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Data Driven Instruction

EDA 557 Presentation

Jordan Tezanos

on 10 July 2017

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Transcript of Data Driven Instruction

Understanding Data Driven Instruction
Data-driven instruction is the philosophy that schools should constantly focus on one simple question: are our students learning? Using data-based methods, schools break from the traditional emphasis on what teachers ostensibly taught in favor of a clear-eyed, fact based focus on what students actually learned.
Hudson Falls Central School District
Assessment: Create rigorous interim assessments that provide meaningful data.
Culture: Create an environment in which data-driven instruction can survive and thrive.
Analysis: Examine the results of assessments to identify the causes of both strengths and shortcomings.
Action: Teach effectively what students most need to learn.
The 8 Mistakes That Matter
1) Inferior interim assessments
2) Secretive interim assessments
3) Infrequent assessments
4) Curriculum-assessment disconnect
5) Delayed results
6) Separation of teaching and analysis
7) Ineffective follow-up
8) Not making time for data
Results from previous of NYS ELA assessments.
Item Analysis of questions.
Sources of Data
Every teacher has access to or can build their own collection of data. It is extremely important that you develop, over time, a system to manage data collection. We must constantly be evaluating whether or not our students are obtaining the skills and concepts we want them to. Furthermore, we must be able to prove that through the use of data. Aside from the data we can collect throughout the year, there may also be some data that the state can provide.
Using Data to Improve Student Achievement
Through the use of our pre, interim and post assessments, we will have real time data on each student, which will serve as our instructional road map. The data will be shared with the entire faculty. The principal, department chairs, teachers and guidance counselors will all be aware of what our students are struggling with and excelling in. Every teacher in the building is going to be charged with the task of improving areas of skill deficiencies, from the physical education teacher, to the skills teacher to the science teacher.
Sources of Data

Results from pre-assessment given at the beginning of the year.
Interim assessment results.
Results from post-assessment give prior to the state exam in April
How to Generate Classroom Data
Observe the following presentation by Hans Rosling, a Swedish professor of Global Health. Note the number of data points used to develop the graph.
Individually, each data point has limited value. Take together, they tell an important story.
Educational data tells a story about the district, building, classroom, students and sometimes the community.
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