Principal Connections: Using Data to Inspire

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Principal Connections Using Data to Inspire

• Esther Rosenfeld
• OLA SuperConference
• February 3, 2006

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Education Facts of Life in Ontario 2006

• Focus on Improving Student Achievement
• Provincial EQAO Literacy and Numeracy Targets
• School Boards Must Have Plans to Achieve Targets
• Schools must have Individual School Improvement
  Plans
• Board and School Plans are based on the concept
  of Data-Driven Decision Making
• Resource allocation is based on all of the above

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The Data-Driven Education Universe

• Data-driven decision making is a process of
  making choices based on appropriate analysis of
  relevant information. A similar term is
  Evidence-Based Practice. It is now standard
  practice in all aspects of education. It
  involves
• Using quantitative data to diagnose a problem
• Developing a plan to deal with the problem
• Creating quantifiable targets to assess success
  in dealing with the problem
• Assessing the success of the plan/initiative by
  looking at measurable improvements
• Being accountable for the success or lack of
  success of the plan/initiative

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Intent Improving Student Achievement by
Improving Instruction and Improving Operations
and Supports

• Ideally, more access to better information
  enables educational professionals to test their
  assumptions, identify needs, and measure
  outcomes.
• Ideally, schools can use data-driven decision
  making to provide more individualized instruction
  to students, identify successful instructional
  strategies, better allocate resources, and
  communicate better with parents and the
  community.
• Ideally, it can transform teaching and
  learning through continuous improvement.

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School Improvement Planning Process

• 1. REVIEW
• Examine what makes a school effective
• Establish a school improvement team
• Review Board system plan and priorities
• Review current school plan
• Identify School Issues and Needs through use of
  quantifiable data (e.g. Demographic profile)
• Examine evidence and collect additional data
• 2. PLAN
• Develop plan in the context of Provincial
  Curriculum and Provincial Initiatives
• Develop plan in the context of Key Board System
  Priorities.
• Identify school priorities based on data
• Develop School Plan with quantifiable outcomes
  and improvement targets and indicators
• 3.IMPLEMENT and ASSESS
• Monitor implementation using indicators

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Another look at SIP Planning
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So?

• Where is the school library in this process? How
  is the library accountable for student
  achievement
• How can the school library become a central part
  of the School Improvement Plan? What needs to be
  done to make this happen?
• What kind of quantifiable data can the school
  library provide at the various stages of the
  School Improvement Process?

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So?

• What kinds of evidence, quantitative and
  qualitative, can teacher-librarians collect about
  the impact of the school library on student
  achievement?
• How can this collected evidence enable a central
  role for the school library in raising
  achievement?

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Types of Quantitative School Library Data

• Library Automation Systems provide valuable
  circulation statistics and collection data that
  can be used at the planning stage to identify
  problems and set targets.
• Library Automation Systems then can provide
  statistics to analyze progress and determine
  whether targets have been met.
• TLs can easily collect many types of quantitative
  data related to aspects of the school library
  program

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Areas of the Library Program which need to be
measured

• David Loertscher asserts that all aspects of
  library program need to be measured in order to
  boost student achievement and make the library
  central to school improvement planning
• Collaboration
• Reading
• Information Literacy
• Technology
• Loertscher provides a variety of templates in We
  Boost Achievement!

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Ross Todds Framework

• Ross Todd asserts that
• There needs to be a framework for collecting
  evidence at
• -the learner level
• -the teaching unit level
• -the organization level
• Both direct and indirect evidence and data should
  be collected

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Using Library Automation System Statistical
Reports Examples

• Total circulationmonthly and yearlywith month
  to month and year to year comparisons
• Number of books checked out per student with
  monthly and yearly comparisons
• Number of books checked out per class with
  monthly and yearly comparisons
• Number of books checked out by boys, girls with
  comparisons
• Statistics on number of books checked out by
  individual students (e.g. at-risk students) with
  monthly and yearly comparisons or comparisons
  with school average

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Using Library Automation System Statistical
Reports Examples

• Number of titles in the school library in various
  forms (books, videos, audios, kits, etc.)
• Number of books per student in the school library
  collection (can be compared with system-wide
  average or regional average)
• Number of titles for curriculum areas or special
  programs (e.g. astronomy books, books for boys,
  ESL, reading clubs)
• Age of the books in the school library collection
  (statistics can be generated by date of
  publication in various subject areas or by Dewey
  range, e.g. books on Canadian history)
• Number of new books added to the school library
  collection within a time period
• Number of books weeded

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Titlewise Collection Analysis

• Provides a quantitative analysis of the age of
  the library collection as a whole and segmented
  by Dewey ranges
• An essential tool which provides information
  which goes far beyond library automation system
  reports
• Useful for gap analysis and collection
  development planning, and budget planning as part
  of whole school plan

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Quantitative Data to Collect Some Examples

• Number of students who have taken part in reading
  clubs, independent reading programs, reading
  contests, etc.
• Number of collaborative teaching units with
  teacher-librarian and classroom teachers
• Number of lessons/projects/units that address
  specific skills related to information literacy
  (e.g. research process lessons, Internet and
  database searching, academic honesty)
• Usage by students of the school library web page
  and online information databases
• Tracking free voluntary reading

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Quantitative Data to Collect Some Examples

• Number of book talks
• Number of special events organized (e.g. Black
  History Month, Author visits, Family Reading
  Night, etc.)
• Number of students using the library to read and
  do research before and after school.
• Pre and post tests to assess student learning of
  information literacy skills and ICT skills
• Number of presentations to staff and parents
• Number of teacher requests for resources
• Number of classes booked into the library for
  various purposes
• Measuring increase in collaboration with various
  teachers

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Collecting Qualitative Data

• The most common statistics collected by
  teacher-librarians are quantifiable data such as
  the number of books circulated and the number of
  instructional sessions conducted. Such data are
  important however, they do not begin to describe
  the tangible outcomesthat are directly linked to
  local student success. In todays schools where
  accountability for student learning extends to
  the entire educational community,
  teacher-librarians must be able to provide
  qualitative evidence of student performance in
  library-led instruction.
• --Ross Todd, 2003

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Examples of Qualitative Data Direct and Indirect
Evidence

• Student, Teacher, and Parent Surveys
• Questionnaires, Checklists
• Reflections, Response Journals, Learning Logs,
  Blogs
• Samples of Student Work
• Rubrics
• Portfolios and Research Folders
• Interviews with Studentsvideo and audio
• Collaboration Templates
• Tracking change from low-level research
  assignments to assignments which stress critical
  thinking
• Annual Reports
• Student and Teacher pre and post self-assessments

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Collecting Data Action Research

• Action research is a means to more systematically
  and rigorously examine ones teaching and its
  impact on student learning.
• The current school restructuring movement has
  site-based, shared decision-making at its core.
  School teams are now accountable for their
  programs and practices.
• It is not enough for teams merely to make
  decisions. They must make decisions that are data
  driven.
• Therefore, it is necessary for them to be much
  more deliberate in documenting and evaluating
  their efforts.
• Action research is one means towards that end.
• Violet Harada 2002

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Data Driven Decision Making and Evidence based
practiceKey question for School Libraries

• Evidence based practice revolves
• around the key question What differences
• does our school library and its
• learning initiatives make to student
• learning? That is, what are the differences,
• the tangible learning benefits,
• defined and expressed in ways that
• lead a school community to say "we
• need more of this!"?
• Ross Todd

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The School Library can have a central role in the
school plan

• Literacy
• Information Literacy
• Information Technology
• Student Success (Grades 7-12)
• Facility Improvement
• Numeracy
• Safe Schools

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Resources

• David Loertscher and Ross Todd, We Boost
  Achievement! Evidence-Based Practice for School
  Library Media Specialists, 2003.
• OSLA Toolkit www.accessola/osla
• Toronto District School Board, Improving Student
  Achievement _at_your library A School Library
  Handbook for Administrators, 2004.