Building a Collaborative
Knowledge Base in Diigo:
How Links, Tags, and Comments
Support Learning

Tami Im
Vanessa Dennen
P...
DIIGO
Social Bookmarking
•  Supports faster re-finding of information at a later date
(Kawase, Herder, Papadakis, and Nejdl, 201...
Research Questions
•  How effectively did Diigo support the development of
a class knowledge base?
•  How interactive was ...
Method
•  Case study
•  Online graduate level class – 6 weeks
•  27 students
•  Data sources
•  Diigo archive (primary)
• ...
Findings: Overall use
•  389 unique items bookmarked
•  57 instructor contributions
•  Individual student contributions
• ...
Findings: Sharing
and Comments
•  16 students shared more than commented
•  11 students commented more than shared
•  3 st...
Finding: tags
•  347 tags (some overlap)
•  10% used repeatedly (low density)
•  Most popular tags used 20-39 times
•  Cla...
Findings: Networks
•  Disperse network with identifiable core
•  Active participants – posted the most
•  Connected partic...
Findings:
Connecting knowledge
•  Indicators of intended knowledge brokering in
comments
•  “I will also share this with m...
Challenges
•  Challenges:
• 
• 
• 
• 

Difficult to determine ideal flow of information
Duplication occurs
Uncertainty abo...
Conclusions
•  Diigo was useful for supporting a knowledge base.
•  Evidence:
•  Relevant sharing and tagging
•  Interacti...
Thank you
Contact:
tim@fsu.edu
vdennen@fsu.edu
vanessadennen.com
References
•  Estellés, E., del Moral, E., & Gonzålez, F. (2010). Social Bookmarking Tools as
Facilitators of Learning and...
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Building a Collaborative Knowledge Base in Diigo: How Links, Tags, and Comments Support Learning (Elearn 2013)

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Abstract: This case study examines the use of Diigo, a social bookmarking tool, in an online class. The purpose of using Diigo in this class was to create a collaborative knowledge base on the course topic around which students might interact. Students contributed relevant links to the Diigo group, although tagging appeared somewhat haphazard. Toward the end of the course, a tagging scheme started to emerge. The brief course duration likely limited the benefits of Diigo to this group, at least where tagging was concerned. Not all students contributed in the same way; some contributed more links whereas others were heavy commenters. Link contributors did not dominate the comment-based interactions. Instead, comments were based on mutual interests, with students letting each other know when and how their contributions were valued.

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Building a Collaborative Knowledge Base in Diigo: How Links, Tags, and Comments Support Learning (Elearn 2013)

  1. 1. Building a Collaborative Knowledge Base in Diigo: How Links, Tags, and Comments Support Learning Tami Im Vanessa Dennen Presentation at E-learn • October 2013 • Las Vegas, NV
  2. 2. DIIGO
  3. 3. Social Bookmarking •  Supports faster re-finding of information at a later date (Kawase, Herder, Papadakis, and Nejdl, 2011). •  Ability to not only store but also retrieve tagged items and notes is critical for the successful adoption of a social bookmarking tool (Mor, Ferran, Garreta-Domingo, and Mangas, 2011). •  Diigo has been recommended for higher ed use (Greenhow, 2009; Estellés, del Moral and Gonzalez, 2010). •  Diigo supported meta-cognitive scaffolding when situated in teacher-centered context (Ya-Ping, Chun-Yi, and ChiungSui, 2011) .
  4. 4. Research Questions •  How effectively did Diigo support the development of a class knowledge base? •  How interactive was the class-based Diigo group? •  How did students use the comment and tag functions to support the development of a collaborative knowledge base? •  Did students connect knowledge in a Diigo group with their daily lives?
  5. 5. Method •  Case study •  Online graduate level class – 6 weeks •  27 students •  Data sources •  Diigo archive (primary) •  Discussion board (secondary) •  Analysis •  Descriptive statistics of use •  Content analysis of archives •  Social network analysis of actors
  6. 6. Findings: Overall use •  389 unique items bookmarked •  57 instructor contributions •  Individual student contributions •  Range 6-30 •  Mean 12 •  Fewer than ½ of student contributions spurred comments/discussion •  Use tapered toward the end of the course
  7. 7. Findings: Sharing and Comments •  16 students shared more than commented •  11 students commented more than shared •  3 students were highly active bookmarkers, but received fewer comments •  Dominant bookmarkers do not dominate the conversation •  Core comment functions: •  Thanking •  Indicating use of information •  Adding related information •  Not very conversational
  8. 8. Finding: tags •  347 tags (some overlap) •  10% used repeatedly (low density) •  Most popular tags used 20-39 times •  Class decided emergent folksonomy was best option •  Avoided obvious tags (granular tagging) •  Individual career interests led to broad tagging
  9. 9. Findings: Networks •  Disperse network with identifiable core •  Active participants – posted the most •  Connected participants – engaged the most •  Bare minimum folks •  Connected folks experienced reciprocity •  Content clusters, proclivity for homophily
  10. 10. Findings: Connecting knowledge •  Indicators of intended knowledge brokering in comments •  “I will also share this with my friends and family.” •  Indicators that links had been found within another social or knowledge network.
  11. 11. Challenges •  Challenges: •  •  •  •  Difficult to determine ideal flow of information Duplication occurs Uncertainty about how to tag Multiplicity of tool options is daunting – what contributes the most?
  12. 12. Conclusions •  Diigo was useful for supporting a knowledge base. •  Evidence: •  Relevant sharing and tagging •  Interactions •  Stated intentions to use or share information •  There may be best times in a course for building a knowledge base •  Flexible, individual outcomes are an important part of building a knowledge base
  13. 13. Thank you Contact: tim@fsu.edu vdennen@fsu.edu vanessadennen.com
  14. 14. References •  Estellés, E., del Moral, E., & Gonzålez, F. (2010). Social Bookmarking Tools as Facilitators of Learning and Research Collaborative Processes: The Diigo Case. Interdisciplinary Journal of E-Learning and Learning Objects, 6, 175-191. •  Greenhow, C. (2009). Social scholarship: Applying social networking technologies to research practices. Knowledge Quest: Journal of the American Association of School Librarians, 37(4), 42-47. •  Kawase, R., Herder, E., Papadakis, G., & Nejdl, W. (2011). In-Context annotations for refinding and sharing Web information systems and technologies. In J. Filipe & J. Cordeiro (Eds.), (Vol. 75, pp. 85-100): Springer Berlin Heidelberg. •  Mor, E., Ferran, N., Garreta-Domingo, M., & Mangas, J.-A. (2011). User Experience of Social Bookmarking Tools Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments. In J. Jacko (Ed.), (Vol. 6763, pp. 510-516): Springer Berlin / Heidelberg. •  Ya-Ping, H., Chun-Yi, S., & Chiung-Sui, C. (2011). A study of the effect on metacognition by integrating Diigo social media tool with instructor's metacognitive scaffolds in an online inquiry learning context. Paper presented at The 2nd International Conference on Next Generation Information Technology.
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