Post on 21-Jan-2016
Using Data Analytics for School Library
Assessment and Improvement
Dr. Lesley FarmerCalifornia State University Long Beach
Does this sound familiar?
I can’t find the articles I need! The catalog says the book is there, but I
can’t find it. What does it take to get a new book on the
shelf before it becomes old? No one uses our self-check out system. Should we subscribe to ebooks? Why are kids just using Google and
Wikipedia?2
What data do you collect?
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Circulation figuresPatron usageFacilities usageComputer usageInternet usageLibrary guides/bibliographies useInstructional sessionsWebsite hits (including tutorials)Database usage vs costOrdering, processing, cataloging, preservation, weeding workflow and timeEbook usage vs costLibrary software usage vs costStaff schedulingEquipment maintenance and repairs
And what tools do you use to collect data?
What do you DO with that data?
Descriptive statistics Analyze workflow for efficiency Reveal trends Benchmark efforts Control quality Do cost-benefit analysis Analyze student learning Optimize scheduling
And how do the data connect with library and school goals?
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What significant trends between 2001 and 2011 exist in California school library resources, services, and usage? To what extent do these trends relate to school demographics, staffing, resources, services, and budgets?
What is the profile of a consistently highly effective school library (in terms of resources, services, and usage)? Concurrently, what is the profile of low-performing school libraries? What relationship do these have to school demographics, if any?
What are the predictors for high – and low -- school library impact over time? What relationship do these usage patterns have to school demographics, if any?
More specifically, does a significant difference exist in school library staffing, resources, services, and usage relative to the grade levels, enrollment, type of school (public vs. private), locale (rural/suburban/urban)?
Research Questions Based on 2001-2011 California School Libraries Data
Use California State Department of Education annual school library survey reports datasets (2011-2012 here)
Code survey variables: e.g., meet standard or not Transform continuous data to normalize Compare school libraries that meet state model
school library standards baseline criteria with those who did not meet standards
Use several statistical techniques: clustering analysis, decision trees, logistic regression
Method
64 Independent Variables
Meet Standard or Not (binary) API (Academic Performance Index) Socio-economic API decile
Dependent Variables
Online library catalog Internet access Online DBs Video DBs Budget Collection currency Reference help
Dependent variable: met standards or not
CART (Classification & Regression Trees) Important Independent Variables
DEPENDENT Variable: API
Materials budget (and book budget) Access in the evening Number of books Having DVDs Having classified employees Online subscriptions (including
streaming services) Textbook service
CART Best Model:Ultimate Important Predictable Variables
Balanced Scorecard
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Decision Tree
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Failure Analysis
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