Data Informed Decisions

Data Informed Decisions

By Mollie Blake

Table of Contents

• What is DID?• Data to Inform Instruction• Learning is the Focus• Data Teams• Smart Goals• RTI• Conclusion

What is DID (Data Informed Decision-Making)?

• The collection and analysis of data in education.

• It guides decisions of teachers in the classroom.

• The intended result is to improve student success.

A unique look at why we need Data to inform instruction

• Education a unique perspective

• In this video Ken Robinson compares the importance of data to inform instruction to a doctor knowing their patient.

• Teachers must know their students like a doctor knows their patients.

Teachers put learning as the focus

• Data Informed decision making pinpoints what the student needs to know according to the grade level standards.

• Teachers across the United States have improved their instruction by focusing on the data in PLCs or Data Teams.

Data Teams

• Data Teams

Setting Smart Goals

• Specific• Measurable• Attainable• Realistic• Timely

Benefits of Smart Goals

• Set priorities

• Increase motivation

• Measure progress

RTI

• Response to Intervention (RTI) is a multi-tier approach to the early identification and support of students with learning and behavior needs.

• System of instruction based on data• High quality instruction • Monitor closely• Educational decisions based on individual

students

RTI

• Response to Intervention

Conclusions

• Data-informed decision making is not a simple intervention because it involves so many aspects of education (e.g., assessment, curriculum, accountability, information technology) and it requires fundamental improvements in the degree of mutual trust and changes in the way teacher time is used.

References

• Armstrong, J., & Anthes, K. (2001). How data can help. American School Board Journal 188(11),• 38–41.Chrispeels, J. H. (1992). Purposeful restructuring: Creating a climate of learning and achievement in• elementary schools. London: Falmer.• B Means, C Padilla, A DeBarger, M Bakia - 2009 – Implementing data-informed decision making in schools: Teacher access, supports and use. Washington, DC:U.S. Department of

Education, Office of Planning, Evaluation and Policy Development. Retrieved April 19, 2011, from http://eric.ed.gov/PDFS/ED504191.pdf• • Chrispeels, J. H., Brown, J. H., & Castillo, S. (2000). School leadership teams: Factors that influence• their development and effectiveness. Advances in Research and Theories of School Management and• Educational Policy, 4, 39–73.• Ingram, D., Louis, K. S, & Schroeder, R. G. (2004). Accountability policies and teacher decision making:• Barriers to the use of data to improve practice. Teachers College Record, 106, 1258–1287.• Johnson, R. (2002). Using data to close the achievement gap: How to measure equity in our schools (1st• ed.). Thousand Oaks, CA: Corwin.• Massell, D. (2001). The theory and practice of using data to build capacity: State and local strategies• and their effects. In S. H. Fuhrman (Ed.), From the capitol to the classroom: Standards-based reform• in the states (pp. 148–169). Chicago: University of Chicago Press.• Nichols, B. W., & Singer, K. P. (2000). Developing data mentors. Educational Leadership, 57(5),• 34–37.• Stringfield, S., Reynolds, D., & Schaffer, E. (2001, January). Fifth-year results from the High Reliability• Schools project. Symposium presented at the meeting of the International Congress for School Effectiveness• and Improvement, Toronto, Canada.• Schmoker, M. (2004). Tipping point: From feckless reform to substantive instructional improvement.• Phi Delta Kappan, 85, 424–432.• Symonds, K. W. (2003). After the test: How schools are using data to close the achievement gap. San• Francisco: Bay Area School Reform Collaborative.• Wayman, J. C., Stringfield, S., & Yakimowski, M. (2004). Software enabling school improvement• through the analysis of student data (Report No. 67). Retrieved December 14, 2004, from Center for• Social Organization of Schools Web site: http://www.csos.jhu.edu/crespar/techReports/Report• 67.pdf• Zhao, Y., & Frank, K. A. (2003). Factors affecting technology users in schools: An ecological perspective.• American Educational Research Journal, 40, 807–840.

References • • • http://en.wikipedia.org/wiki/Data-informed_decision-making• • Education a unique perspective• • Data Teams• • http://www.rtinetwork.org/learn/what/whatisrti• • https://www.google.com/search?sa=G&q=teacher+and+the+student&tbm=isch&tbs=simg:CAQSZRpjCxCo1NgEGgIIPQwLELCMpwgaPAo6CAE

SFJkFmAWXBZoFsgSbBbYEvATBBJwFGiDTuH-2mmHzVdVfaLaYVpmEw9i69pqls4q5TSpaealrfwwLEI6u_1ggaCgoICAESBJTaunUM&ei=4VamUq-5HYipqwGFnIGQCQ&ved=0CCcQwg4oAA&biw=800&bih=423 image on front page

• • http://www.clrn.org/elar/Images/image006.gif image on slide 2• • • http://rt3region4.ncdpi.wikispaces.net/file/view/Data%20Team%20Process.png/407832490/Data%20Team%20Process.png image on slide 4• • http://2.bp.blogspot.com/-M_8fbQqP3Hc/UA_aPfzooLI/AAAAAAAAA9k/mPrBAvMOSac/s1600/learning+steps.jpg image on slide 5•

http://static8.depositphotos.com/1005979/855/i/450/depositphotos_8553809-Learn-Word-on-Ruler-Measure-Education-Progress-Growth.jpg image on slide 8

• http://www.aleks.com/k12/RtI-Tiers-v3.jpg image on slide 9