Remixing iRemix: Data Visualizations to Understand Learning and Development in Online Social Learning Networks
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The document discusses the use of data visualizations to analyze participation and learning outcomes in social learning networks (SLNs) during a digital media and learning conference. It explores how various stakeholders, including teachers and students, can leverage SLN data for evaluation and improvement, while also emphasizing the need for models and frameworks to guide effective practice in these networks. The authors present their analytical framework, highlight key questions for discussion, and showcase examples of data collected from a specific curriculum implementation involving students and teachers.
Remixing iRemix: Data Visualizations to Understand Learning and Development in Online 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!
- 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!