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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