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AERA Conference

AERA Conference. April, 2005 “Using Data for Supporting Improved Instruction and Student Learning” Pat Roschewski, Director Statewide Assessment Nebraska Dept. of Education proschew@nde.state.ne.us. CLASSROOMS. Nebraska’s STARS - School-based Teacher-led Assessment and Reporting System.

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AERA Conference

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  1. AERA Conference April, 2005 “Using Data for Supporting Improved Instruction and Student Learning” Pat Roschewski, Director Statewide Assessment Nebraska Dept. of Education proschew@nde.state.ne.us

  2. CLASSROOMS Nebraska’s STARS - School-based Teacher-led Assessment and Reporting System “Decisions about student learning should occur where the learning takes place – in the classroom.” Doug Christensen, Nebr. Commissioner of Education

  3. A Balanced Assessment System Locally developed Assessments in Reading Mathematics Science Social Studies Statewide Writing Assessment Alternate Assessment _ SPED Language Acquisiton NAEP – NRT results - ACT

  4. Primary Purpose of STARS? SCHOOL IMPROVEMENT • Every educator knows learning targets (standards). • Every educator appropriately and accurately measures the targets (assessment). • Every educator uses data for improved instruction and student learning. (accountability)

  5. Two Nebraska Case Studies • Customized Data Sessions • Inexperienced Using Data • Desire to learn data analyses and involve all staff

  6. District Two • 60 staff members • 3 administrators • 850 students • Homogeneous Population • 30% SES • 98% Caucasian • 15% Mobility • 13% SPED • District One • 230 staff members • 17 administrators • 3,500 students • Diverse by Nebr. standards • 40% SES • 55% Hispanic • 25% mobility • 17% SPED

  7. METHODOLOGY Step 1: NDE staff customized each district’s data • District One • had been collecting large amounts of data • “granular” – no big picture • huge notebook • District Two • didn’t have any data

  8. Step 2: NDE staff on-site and taught: • Strategic planning for grouping staff • Formats/purposes of data displays • Embedded assessment literacy – assessment types/purposes

  9. DATA ANALYSIS • What does the data tell us? (factual) • What might this mean? (hypothesis) • What should we do about it? (action)

  10. Conclusions: Commonalities Across Districtsregardless of size, demographics • Neither administrators nor teachers knew these steps • Assessment literacy was lacking • “Hungry” for data analysis skill • Willingness to learn and act • Both districts have extended this K-12

  11. RECOMMENDATIONS • Statewide professional development in data analysis • Statewide professional development in assessment literacy • Improved teacher and administrator preparation • Statewide model of analysis formats tied to instruction • Data support teams

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