1 / 36

Data Driven Instructional Leadership

Data Driven Instructional Leadership. Region 18 – Leadership Development. Overview. Told Constantly Common sense Data rich but information poor. Making Sense Out of Chaos. What data? How and When? How and When should it affect programs? How and when will it affect the classroom?

adsila
Télécharger la présentation

Data Driven Instructional Leadership

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Driven Instructional Leadership Region 18 – Leadership Development

  2. Overview • Told Constantly • Common sense • Data rich but information poor

  3. Making Sense Out of Chaos • What data? • How and When? • How and When should it affect programs? • How and when will it affect the classroom? • How will we make time? • How does it affect goal setting?

  4. Data Driven Instruction System • It cannot be haphazard • Designing and Implementing data can be the catalyst for school improvement • Not as difficult as it sounds if carefully planned and implementation is clearly mapped out

  5. DDIS Model

  6. Data Collection Most critical aspect Constantly changing Backbone of DDIS

  7. Data Reflection • Student needs must drive staff development

  8. Data Translation • Includes curriculum mapping • Content adjustments • Instructional adjustments • Starts with curriculum experts – Identifying the gaps • Teaching staff involved in solution

  9. Data Driven Instruction Design • Includes lesson design and differentiation • The tiered lesson plan model • Analyze data about student learning • Design lessons to meet each students needs • Teach the lesson to multiple levels of students at once • Teachers do not innately know how to do this • We must get them training

  10. Design Feedback Provide information back to stakeholders Regarding the design and implementation of the school improvement activities

  11. Summative and Formative Feedback • Conducted to determine success so far • Teachers have got to stop seeing assessment as summative • Assessment should be used to inform teachers about the gaps in the learning progress of students

  12. Goal Setting and Getting Started Fluid Process

  13. II. Data Collection Making sense of the data is critical

  14. Who, What, Where, When, Why, and How Use multiple measures of data

  15. Demographic Data • Gender • Title I Status • Race • Socio-Economic • ELL Status • Discipline • Attendance • Health Status • Enrichment programs • Remediation Programs • Special Pops

  16. Student Learning Data • Local Assessment • State Assessment • Classroom • Lexile levels

  17. School Process Data • Finance • Transportation • Bell Schedules • Tutorials • Key is to determine how this information relates to student learning

  18. Perceptions Data • How do students view things? • How do parents view things? • What is the school known for? • Typically overlooked • Not easily collected

  19. The Who, When, and How of Data Collection • Someone needs to be in charge • Data is collected by many • Establish a data discovery team • Team’s purpose is to identify what data is important • Compile a list • Everyone must understand their role and why it is critical

  20. District manager makes sure everything runs smoothly • Create a data entry protocol • No shortcuts • Who is going to check for accuracy • School must track the staff development teachers attend • Have the students been successful • Cost effective • If students are not improving then what adjustments must be made • Where to store the data? • Must be assessable and Robust • Must be able to drill down from several angles • The district cannot be nonchalant about data collection • District must be willing to make long term commitment to data process • Do not assume that data gathering is easy just because it is first

  21. III. Data Reflection • Teachers must be given time to talk

  22. When and When to Find Time

  23. District Data Retreat Workshop

  24. Professional Learning Time • Formal and Informal

  25. Data Translation • Is systemic and takes time • Involves analyzing and interesting data • Teachers and administrators must use their own data in order to know what changes must be made • School district must support the process with resources • Focus on one improvement

  26. V. Data-Driven Instructional Design Goal of entire program

  27. How and When • Get data into the hands of teachers • Ownership is the key • Takes time and training • Once teachers understand what is behind it they will embrace it • A data driven culture will develop • Constant follow-up and focus are needed • Changes are made based on data that was reviewed and translated into curriculum and classroom instruction design

  28. VI. Design Feedback • How is data assessed and how is this communicated to stakeholders

  29. How is DDIS Adjusted and Evaluated • SMART goals • Specific • Measured • Attainable • Results oriented • Time bound

  30. Adjustment

  31. Evaluation

  32. VII. Summative and Formative Assessment • We must change the paradigm

  33. VIII. Summary and Results: Is It Worth It? • Scores will increase • Innovative programs will be developed • ESL • Literacy • Gives you the framework for the improvement process

  34. How Do I Get Started? Put someone in charge of the process

  35. Four Keys • Purpose • Focus • Communication and coordination • Follow Through

  36. Important • Limit the focus to one or two things • If teachers are asked to do something they must understand why they are doing it

More Related