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They Searched
What? Usage Data
As a Measure of
Library Services &
Outreach
Hello!
Melissa Gustafson
Electronic Resources Librarian
Indiana State University
You can find me at:
@MelissaGustafsn
About ISU
▧Public Institution
▧Students: 13,565 (11,257 undergrads)
▧Large 1st generation population
▧Large foreign population
1.
Why
What we know & what we think we know…
Reference
Outreach
▧ Exclusively tool based
instruction
▧ Less & less 1 on 1
reference interviews
▧ One shot instruction
Context ISU Library
Usage Data
▧ No standard collection
method
▧ Used solely for
renewal decisions
▧ No real behavioral
analysis
Enter Summon – SycaMORE Research 2.0
We Believe:
There is a fundamental
difference in
information seeking via
discovery versus
subject specific,
controlled thersauri
databases.
2.
How
From information to action…
“Translation is the art of failure.
-Umberto Eco
Place your screenshot here
Summon Analytics
Lots to see…..but what does it all mean?
¿Cómo se dice?
▧ Informal discussions w/ Reference librarians
▧ Attend reference dept. meetings
▧ ID their challenges in teaching
▧ What type of usage would best inform what
they do.
3.
What
….we found
Discovery
eResources
User
Behavior
Website/
LibGuides
Telling Our Story
Preferred Browsers
Firefox Chrome IE Safari
0
2000
4000
6000
8000
10000
12000
14000
Total Views
Most Used LibGuides
Finding Research Instruments, Surveys, and Tests Literature Reviews
Top Summon Searches
drag racing hypnosis motorsport motorsports psychology
Other Insights
▧On the fly “Best bets”
▧LibGuides tag refinement
▧Review placement of info on library page
Looking
Ahead
Future Directions…
eResource Highlights
Semiannual & as needed to inform outreach
& instruction!
The ball keeps rolling
Continued
Refinement
Automated
Data
User
Personas
Diagram featured by http://slidemodel.com
Thanks!
Any questions?
You can find me at:
@MelissaGustafsn
Melissa.Gustafson@indstate.edu
mgustfsn@gmail.com
Credits
Special thanks to all the people who made and
released these awesome resources for free:
▧ Presentation template by SlidesCarnival
▧ Photographs by Unsplash
▧ Backgrounds by Pixeden
Presentation design
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They Searched What? Usage Data As a Measure of Library Services & Outreach

  • 1. They Searched What? Usage Data As a Measure of Library Services & Outreach
  • 2. Hello! Melissa Gustafson Electronic Resources Librarian Indiana State University You can find me at: @MelissaGustafsn
  • 3. About ISU ▧Public Institution ▧Students: 13,565 (11,257 undergrads) ▧Large 1st generation population ▧Large foreign population
  • 4. 1. Why What we know & what we think we know…
  • 5. Reference Outreach ▧ Exclusively tool based instruction ▧ Less & less 1 on 1 reference interviews ▧ One shot instruction Context ISU Library Usage Data ▧ No standard collection method ▧ Used solely for renewal decisions ▧ No real behavioral analysis
  • 6. Enter Summon – SycaMORE Research 2.0 We Believe: There is a fundamental difference in information seeking via discovery versus subject specific, controlled thersauri databases.
  • 8. “Translation is the art of failure. -Umberto Eco
  • 9. Place your screenshot here Summon Analytics Lots to see…..but what does it all mean?
  • 10. ¿Cómo se dice? ▧ Informal discussions w/ Reference librarians ▧ Attend reference dept. meetings ▧ ID their challenges in teaching ▧ What type of usage would best inform what they do.
  • 13. Preferred Browsers Firefox Chrome IE Safari 0 2000 4000 6000 8000 10000 12000 14000 Total Views Most Used LibGuides Finding Research Instruments, Surveys, and Tests Literature Reviews Top Summon Searches drag racing hypnosis motorsport motorsports psychology
  • 14.
  • 15. Other Insights ▧On the fly “Best bets” ▧LibGuides tag refinement ▧Review placement of info on library page
  • 17. eResource Highlights Semiannual & as needed to inform outreach & instruction!
  • 18. The ball keeps rolling Continued Refinement Automated Data User Personas Diagram featured by http://slidemodel.com
  • 19. Thanks! Any questions? You can find me at: @MelissaGustafsn Melissa.Gustafson@indstate.edu mgustfsn@gmail.com
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Editor's Notes

  1. So I’m going to start by Give a little context then tell you Why we needed to look at how we use our usage data and then How we changed our process and finally offer you some information on Future plans for the information we have
  2. Eresources librarian at Indiana state university in Terre Haute about a little over an hour away, where I have been for about 3 years now. Librarian since 2010 working in libraries since 1999. Prior to isu I worked in special libraries as eresources and reference librarian
  3. Eresources 1 librarians
  4. historically CML depts. very distinct missions within the library. Not a lot of cross collaboration…. Increasingly, as with all libraries, the need for a more holistic approach to answering the needs of CML users was necessary. While there are specializations within the library our users see one library service so we need to foster greater collaboration.
  5. The reference department’s focus on instruction was almost exclusively tool based, included one shot in class instruction, and with the collapse of the standalone reference desk into circ, one on one instruction was very much on the decline. There was a noted desire and movement among the reference faculty to move toward a more all-inclusive instructional role addressing information literacy, however issues of time and a lack of a clear understanding of our students search behavior impeded this transformation. Usage Data The latter issue was partly due to the way in which CML employed usage data which to date included solely renewal decisions. The information was passed to reference librarians as part of a cost per use analysis on an annual and as requested basis in numerical totals. And there hadn’t been a real standard collection method to obtain that usage data. A need for more in-depth visual information that would assist in behavioral analysis was recognized.
  6. So when the library acquired Summon in 2015, we were really able to dig a little deeper into USER BEHAVIOR via their built in business analytics And we were excited, but we needed to make something clear before we could hope of building any collaborative effort with reference and that is the fundamental difference we feel exists between discovery and subject specific databases. Establishing this was key to building a relationship on these matters with Reference who worried that the discovery tool as a replacement for subject specific databases. Subject specific databases with their controlled vocabulary and thesauri are still the tool for the proficient researcher or searcher as as such play an important role in the overall education of undergrad majors, graduate students, and faculty.
  7. So now that we have a sense for the context into which we are trying to make change how do we get there
  8. It starts with considering my role as translator, I want to give them data, I am EXCITED to do so. But I have to be mindful of the overall STORY that data tells or I will lose them at the onset. They have ready expressed how busy they are, so they wont have time for long explanation. And the definitely want something, like we all do, that will inform what they do in a meaningful way. The end goal is to answer a central question, how can the vast usage data electronic resources collects empower reference to better serve our users?
  9. So how do I take the vast information available on the Summon Analytics pages and turn it into something understandable….
  10. Even before I showed them what we had, I wanted to get a sense for what they needed and identify challenges they face. I did this by popping in on them and asking them about what they were working on, what was taking up their time, what was frustrating, what did they value, what did they love about work of course. And what could I give them that would help.
  11. So I took a bunch of notes and looked for what seemed like over arching issues of concern or wins….and then I started thinking about all of the data we collected
  12. A story starts to emerge by combining our various resources with the qualitative information I gleamed from the reference librarians. The combined information was translated into visual representations of our usage with summaries for reference. These dashboards included usage over months and years, across resources, and incorporated our services via the inclusion of the CML LibGuide use. The piece that tied it all together was the inclusion of search behavior and queries from our discovery layer.
  13. In this example we see “A comparison of what we are seeing in Summon versus LibGuides” and what we learned was that there is a potential missed opportunity here for our programs in college of technology and specifically Automotive Engineering Technology. Motorsport and drag racing was trending in both LibGuides and summon searches for the last two semesters. We also learned that while we have several subject specific LibGuides, the great majority of use was far and away in our scholarly communications and information literacy focused guides. It was a huge difference in use and to us points to the need for these types of materials as integral to any sort of instruction or outreach. We also made a point to look into and track the summon drilldown on “preferred browsers” . We did this specifically because when interviewing our reference faculty and in dealing with problem ticket form the same group, it was definitely noticed that they as whole preferred to use and instruct with IE. As you can see the chart clearly shows our findings….and the demonstrated need for them to shift their instruction to Chrome and Firefox, they need to become more comfortable doing so.
  14. While visits and searches overall ticked up through the semester, average searches per visit went down. Possible reasons for this trend either people where either refining their searches on the whole or they were directed to look for something very specific for class. When comparing the data to the actual queries searched in Summon it was found that it was likely the former. Also when comparing that data with faceted searching, the picture became further clear. Faceted searching grew which to both eResource and reference faculty was both positive and potentially negative. Users were indeed refining their searches and getting to know how to query the system more effectively, however they also may not be articulating their first searches well. Another opportunity for instruction. Finally, also noted and shared with the CML science librarians the specific citation searches noted were far and away among their users. The science librarians took this to mean that they needed more focused instruction in citation searching and access.
  15. On the fly best bets created for popular Subject and Services searches including, Math & Writing Center and Our largest user groups Psychology, Criminology, & Nursing Took the opportunity to look at search queries and suggest to the creators new tags, in some cases consider misspelled tags to bring them up in searches. And revisiting the placement of links on our site, what are the most popular services searched and do we need to highlight them better. I bring up Math & Writing center which was previously buried in links at the bottom of our page and moved to our students “quick links” section.
  16. So the reaction to the new dashboard and summary format was on the whole positive, and called for more regular collaboration on these types of reports.
  17. Going forward eResources will be providing updates on the reports on a semiannual and as needed basis. The reports are also being stored securely on a shared drive and can be shared with faculty groups outside the library to highlight the important work the library does and foster greater collaboration with academic departments.
  18. Continued refinement Data changes as outreach and instructions needs change. Inclusion of a variety of new measures or removal of outdated measures. – all informed by the qualitative experience our liasions and the library literature and trends. Automated data output Looking at a more streamlined Data and dashboard display? Via Hi Charts, Tableau or similar product. User Persona Profiles Deeper Information behavior analysis, user motivations and goals…