2.  From Webster’s Dictionary, data is defined as “ factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation”  Data can be collected in many different ways. Some examples include tables, charts, graphs, or anecdotal observations.

3.  Educational data include (but not limited to) › Student achievement data-observational notes, samples of classwork, student portfolios, results of formal and informal classroom assessment, report cards, or large-scale assessment results. › Other student data- student mobility, attendance, behavioral, homework completion. › Contextual data- not under the control of the teacher (background, linguistics, socio- economic factors).

4.  1. Determine what you want to know  2. Collect or access data  3. Analyze results  4. Set priorities and goals  5. Develop strategies  These steps will help in the effectiveness of instruction.

5.  Data can be collected in our classrooms.  It is very important to analyze data collected and use it in instruction.  Some of our classroom data assessment includes DIBELS, Running Records, Quarterly Test Scores, Observational Records, and many more.

6.  DIBELS Overview:  The Dynamic Indicators of Basic Early Literacy Skills (DIBELS) are a set of procedures and measures for assessing the acquisition of early literacy skills from kindergarten through sixth grade. They are designed to be short (one minute) fluency measures used to regularly monitor the development of early literacy and early reading skills. http://dibels.org/dibels.html http://dibels.org/dibels.html  DIBELS scores can be looked at an assessed for instruction.  Grouping students on scores is a good way to create interventions in the classroom.  A data collection table can be created to help with showing where students struggle with different phonemic skills.

7. LNF PSF NWF - CLS NWF - WWR DORF Middle of Year BBBMEBME Words Correct Errors Accuracy Retell 0-24 AR0-17 AR0-32 AR 58 AR 0 AR 0 - 2 AR 0-5 AR Please enter the median score Automatically calcuated Please enter the median score only,NOT the percent Last, First 25-39 SR18-26 SR33-42 SR47-57 SR 3-7 SR 6-12 SR 40+ LR27+ LR43+ LR50+ LR 1+ SR OR LR 8+ LR 13+ LR

8.  Look at the test results and group students based on their needs.  Create authentic activities that can help strengthen weaker skills.  Monitor progress with these students by collecting work samples, retesting students on different subtests.  Fill out the activity progress chart

9. Name of StudentStrengthsWeaknesses Activity Progress Chart Names of Students: Week of: _____________________ Skill: Brief description of activity: Resources Used:

10. StudentNonsense Word Fluency Score Benchmark Score Observations Ryan1127+Very quiet, non fluent, not confident in letter sounds Morgyn1227+Very distracted, non-fluent speed Antonio1627+Unsure of letter sounds, easily distracted Nico727+Non-fluent speed, need reassurance frequently

11.  Example: › I know that from the data collected from this one subtest that these students need more fluency with their letter sounds. › I would find and activity that worked with segmenting letter sounds to make CVC words. › See lesson plan for “Tap it, Map it, Zap it” handout. › I can follow my lesson by filling out the strengths and weaknesses part of my student activity sheet.

12.  Bambrick-Santoyo, P. (2007). Data in the Driver's Seat. Educational Leadership, 65(4), 43-46. Retrieved from Cook Library.  Buzzeo, T. (2010). STRENGTH in Numbers. School Library Journal, 56(10), 38-40. Retrieved from Cook Library.  Gersten R, Keating T. The burden of proof: Validity as improvement of instructional practice. Exceptional Children [serial online]. May 1995;61(6):510-519. Available from: Academic Search Premier, Ipswich, MA. Accessed Cook Library.  Kiley, T. J., & Jensen, R. A. (2007). Reading First: A Catalyst for Change. Illinois Reading Council Journal, 35(1), 48-51.  Lewis, D., Madison-Harris, R, Muoneke, A., Times, C. Using Data to Guide Instruction and Improve Student Learning. SEDL Letter Volume XXII, Number 2.  Marshall, Kathie (2009). What Data-Driven Instruction Should Really Look Like. Teacher Magazine.  Noyce, P., Perda, D., & Traver, R. (2000). Creating Data-Driven Schools. Educational Leadership, 57(5), 52.  Rudy, D. W., & Conrad, W. H. (2004). Breaking Down the Data. American School Board Journal, 191(2), 39-41. Retrieved from Cook Library.  Noyce, P., Perda, D., & Traver, R. (2000). Creating Data-Driven Schools. Educational Leadership, 57(5), 52.  Using Data to Drive Instructional Decision: A Four Step Student Success Model. (2008). Retrieved March 21, 2001, from http://www.winsorlearning.com  Van Barneveld, Dr. Christina. (2008). Using Data to Improve Student Achievement. The Literacy and Numeracy Secretariat. Accessed October 11, 2011.