Data Driven Instructional Leadership
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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?

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Data Driven Instructional Leadership

Region 18 – Leadership Development


Overview
Overview

  • Told Constantly

  • Common sense

  • Data rich but information poor


Making sense out of chaos
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?


Data driven instruction system
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



Data collection
Data Collection

Most critical aspect

Constantly changing

Backbone of DDIS


Data reflection
Data Reflection

  • Student needs must drive staff development


Data translation
Data Translation

  • Includes curriculum mapping

  • Content adjustments

  • Instructional adjustments

  • Starts with curriculum experts – Identifying the gaps

  • Teaching staff involved in solution


Data driven instruction design
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


Design feedback
Design Feedback

Provide information back to stakeholders

Regarding the design and implementation of the school improvement activities


Summative and formative feedback
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



Ii data collection
II. Data Collection

Making sense of the data is critical


Who what where when why and how
Who, What, Where, When, Why, and How

Use multiple measures of data


Demographic data
Demographic Data

  • Gender

  • Title I Status

  • Race

  • Socio-Economic

  • ELL Status

  • Discipline

  • Attendance

  • Health Status

  • Enrichment programs

  • Remediation Programs

  • Special Pops


Student learning data
Student Learning Data

  • Local Assessment

  • State Assessment

  • Classroom

  • Lexile levels


School process data
School Process Data

  • Finance

  • Transportation

  • Bell Schedules

  • Tutorials

  • Key is to determine how this information relates to student learning


Perceptions data
Perceptions Data

  • How do students view things?

  • How do parents view things?

  • What is the school known for?

  • Typically overlooked

  • Not easily collected


The who when and how of data collection
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


  • 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


Iii data reflection
III. Data Reflection

  • Teachers must be given time to talk




Professional learning time
Professional Learning Time

  • Formal and Informal


Data translation1
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


V data driven instructional design
V. Data-Driven Instructional Design

Goal of entire program


How and when
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


Vi design feedback
VI. Design Feedback

  • How is data assessed and how is this communicated to stakeholders


How is ddis adjusted and evaluated
How is DDIS Adjusted and Evaluated

  • SMART goals

  • Specific

  • Measured

  • Attainable

  • Results oriented

  • Time bound




Vii summative and formative assessment
VII. Summative and Formative Assessment

  • We must change the paradigm


Viii summary and results is it worth it
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


How do i get started
How Do I Get Started? Worth It?

Put someone in charge of the process


Four keys
Four Keys Worth It?

  • Purpose

  • Focus

  • Communication and coordination

  • Follow Through


Important
Important Worth It?

  • Limit the focus to one or two things

  • If teachers are asked to do something they must understand why they are doing it


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