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Not Just Content

Not Just Content. Supporting Community-Building and Collaboration in Digital Libraries. Adam Worrall LIS 6279, Fall 2009 Dr. Melissa Gross 10/15/09. Research problem. Existing DLs do not support well, through their content and services, the social context surrounding and within them

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Not Just Content

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  1. Not Just Content Supporting Community-Building and Collaboration in Digital Libraries Adam Worrall LIS 6279, Fall 2009 Dr. Melissa Gross 10/15/09

  2. Research problem • Existing DLs do not support well, through their content and services, the social context surrounding and within them • Should improve this support of social interactions to integrate better with social groups and communities (Lynch, 2005) • Propose that problem be examined with exploratory pilot experimental study • Use of social annotations to support community-building and collaboration

  3. Research questions and hypotheses • Does the addition of social annotation features to the D-Scholarship2 digital library prototype change the level of support it provides for community-building by those users, communities, and networks that use its content and services? • H1: The provision of social annotation features is related to the level of support for community-building provided by D-Scholarship2.

  4. Research questions and hypotheses • Does the addition of social annotation features to the D-Scholarship2 digital library prototype change the level of support it provides for collaboration by its users? • H2: The provision of social annotation features is related to the level of support for collaboration provided by D-Scholarship2.

  5. Benefits • Short-term benefits for FSU students/faculty • Potential social annotation features evaluated • Generally (and more long term) • Greater ability for users to collaborate • Improved potential to network with other users • LIS field will gain better understanding of • social communities and networks of DL users • social / group information behavior in DLs • how DL collections and services are used • Better services provided to user communities

  6. Social annotations • Annotation: “the enrichment of information object[s] with comments and other forms of meta-information” (Neuhold et al., 2003, p. 10) • Free-form or structured • Private, public, or in-between • Include tags but not restricted to such • Social annotations are collaborative, usually public • Provide “a valuable medium for collaboration” (p. 11)

  7. Social annotations • COLLATE(Frommholz et al., 2003) • DL prototype for film studies scholars • Provided social annotations, keywords, and collaborative cataloging • Lack of evaluative user studies • Difficult to transfer results to other populations • AnswerBag(Gazan, 2008) • Web 2.0 question-and-answer site • Faced many of the same challenges as DLs • Highly successful; > 1 million users • Shows promise of social annotations method

  8. Social annotations • DEBORA(Nichols et al., 2000) • Digitized images of Renaissance books • Annotations • Both private and public • Chained like trails in Bush’s “memex” (Bush, 1945) • Results • Social annotations and chaining liked by users • Interface found to be confusing • No further research due to lack of funding / end of project • Social annotations are not a panacea

  9. Social annotations • DLESE(Arko et al., 2006) (www.dlese.org) • Educators, students, scientists in earth sciences • Developed framework for storing and creating annotation metadata • All annotations social • Users could remain anonymous • Comments, “teaching tips”, “structured reviews” • Usability issues with accessing annotations • Barriers to entry • Vetting of annotations • User education an issue

  10. Social annotations • Steve(Bearman & Trant, 2005; Trant, 2006, 2009) (www.steve.museum) • Digital museum tagging project • Most tags used not present in existing metadata/catalog records • Improved access • Future research aimed at engaging user communities through collaborative knowledge sharing using tags, folksonomies • Good example of how social tagging and annotation could help support collaboration in digital museum or digital library

  11. Variables of interest • Dependent variables • Levels of support provided by D-Scholarship2 for community-building and collaboration • Interval • Independent variable • Whether social annotation features are included • Nominal • Extraneous variable (potentially) • Role of D-Scholarship2 users • 2 ordinal scales (students, faculty)

  12. Population • Users of D-Scholarship2 • Prototype of digital library for scholarly publications and gray literature • Currently under development at FSU • Testing group: 500 total • 300 upper-level undergraduates • 150 graduate students • 50 faculty members • Note no freshmen, sophomores, staff • Further research will be necessary with other populations and DLs

  13. Recruitment of participants • E-mailed letter • Sent to all members of population • Included explanation, benefits, contact info, invitation to participate • Participants • 20 undergraduates (juniors and seniors) • 20 graduates • 10 faculty members • 50 total • Every participant from different department • Will help reduce contamination

  14. Instruments • Same measures as in survey approach • Primarily based on social network analysis • Extra questions in pretest to elaborate upon role • Students: juniors, seniors, master’s, doctoral • Faculty: how many years experience • Administered online • Question skips easier, order tightly controlled, administration completed more quickly • Unique identification strings • Will be used in any published or unpublished reports or articles

  15. Procedures • True experimental model • Randomized comparative change design • Pretest, posttest • Same instrument for both, except extra pretest questions about role • Experimental group • New version of prototype with social annotation features • Comparison group • Different, but still new, version of prototype with usability improvements to existing features • Debriefing

  16. Validity and reliability • Validity high • Instruments effectively measure variables • Same measures used in pretest and posttest • Pretest eliminates extreme score issues • Comparison group  minimal compensatory rivalry and Hawthorne effects

  17. Validity and reliability • Validity threats • Small chance of selection bias • Random assignment should help group equivalency • Possible contamination • Natural collaboration of users with each other • Limited via use of only one participant from each department • Participants encouraged not to discuss specifics of study with each other • Reliability • Not high compared to other methodologies

  18. Limitations and Ethics • Limitations • Not possible to apply results to other DLs and other users • Narrowly defined population • Further research required with other DLs • Cannot find best method • Only social annotations being studied • Further research required with other methods • Ethical considerations • No principles violated • Attempts made to equalize benefits • Debriefing will mitigate any potential harm, risks

  19. References • Arko, R. A., Ginger, K. M., Kastens, K. A., & Weatherley, J. (2006). Using annotations to add value to a digital library for education. D-Lib Magazine, 12(5). doi:10.1045/may2006-arko • Bearman, D., & Trant, J. (2005). Social terminology enhancement through vernacular engagement: Exploring collaborative annotation to encourage interaction with museum collections. D-Lib Magazine, 11(9). doi:10.1045/september2005-bearman • Frommholz, I., Brocks, H., Thiel, U., Neuhold, E., Iannone, L., Semeraro, G., . . . Ceci, M. (2003). Document-centered collaboration for scholars in the humanities: The COLLATE system. In T. Koch & I. T. Sølvberg (Eds.), Lecture Notes in Computer Science: Vol. 2769. Research and Advanced Technology for Digital Libraries (pp. 434-445). Berlin, Germany: Springer-Verlag. doi:10.1007/b11967 • Gazan, R. (2008). Social annotations in digital library collections. D-Lib Magazine, 14(11/12). doi:10.1045/november2008-gazan • Lynch, C. (2005). Where do we go from here? The next decade for digital libraries. D-Lib Magazine, 11(7/8). doi:10.1045/july2005-lynch • Neuhold, E., Neiderée, C., & Stewart, A. (2003). Personalization in digital libraries: An extended view. In Lecture Notes in Computer Science: Vol. 2911. Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access (pp. 1-16). Berlin, Germany: Springer-Verlag. doi:10.1007/b94517 • Nichols, D. M., Pemberton, D., Dalhoumi, S., Larouk, O., Belisle, C., & Twidale, M. B. (2000). DEBORA: Developing an interface to support collaboration in a digital library. In J. Borbinha & T. Baker (Eds.), Lecture Notes in Computer Science: Vol. 1923. Research and Advanced Technology for Digital Libraries (pp. 239-248). Berlin, Germany: Springer-Verlag. doi:10.1007/3-540-45268-0_22 • Trant, J. (2006). Social classification and folksonomy in art museums: Early data from the steve.museum tagger prototype. In J. Furner & J. T. Tennis (Eds.), Advances in Classification Research: Vol. 17. Proceedings of the American Society for Information Science and Technology Special Interest Group in Classification Research Workshop. Retrieved from http://dlist.sir.arizona.edu/1728/01/trant-asist-CR-steve-0611.pdf • Trant, J. (2009). Tagging, folksonomy and art museums: Early experiments and ongoing research. Journal of Digital Information, 10(1). Retrieved from https://journals.tdl.org/jodi/article/view/270/277

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