What Does “Data Training” Look Like?. What the Research Says Jimerson and Wayman (2011) –...

What Does “Data Training” Look Like?

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What the Research Says

• Jimerson and Wayman (2011)– Little research about best ways to provide

effective data-related professional learning

– Implied constellation of knowledge and skills in admonition to use data to inform instruction

What the Research Says

• Jimerson and Wayman (2011)– Types of data and their interactions– Triangulation of data– Basic data concepts

– Best learned by examining data related to important questions• Overcomes worry about what should we do with all this data

– Confidence that repeated practice will lead to understanding• Desire “cheat sheets” and templates

What the Research Says

• US Department of Education (2011)1. Data Location2. Data Comprehension3. Data Interpretation4. Data Use in Instructional Planning5. Question Posing

What the Research Says

• US Department of Education (2011)1. Data Location

• Find information in a table or graph to answer a question

2. Data Comprehension• Manipulate data to answer a question• Translate numbers to a verbal statement

What the Research Says

• US Department of Education (2011)3. Data Interpretation

• Difference between cross section and longitudinal data

• Difference between status, improvement, and growth

• Impact of “N” size• Impact of “outliers”• Implications of “measurement error”• Importance of subgroups

What the Research Says

• US Department of Education (2011)3. Data Interpretation

• Difference between mean, proportion, and range• Difference between a continuous data graph and a

categorical data graph• Difference between a percentage and a percentile• Categorical prediction based on population• Role of multiple measures (“triangulation”)• Validity, reliability, and fidelity• Use of standard score to determine degree of

growth

What the Research Says

• US Department of Education (2011)4. Data Use in Instruction

• Item analysis• Disaggregation by skill clusters• Implications for differentiated instruction• Implications for reteaching• Implications for interventions

5. Question Posing• Questions that data can vs. can’t answer• Questions that available data can answer

Resources

• Dan Venables– The Practice of Authentic PLCs– How Teachers Can Turn Data into Action– Data Action Model

1. Ask questions of the data2. Obtain additional data to triangulate3. Identify learner gaps and instructional gaps4. Set an improvement goal5. Research and commit to improvement strategies6. Implement strategies7. Evaluate fidelity and impact