1. Utilizing Data to Facilitate Academic Growth in Middle School
Presented by: Joseph R. Villarreal – Principal Resaca Middle School
Los Fresnos, TX

2. RESACA MIDDLE SCHOOL DEMOGRAPHICS
Located in Los Fresnos in the Rio Grande Valley
Population – 872 students (6-8)
African American - .3%
Hispanic – 95%
White – 5%
Economically Disadvantaged – 78%
Advanced Academics – at least 50% of all students are enrolled in at least one Pre-AP class
Accelerated Reader is a curriculum requirement

3. STATE ASSESSMENT DATA
YEAR
MATH
READING
SCIENCE SS
WRITING
2011 89 92 90 99 97
2010 93 95
2009 91 94 85 98
100

4. DATA IS EVERYWHERE School data is everywhere. Lexiles Quantiles
Vertical Scale Scores
Raw Score
Schools have enough data to clearly define their strengths and weaknesses.
THE CHALLENGE IS NO LONGER GETTING DATA.

5. DATA IS EVERYWHERE
The challenge now is using data to improve school and classroom practices and to raise students’ achievement while they are in school.
It is easy to obtain end-of-the-year data, such as state assessments results; however, schools need to take a proactive approach in obtaining data throughout the school year.
This includes data obtained from formative assessments and classroom practices.
This data will allow schools to diagnose early and provide immediate remediation.

6. Comparison of Traditional and Data-Driven Decision Making
Decision Making Based on Intuition, Tradition, or Convenience
Data-Driven Decision Making
Scattered staff development programs
Focused staff development programs as an improvement strategy to address documented problems/needs
Budgetary decisions based on prior practice, priority programs
Budget allocations based on data informed needs
Staff assignments based on interest and availability
Staff assignments based on skills needed as indicated by data
Staff meetings that focus on operations and the dissemination of information
Staff meetings that focus on strategies and issues raised by school data
Grading system based on each teacher’s criteria of completed work and participation
Grading systems based on common student-performance criteria that report progress on the standards as well as work skills
Periodic administrative team meetings focused solely on operations
Administrative team meetings that focus on measured progress toward data-based improvement goals

7. DATA IS EVERYWHERE
Collecting the data should be a planned, purposeful process. Valuable data will guide the school in developing improvement goals for the benefit of all students.
State Assessment Data
Formative Data
Instructional Data

8. STATE ASSESSMENT DATA

9. STATE ASSESSMENT DATA
Class rosters are mostly determined by utilizing quantiles and lexiles. This includes placement in General Ed. and Pre-AP classrooms.
Reading and Math Enrichment classes are a part of the master schedule. Any 6th, 7th or 8th grade student who does not pass TAKS Reading or Math is placed in these classes. Collaboration between the Enrichment teachers and the general education teachers occurs so that the enrichment teachers emphasize certain skills in the classes. The class size is small to ensure one- on-one instruction .
Goal-setting occurs with the teachers and within each department utilizing this data.
Goal-setting occurs with students utilizing lexiles, quantiles, benchmark scores and AR.
With the advent of STAAR, TAKS item analysis data has become crucial in identifying our weakest areas.

10. STATE ASSESSMENT DATA
The disaggregation of data from the beginning of the school year is a key indicator of instructional support.
A two year comparison of every student’s TAKS performance is made and is a reflection of a teacher’s performance.

11. VERTICAL SCALE SCORE MATH FULL YEAR READING GRADE MET COMM GROWTH 6
637
783 34
644
797 24 7
670
823 33
829 26 8
700
850 30

12. LEXILE DATA
A student’s lexile measure determines his/her reading grade level and sets forth a goal for the student in the AR program.

13. LEXILES
Lexiles provide a common, developmental scale for matching reader ability and text difficulty.

14. RESEARCH-BASED GOAL SETTING
AR goals are set utilizing research-based data. Students are expected to accumulate a certain number of points based on their grade and reading level. Students with below grade reading levels are graded based on reading level improvement, rather than point accumulation.

15. TAKS ITEM ANALYSIS
2011 TAKS data was analyzed and weak TAKS objectives were correlated with STAAR Readiness and Supporting Standards to identify campus weak spots. Instruction is designed to address these hotspots.

16. QUANTILES
Quantiles measure both a student’s math ability and the difficulty of math tasks

17. FORMATIVE DATA

18. DIAGNOSTICS
Throughout the school year, periodic assessments efficiently provide immediate results of student performance on key TEKS.
Diagnostic Exams – ascertain student placement and determine students afforded early intervention through tutorials
Comprehensive Diagnostics – semester exams, keyed to the state assessment and district timeline, track students’ progress and their strengths and weaknesses. Tutorials are readjusted according to the data collected.

19. DIAGNOSTICS
Periodic assessment data should be collected and used during the school year.
During CTLs (Cooperative Teachers Learning), assessment data is shared among teachers and ideas are generated on how to improve instruction.

20. DIAGNOSTICS
Even if teachers are implementing a variety of assessment methods, these assessment options are meaningless unless their results are used to make decisions for improving student achievement.
Meaningful data is charted and displayed with students.

21. INSTRUCTIONAL DATA

22. DATA COLLECTION WALKTHROUGHS
A data system exists that manipulates information from walkthroughs into percentage of curriculum engagement, identification of instructional practices, identification of grouping formats, and identification of research-based instructional strategies. There is also a focus on the learner, which includes an identification of student actions, identification of instructional materials, percentage of usage of HOTS, and percentage of level of class engagement. This can then be compared to student data to see correlation and areas that need attention or professional development.

23. INSTRUCTIONAL MEETINGS
From the data collected, CTLs are held by department. Instructional implications and strategies are discussed based on the data collected.
Each teacher who had the highest performance on a certain TEKS teaches a lesson at a CTL.
Teachers enter assignments into the grade book and identify each TEKS assessed. This enables teachers to see curriculum gaps and trends.

24. DATA COMES FULL CIRCLE
Interventions and instructional strategies are
implemented, including afterschool tutorials, conference pullouts and Saturday Academies.
Students' progress is monitored utilizing teacher observations and mini-assessments; included in the monitoring is the recognition of successful student progress
CTLs and teacher/principal conferences are held to discuss interventios and insructional strategies.
Benchmark and TAKS data, including scores, lexiles and quantiles, are used to identify students' instrucional needs
Identify students and their instructional needs
Research and plan interventions
Implement ideas and strategies
Progress monitor via teacher observations mini-assessments

25. DATA IN THE AGE OF STAAR

26. DATA IN THE AGE OF STAAR MATH AND SCIENCE
Increase in number of open-ended items
Process skills will be assessed in context
4 hour time limit
Implication
Assess students on open-ended questions on each test
Formulate questions to include process skills as identified by the standard
Each test is timed and additional time is not given

27. DATA IN THE AGE OF STAAR READING WRITING
Greater emphasis on critical analysis
Increased focus on expository text
4 hour time limit
Implications
Assessments reflect the changes
WRITING
Assessed in two days
Two essays (personal narrative and expository)
Every assessment includes a form of a personal narrative or expository short answer or essay

28. DATA IN THE AGE OF STAAR
Each STAAR blueprint provides a breakdown of the assessment which should be reflected in every test that is given, including the diagnostics.

29. DATA IN THE AGE OF STAAR
Students scoring commended on the TAKS will pass the STAAR.
Data from students who did not score commended should be scrutinized.
Curriculum gaps
Level of rigor
Depth and Complexity
Teacher level commended growth.
Compare teacher data looking at scores and SE with and without commended

31. HAVE A GREAT YEAR!! For more information, email me at jvillarreal@lfcisd.net