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Presentation Objectives

Data-Based Decision Making D -riving Force for instruction A - nalyzing for better outcomes T - eacher training and ownership A - chievement for all stakeholders Presented by: Norris Evans Shenoah Howard Robbie Garnes Kristina Truell. Presentation Objectives.

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Presentation Objectives

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  1. Data-Based Decision MakingD-riving Force for instructionA-nalyzing for better outcomesT-eacher training and ownershipA-chievement for all stakeholdersPresented by:Norris Evans Shenoah Howard Robbie Garnes Kristina Truell

  2. Presentation Objectives • To understand the benefits of data driven instruction • To learn the key steps of successful data use • To implement data strategies to be used daily

  3. Changing Habits If you keep doing what you’re doing, you’ll keep getting what what you’ve got. - Albert Einstein

  4. Data-Based Decision Making • Data-Based Decision Making is a school improvement approach that uses quantitative data to help describe or define problems, direct activities, target interventions, and allocate resources. (asca.membershipsoftware.org)

  5. Entertaining Video about Data Driven Instruction http://www.youtube.com/watch?v=Mf56qEGmIsI

  6. What are the benefits of understanding and using data? • Develops a system that is unified, integrated, and, meaningful • Helps us to more accurately and efficiently choose interventions and determine if they are working • Builds a culture of inquiry and continuous improvement

  7. Benefits of the Data Graph A Data Graph: • can be used to show a clear visual picture of student performance. • can be used to show areas of growth, areas of weakness, and can also serve as a guide for departments when developing lesson plans to meet students’ needs.

  8. Differential Population

  9. Expectations for Data Use • Use daily to improve instruction • Use daily to improve student performance • Use daily to improve ownership of the teaching process • Use daily to improve ownership of the learning process

  10. Conditions that Facilitate the Growth Process • Collaborative Culture – Wide range and diversity of perspectives with colleagues who value data • Collaborative Structures – Data teams with scheduled meeting times and officially sanctioned opportunities • Access to useful data – pulling data from multiple sources in a user-friendly way • Widespread data literacy – To make sense of the data and to develop measurable goals

  11. Sources & Resources • Staff Development • Data Room • Data planning Wednesday • Data teams based on grade level

  12. Data Teams • Teams will be based on grade level. • Teams will also be given time to develop strategies that address low performing students in a cross- curricular format with incentives to increase student achievement.

  13. Implementation • August- Analyze Previous year’s scores • September –Course Pre-test • October- Data driven instruction • November-Data driven instruction cont. • December-Data driven instruction cont. • January – Mid Term • February – Data driven SOL prep • March – Data driven SOL prep cont. • April – Data driven SOL prep cont. • May – CoursePost-Test

  14. Previous Year’s Scores • Core Teachers will receive scores relevant to their content area. • Elective/HPE Teachers will receive reading scores. (Reading in the content area will be addressed in these classes.)

  15. Intervention • Teachers should show evidence of Intervention by giving students pre-tests and post-tests.

  16. Pre- Test • Each teacher will give a pre-test before learning begins; this will allow teachers the ability to identify areas to focus on during instruction.

  17. Post Test • The teacher will give a post-test to determine if students have increased their knowledge and/or mastered particular concepts that were taught throughout the school year.

  18. Example of Data gathering tool Pre and Post Test

  19. Data Observation DATA USE OBSERVATION: Purpose: To evaluate the use of data to improve instruction The teacher: 1 2 3 4 ☐ Analyzes data and revises lesson plans. ☐ Uses data to differentiate instruction. ☐ Uses data to re-focus student attention. ☐ Collaborates with grade level. ☐ Shows others how to use data. • Evidence will be data collection tools ( informal and formal assessments) • Teachers will be rated on a scale of 1 – 4 of which 4 is the highest rating.

  20. Conclusion Data are important tools that all stakeholders should fully understand, evaluate, and use daily to drive instruction to increase student achievement.

  21. References Harringtion, K. & Gray, K. Creating successful creating successful elementary schools data teams (2012). Retrieved from asca.membershipsoftware.org/files/datadecisionteams.pdf

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