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Data analysis 2011

Data analysis 2011



This presentation is based on research regarding best practices for data analysis. The sources are listed on the last slide.

This presentation is based on research regarding best practices for data analysis. The sources are listed on the last slide.



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    Data analysis 2011 Data analysis 2011 Presentation Transcript

    • Putting Data Analysis to Work
      Using data analysis to answer the questions, “What do the data tell us about our students’ learning and what do we do next?”
      Presented by: Ginny Huckaba
    • NORMS
      • Be timely, present and participatory
      • Phones on silent
      • Minimize bird walking (M. Hunter)
      • Return from break
    • Goals
      At the end of this session, participants will:
      Have knowledge of the process of data analysis
      Be able to use HIVE site to analyze student performance data
      Have templates to use to perform item trend analyses
      Be able to serve as a resource of information to other educators
      Have developed a plan for taking the knowledge back to colleagues
    • AGENDA
      Welcome, Introduction, Goals
      Group dynamics
      What do you already know?
      What do you want to know (goals)?
      Using Data to Enhance & Improve Student Learning
      Item & Trend Analysis
      Data analysis-needs assessment
      Planning for Back-home Colleagues/PD
    • What is Data Analysis?
      • The breaking (“unwrapping” per Ainsworth) of a whole into its parts and looking for relationships and functions. In the educational setting, it provides clarity for what students must know and be able to do.
      • It is NOT data disaggregation (that stops at the breaking-down stage)
      • Analyzing data requires:
      • looking at data closely and objectively.
      • using it to make improvements.
      • halting the gathering of it if you don’t use it!
    • Analyzing the Data
      • What is the function (the purpose) of data analysis?
      • What does “it” measure and how is that information used?
      • What is the best use for assessments? What are the relationships between interim assessment data, progress reports/report cards and criterion-referenced tests?
    • Common reasons for Data Analysis:
      • Improve student learning and achievement
      • Improve teachers’ instruction
      • Provide students with feedback on proficiency
      • Get a common understanding of exemplary performance/work and how to achieve it
      • Measuring program effectiveness
      • Rescuing kids who are falling through the cracks
      • Learn what programs are yielding desired results
      • Getting to the root cause of a problem
      • Accountability
      • Guide curricular revisions/development
    • 4 Data Lenses through which to look:
      Demographics ( sources: test scores, APSCN)
      Student learning (sources: state, school, class levels)
      School process data (sources: special programs, finance, transportation, professional learning)
      Perceptions (public/stakeholders; sources--surveys)
    • Step 1 of Data Analysis: Data Collection(Treasure Hunting)
      • Student assessment data shows what is/was—they do not necessarily tell why
      • “Why” may come from secondary data
      • Teacher training
      • Instructional strategies
      • Student demographics, norms, behaviors
      • Interventions
      • Existing support systems
    • Step 1: Data Collection
      • Ask: “Who are our kids and how are they performing on high-stakes assessments?”
      • Find out: “How do the prominent stakeholders feel about our actions/efforts?”
      • Examine: “What programs and processes are in place in our school that meet our students’ needs (or don’t meet them)?”
      • Objectively answer: “How are our students identified for extension, supplemental, and gifted-student programs?”
    • 2: Data Analysis(Reflection)
      • Most critical; most complex to organize
      • Student achievement drives change (avoid getting PD cart before the horse)
      • Don’t implement what kids don’t need
      • Look first at the data, then decide on the PD
      • Focus on use of data at the classroom level
      • To be effective, teachers must be provided:
      • Tools
      • Time
      • Leadership’s commitment
    • 2: Data Analysis(Reflection)
      • What did we discover from the data:
      • Strengths & Success (celebrate)?
      • Challenges (to be met)?
      • Trends (across subject/grade levels)?
    • 3: Set Data-based Priorities(Translation—or “triage!”)
      • Careful scrutiny of each component:
      • What has to be done
      • External factors
      • “Do now” (meet AYP)
      • Rank Ordering (determining most pressing)
      • Based on areas of greatest need
      • Based on existing capacity
    • Set Data-based Priorities(Translation)
      • Moves the data to the instructional level
      • Requires change at the systems level
      • Decisions grounded in:
      • Analyzing test data
      • Implementing measurable changes
      • Studying change data
      • Making decisions from resulting data
      • Evidence: visible changes within schools
    • Set Data-based Priorities (Translation)
      • Components (partial listing):
      • Curriculum mapping
      • Content adjustments
      • Instructional adjustments
      • Data-driven instructional design, feedback
      • Formative and summative assessments
      • Goal setting
    • 4: Goals
      • Goals must be:
      • Explicit
      • Focused
      • Make sure all goal statements are designed to be:
      • Specific
      • Measurable
      • Achievable
      • Relevant
      • Timely
    • Goals—three essentials
      • Setting
      • Reviewing
      • Revising
    • 5: Instructional Design
      • Is powerful & focused
      • Requires: teacher training and consistent practice in classroom instruction
      • Enhances student achievement
      • Is composed of Instructional Strategies
      • Is NOT: activities, programs, adopted textbooks
    • 6: Feedback(Interim measures)
      • Formative (formal and teachermade) and summative assessments (and teachers’ use of them)
      • Use to determine:
      • Proper implementation of instructional design (strategies)
      • Effectiveness (intended effect on student performance taking place)
      • “Is it worth it?”
    • 7: Action Plan(It’s Quality, not quantity, that matters)
      • Quality Action Plan Must Haves:
      • Explicitly communicated to staff, parents
      • Administrative backing of implementation and sustainability
      • Leadership follow-up
      • Relentless efforts to maintain focus on the data
    • The Process, in a Nutshell:
      • Build the team, then get started
      • Identify the Problem (key indicators for student success/failure)
      • Hypothesize and articulate hunches
      • Identify the data to be examined and gather it
      • Analyze the data (strengths/weaknesses/trends/challenges)
      • Begin developing an Action Plan by:
      • Setting Goals for the plan and implement the plan
      • Evaluate using interim assessments/measures
      • Make decisions for improvement based on evaluation results
      • Put Action Plan to work
      • Start the process all over again—data analysis IS a cycle, NOT a checklist!
    • Building your Team
      • Collaboratively decide:
      • Who is going to do what work and when?
      • Team roles (not limited to):
      • Lead project
      • Input data
      • Produce reports
      • Maintain data “warehouse”
      • Report the results of analysis
    • Questions to Ponder:
      • Is time set aside to reflect on actual student work?
      • Is there a process in place whereby reflections and insights are used to make modifications in instructional/school practices?
      • Is there a habit of taking action as a result of patterns/trends that come out of the data?
      • Are there redundant or ineffective practices that need to be eliminated?
      • Is data collection followed by their analyses and changes/improvements?
      • Is the feedback cycle used for continuous improvement of instruction? (evaluation, decisions made based on evaluation results , actions taken because of those decisions, evaluation of resulting actions, etc.)
    • Resources:
      • Bernhardt, V.L. (2004), Data Analysis for Continuous School Improvement. Larchmont, NY: Eye on Education, Inc.
      • Blink, R.J. (2007), Data-Driven Instructional Leadership. Larchmont, NY: Eye on Education, Inc.
      • Love, N. editor (2009), Using Data to Improving Learning for All. Thousands Oaks, CA: Corwin Press
      • White, S.H. (2005), Beyond the Numbers. Englewood, CO: Advanced Learning Press
      • White, S.H. (2005), Show Me the Proof. Englewood, CO: Advanced Learning Press