Mining library and university data to understand library use patterns. Issue 3 (1st June 2015)
- Record Type:
- Journal Article
- Title:
- Mining library and university data to understand library use patterns. Issue 3 (1st June 2015)
- Main Title:
- Mining library and university data to understand library use patterns
- Authors:
- Renaud, John
Britton, Scott
Wang, Dingding
Ogihara, Mitsunori - Abstract:
- <abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – Library data are often hard to analyze because these data come from unconnected sources, and the data sets can be very large. Furthermore, the desire to protect user privacy has prevented the retention of data that could be used to correlate library data to non-library data. The research team used data mining to determine library use patterns and to determine whether library use correlated to students' grade point average. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – A research team collected and analyzed data from the libraries, registrar and human resources. All data sets were uploaded into a single, secure data warehouse, allowing them to be analyzed and correlated. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – The analysis revealed patterns of library use by academic department, patterns of book use over 20 years and correlations between library use and grade point average. </p> </sec> <sec> <title content-type="abstract-heading">Research limitations/implications</title> <p> – Analysis of more narrowly defined user populations and collections will help develop targeted outreach efforts and manage the print collections. The data used are from one university; therefore, similar research is needed at other institutions to determine<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – Library data are often hard to analyze because these data come from unconnected sources, and the data sets can be very large. Furthermore, the desire to protect user privacy has prevented the retention of data that could be used to correlate library data to non-library data. The research team used data mining to determine library use patterns and to determine whether library use correlated to students' grade point average. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – A research team collected and analyzed data from the libraries, registrar and human resources. All data sets were uploaded into a single, secure data warehouse, allowing them to be analyzed and correlated. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – The analysis revealed patterns of library use by academic department, patterns of book use over 20 years and correlations between library use and grade point average. </p> </sec> <sec> <title content-type="abstract-heading">Research limitations/implications</title> <p> – Analysis of more narrowly defined user populations and collections will help develop targeted outreach efforts and manage the print collections. The data used are from one university; therefore, similar research is needed at other institutions to determine whether these findings are generalizable. </p> </sec> <sec> <title content-type="abstract-heading">Practical implications</title> <p> – The unexpected use of the central library by those affiliated with law resulted in cross-education of law and central library staff. Management of the print collections and user outreach efforts will reflect more nuanced selection of subject areas and departments. </p> </sec> <sec> <title content-type="abstract-heading">Originality/value</title> <p> – A model is suggested for campus partnerships that enables data mining of sensitive library and campus information.</p> </sec> </abstract> … (more)
- Is Part Of:
- Electronic library. Volume 33:Issue 3(2015)
- Journal:
- Electronic library
- Issue:
- Volume 33:Issue 3(2015)
- Issue Display:
- Volume 33, Issue 3 (2015)
- Year:
- 2015
- Volume:
- 33
- Issue:
- 3
- Issue Sort Value:
- 2015-0033-0003-0000
- Page Start:
- 355
- Page End:
- 372
- Publication Date:
- 2015-06-01
- Subjects:
- Digital libraries -- Periodicals
Libraries -- Automation -- Periodicals
025.00285 - Journal URLs:
- http://www.emeraldinsight.com/journals.htm?issn=0264-0473 ↗
http://www.emeraldinsight.com/ ↗ - DOI:
- 10.1108/EL-07-2013-0136 ↗
- Languages:
- English
- ISSNs:
- 0264-0473
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3702.580500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 3975.xml