Data Driven Instruction

What is data-driven decision making? DDDM is a system of teaching and management practices that gets better information about students into the hands of their classroom teachers.

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DDDM requires a shift in paradigm for teachers from the day-to-day instruction that focuses on process and delivery to a pedagogy that is results oriented.

DDDM is about getting better, more specific information to the teachers to drive more effective instruction in the classroom.

Five major components of data driven instruction are a good baseline, measurable instructional goals, frequent formative assessments, professional learning communities, and focused instructional interventions.

Educators can use baseline data and identify and set mastery levels and learning needs of the class, subgroups, and individual students.

Data driven educators use the information to set measurable instructional goals that serve as academic targets to guide classroom instruction. The goals are called SMART goals - Specific, Measurable, Attainable, results-Oriented, and Time-Bound.

An example: The percentage of third grade students scoring at grade level or higher on the state mathematics test will increase from 64% in Spring 2004 to 82% in Spring 2005.

Areas of Improvement:
Number Sense
Computation
Measurement

Six elements that are necessary:
Measurable baseline (64%)
Measurable goal (82%)
Specific time frame (Spring 2004-Spring 2005)
Specificity about what will be assessed (3rd graders scoring at grade level or higher)
Specificity about the method of assessment (state math test)
Focus areas that will guide future instruction to reach goal (number sense, computation, and measurement)

This type of instruction can be used in the classroom, by a tutor, by a parent or professional educator. DDDM is effective and delivers results if properly implemented.

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