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Remixing Remix: Understanding Use of Social Learning Networks
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Remixing Remix: Understanding Use of Social Learning Networks

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Collaborative presentation at the Digital Media and Learning conference (2012) with Denise Nacu, Kiley Larson, and Ruth Schmidt

Collaborative presentation at the Digital Media and Learning conference (2012) with Denise Nacu, Kiley Larson, and Ruth Schmidt

Published in: Education, Technology
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  • 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
  • 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.
  • Driving this work are some fundamental questions:
  • 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.
  • 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.
  • 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?
  • NicholeAdd screenshot(s)
  • 2. Context and SLN overview: Nichole
  • What metrics matter?
  • “Naya” outlier is taken out of here.These graphs need to be edited a bit.
  • Kiley: Social Network MapPatterns we can see about relationships and tiesA way to drive additional data collection
  • Transcript

    • 1. Remixing iRemix:Data Visualizations to UnderstandLearning and Development in OnlineSocial Learning NetworksDenise 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 LearningNetwork (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 dataExamples:!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 frameworksand 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. 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 !
    • 9. Performance Spaces Final Collaborative Anchored “Texts”! Deliverable!
    • 10. 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!
    • 11. Our Working Framework:Conceptual Framework for Analyzing SocialLearning NetworksPar2cipa2on   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  
    • 12. Data Table: Sample SLN Log DataSummary of activity across classes, sorted by class and number of logins (one month of activity)! Sample Data Handout!
    • 13. 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  
    • 14. Social Network Map: Social ConnectionsCommenting activity for one month, among students and teachers in Classes A, B, and C.!
    • 15. Social Network Map: Social ConnectionsSame data, arranged differently! Naya  
    • 16. Social Network Map: Social ConnectionsShowing two or more comments between individuals! Naya  
    • 17. Social Network Map: Social ConnectionsShowing three or more comments between individuals! Naya  
    • 18. 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  
    • 19. Radar Graph: Individual ProfilesNaya  (Female),  Class  C   Taylor  (Female),  Class  A   Ryan  (Male),  Class  C  
    • 20. Layered Timeline: Events and MultipleVariables Over Time
    • 21. 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!
    • 22. 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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