 15 mins12:30 Session end
  2. DeniseWe are focusing on capturing, analyzing, and using data about user activities in social learning networks.We have a particular interest in building effective data visualizations that can be used for a several of important purposes by a variety of stakeholders. What data do we need to capture, organize, and make usable for these stakeholders.
  3. Driving this work are some fundamental questions:
  4. While there are a variety of online social networks organized for learning goals, we highlight a few features here.As we develop new ways of designing online spaces to promote social learning, we need to take the opportunity to find new ways of understanding how learning is happening in these networks. For example, SLNs enable students, teachers, mentors, and expert to interact in new ways. To test our assumptions about the kinds of interactions we want to see, we need to look at social ties and interaction patterns.Given this opportunity to build and and collect data from this new kind of learning environment, it’s critical that we make sure we are asking the right questions and making good use of the data. What are the metrics that really matter? And, what kinds of data are more difficult to capture through sources like activity logs, but are going to be critical to answering these questions.
  5. Furthermore, since we are at the beginning stages of building and implementing social learning networks, there are many open questions about what learning SHOULD look like. What kinds of behaviors do we want to encourage among teachers and mentors? What does good online mentoring look like?Therefore, we see one very important use of social network use data is is to build models of practice, that can be used to inform the technology design as well as teacher/mentor development.We need to create a picture of what learning in a social network should/could be like.
  6. We will describe the specifics about our context, social network platform, and analysis framework. However, we see these issues as relevant to a broad range of contexts and applications in which social networks are being used to support learning.Goals for this SessionLaunch a working group We would love to bring together a group of from a variety of fields and backgrounds that can continue to share tools, methods, frameworks, and insights related to SLN data and visualization. We hope we can get connected and find potential collaborators. We are also interested in sharing datasets to further our goals.Describe our R & D contextShare some examplesStart some discussion to explore questions, data elements, and visualizations.What are the important questions to answer?What data do we need? What is easy to capture? What will be more difficult to capture?
  7. NicholeAdd screenshot(s)
  8. 2. Context and SLN overview: Nichole
  9. What metrics matter?
  10. “Naya” outlier is taken out of here.These graphs need to be edited a bit.
  11. Kiley: Social Network MapPatterns we can see about relationships and tiesA way to drive additional data collection