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.
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.
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.
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.
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.
A model is suggested for campus partnerships that enables data mining of sensitive library and campus information.
Mining library and university data to understand library use patterns by Mitsunori Ogihara, John Renaud, Scott Britton, and Dingding Wang.
The Electronic Library, Vol. 33 Iss: 3, pp.355 – 372 (ISSN: 0264-0473)