1. Data – Driven Instruction Presented by: Elyse Pernick Patricia Hutton http://cmsce.rutgers.edu

3. It used to be that schools talked first about children. Now they talk about test scores and data. “ Data Driven” is the latest buzzword sweeping the educational industry. “ One strategy for all” does not work in Data-Driven environments. The teacher must be able to multitask within the classroom environment and still keep discipline.

5. What happens if the Data takes control of the teacher? Teaching to the test… How can we avoid these concerns turning into problems? If we can avoid these dangers, there are solid advantages to Data Driven Instruction.

9. Data-driven decision-making is about getting better information into the hands of classroom instructors. Data-driven teachers understand the importance of utilizing multiple measures, and multiple indicators within measures, when assessing school and student success (Bernhardt, 2004). Once classroom teachers have good baseline information, they should select key indicators of success for their classrooms that are Specific, Measurable, & Attainable. Data analysis is meaningless if it does not result in meaningful instructional change. Data-driven educators are able to use summative and formative assessment data together to implement strategic, targeted, focused instructional interventions to improve student learning.

11. Confucius noted that a journey of a thousand miles starts with a single step. While teachers may not be able to address the often-overwhelming problem of low student achievement all at once, they can take small steps that together add up to big improvements over time. Once classroom teachers have access to good baseline information, they should select key indicators of success for their classrooms. With Data Driven decisions we are able to accurately point out problems; identify students needing interventions and find solutions. We are also able to make decisions in mid-course to continually improve the academic success of our students.

15. Grade: 6 The Assignment: The assignment was an end-of-the chapter test on decimals. Maximum score on the test was 50. Students were required to: Write numbers as decimals Estimate answers Solve problems and equations Graph solutions and equations Evaluate equations Construct tables Scoring Guide: Raw Score Percentage Letter Grade 48-50 96 A+ 46-47 92 A 45 90 A- 43-44 86 B+ 41-42 82 B 40 80 B- 38-39 76 C+ 36-37 72 C 35 70 C- 33-34 66 D+ 31-32 62 D 30 60 D- 0-29 58 F Example Classroom Data Analysis

16. Raw % Letter Name Gender Score Correct Grade 1. Andres M 47 94% A 2. Arlene F 38 76 C+ 3. Bryan M 44 88 B+ 4. Carlos M 44 88 B+ 5. Cesar M 45 90 A- 6. Daniel M 48 96 A+ 7. Dustin M 48 96 A+ 8. Edith F 44 88 B+ 9. Eunice F 31 62 D- 10. Gustavo M 42 84 B 11. Jessica F 34 68 D+ 12. Joshua M 45 90 A- 13. Juliana F 42 84 B 14. Laau F 41 82 B 15. Linh F 40 80 B- 16. Lucera F 44 88 B+ 17. Marisol F 46 92 A 18. Mikaele M 40 80 B- 19. Nayeli F 47 94 A 20. Neil M 40 80 B- How can these data be organized to inform instructional decision-making?

17. Implications for Instructional Decision-Making What did you learn about the students’ performance on this test from organizing these data in a user-friendly format? 1. 2. 3. 4. 5. How might these data be used for instructional decision-making? 1. 2. 3. 4. 5.

22. Three Ways to Think About Organizing Data To promote the skillful use of classroom data for instructional decision-making, teachers, and administrators can organize data by the: 1. Distribution of Scores ... ...which answers the questions related to “how many?” 2. Distribution of Students ... ...which answers the questions related to “who?” 3. Patterns in Student Work... ...which answer the questions related to “what?”

23. What Do I Do for the Students Who Don’t Get It? 1. Re-do? 2. Review? 3. Re-teach?