"In God we trust, all others bring data"

"In God we trust, all others bring data"

USING DATA TO CLOSE THE ACHIEVEMENT GAP Title 1 Categorical Program Advisors September 15, 2006 Dr. Ruth S. Johnson, Presenter Presentation Topics Introduction and Overview The Power of Data in the Change Process The Data Context Data: Uncovering and Using Other Data Data Stories Throughout Dr. Ruth Johnson The Power of Data: Using Data to Create Reform In God we trust, all others bring data. The author and principle thinker of and in these materials was Dr. Eddie Green, Interim Deputy Superintendent,

Division of Educational Services, Detroit Public Schools. Accountability and Equity I am defining equity as an operational principle for shaping policies and practices which provide high expectations and appropriate resources so that all students meet or exceed rigorous standards-with minimal variance due to race, income, language or gender. (c) R. 2002 Germanine-Watts and Hart in Johnson, Moving From Basing Achievement on Percentiles (c) and Percentages to Mastery of Standards Thinking about Change The Candy Factory Getting Off Dead Horses

(c) What We See, Depends on Where We Look Our understanding of problems and issues often act as mental straitjackets that prevent us from seeing other ways of formulating basic concerns and finding the alternative courses of action. Morgan, G.p.178 (c) Data Decisions Influence School Careers: The Good, the Bad, and the Ugly On one hand, a steady stream of data showing gaps among groups may serve to: Create myths and reinforce faulty perceptions about students capabilities Develop permanent labels/ groups/ tracks Acceptance and toleration of gaps Produce a climate of fear and blame that fuels negative behaviors and institutional cannibalism Dr. Ruth Johnson

Data Decisions Influence School Careers: The Good, the Bad, and the Ugly On the other hand data may serve to: Create a culture of inquiry that identifies, uncovers, and magnifies what current conditions need to change Explode myths,focus dialogue, and provide a powerful opportunity to move from limited thinking to possibility thinking Dr. Ruth Johnson Data Decisions Influence School Careers: The Good, the Bad, and the Ugly On the other hand data may serve to: Create ownership From Caboose to Engine: Moving from data provider to data generator and user Create a passion to challenge and eliminate inequities that handicap adults and students Unlock genius in adults and students Dr. Ruth Johnson

The Context Possible Implications for Young People Related to Levels of Education and Potential Earnings Yearly Earnings Potential As Related to Educational Level YEARLY EARNINGS POTENTIAL AS RELATED TO EDUCATIONAL LEVEL Not a High School Graduate EDUCATIONAL LEVEL High School Graduate Only Some College, No Degree Associate Degree Bachelor's Degree Master's Degree Doctorate

Professional Degree $0 $10,000 $20,000 $30,000 $40,000 $50,000 YEARLY EARNINGS $60,000 Source: 1994 U.S. Census Data. $70,000 $80,000 The Employment Picture Is Changing: Who Needs the College Prep Curriculum?

Telephone Line Installers and Repairers: algebra and trigonometry Airplane technicians: physics, chemistry, advanced mathematics, computers and electronics Recent survey of human resource personnel reveals employers are looking for: Strong reading and research skills,including literature Strong writing and research skills Mathematics through algebra II Source: Ed Trust: Making Data Work: School and Community Guide (Downloaded from the web 2/6/06) Who Goes to Four Year Colleges? PERCENT OF STUDENTS 100% Kindergarten Enrollees 75%

12th Grade Graduates 50% Eligible for Public California 4-Year College/University 25% Entered Public California 4-Year College 0% African American Asian Latino RACE / ETHNICITY Source:The Education Trust, Inc. Achievement in America 2001 in Johnson, R. 2002. Using Data to Close the Achievement Gap (c) White The Pipeline

by the end of the century the grade 9-10 transition was clearly the largest leak in the education pipeline. (Cohort progression analysis of the 50 states) Recent evidence from Texas indicates that 7080% of students who are flunked to repeat grade 9 will not persist in school to high school graduation. Source: The Pipeline Project The Pipeline and Gaps Over Time In short, failure to graduate from high school dramatically increases the odds that young people, especially black males, will end up in prison at least once and even more than once. The National Board on Educational Testing and Public Policy. The Education Pipeline in the United States 1970-2000

District Mandated Local Assessments Name Types Grades Classroom/Instructional Focus for Intervention Freque Standards Master Standards-Based Grades 2-11 Use to identify weaknesses in standards and to inform instruction through Student Advisory, Standards Advisory, Item Analysis reports; can create mini-lessons Monitor progress through TOPS, Summary, and Progress Reports

3 Grades 1-11 Motivate students to read by setting and attaining goals for book levels and points Monitor student comprehension of reading assignments (Student Record Reports, Reading Logs, Diagnostic Reports) Identify weak skill areas (Literacy Skills Tests) reading logs, power lessons, checking TWI reports, etc. Set goals for book levels Daily Week Grades 1-11 Motivate students to work at their own pace in

appropriate standards-based libraries Monitor goals towards meeting objectives (Status of the Class, practice assignments and tests, Diagnostic Reports, Goal History) Identify weak skill areas (Status of the Class and TOPS Reports) by teaching math vocabulary, power lessons, AM library, etc. Set goals for number of objectives mastered and average percent correct Daily Week Grades K-3 Use Daily Accelerated Reading Accelerated Math STAR Early Literacy

Standards-Based to identify emergent readers proficiency in literacy skills by domain (graphophonemic time Elem./S 4 time Second Traditional Ways to Look at Achievement Gaps Traditional: Evidence that indicates varying levels of achievement among groups Noting which groups are consistently performing at upper and/or lower levels. Disaggregation Proportionality Comparison to Other Locales Dr. Ruth Johnson Achievement of Different Groups Av era g e M a th N A E P S co re

300 220 1973 1978 1982 1986 African American 1990 Latino 1992 Source: US Department of Education, National Center for Education Statistics. NAEP 1994 Trends in Academic Progress (p. 137) Washington, DC: US Department of 1998 by The Education Trust, Inc. Education, July 1994. Additional Ways to Look at Achievement Gaps Related to Equity Direction

of gap closure Gap for each group from the desired goal Gaps beyond traditional test score measures Technology Dr. Ruth Johnson Additional Ways to Look at Achievement Gaps Related to Equity Program Placements (G&T, Special Ed, Course Enrollments, etc.) Gaps Over Time Non Academic - Sally Can Skip, but Jerome Cant Jump (Source: Denise Collier. Doctoral Student CSULA/UC Irvine) See Hand outs

Dr. Ruth Johnson Peeling the Data: Ways of Looking at Test Information 5. Individual Teacher 6. Individual Student 7. Content Cluster strengths and weaknesses (School, grade, class, student) 8. Content Cluster alignment to the curriculum 9. Over Time Ruth Johnson Peeling the Data: Levels of Looking at Data 1. District 2. K-12 Feeder Patterns 3. School levels 4. Grade level 5. Programs/tracks 6. Classroom/teacher

7. Student Ruth Johnson District Goal: All Students will take SAT and PSAT and will graduate meeting the A-G Requirements A-G Completion Rates-Primary Source: The Achievement Council: Secondary Source: Data Quest 100% 1999/2000 83% 80% 60% 50% 53% 2000/2001 47%

40% 25% 20% 18% 23% 10% 0% Asian White Latino African American District Goal: All Students Will Graduate Meeting the A-G Requirements Students Enrolled by Grade Compared to the Number of Students in Desired Course Sequence

1600 1400 1200 1000 800 600 400 200 0 1369 Enrolled On Target 362 305 79 35 63 15 Grade 7 Grade 8

26 Grade 9 351 52 Grade 10 209 27 Grade 11 8 Grade 12 146 Total District 6 CST Language Arts % of Students Proficient & Advanced 2001-2006 Question: What do we want these data to look like and when?

100 90 80 70 60 50 33 33 40 24 19 30 23 20 17 20 21 18 17 17 14 18 15 17 17 12 20 11 9 10 13 12 11 8

9 11 9 9 8 7 0 2nd 3rd 4th 5th 6th Source: LAUSD Region 6-Spring Data 2006 7th 8th 9th

10th 11th 2001 2002 2003 2004 2005 2006 District 6 CST Mathematics % of Students Proficient & Advanced 2002-2006 What is the performance goal? 90 What do the disaggregated data of different groups 80 look like? 70 100 60

43.5 50 32.6 40 30 2002 2003 2004 2005 2006 19.5 20 6.8 9 12.6

5.4 6.4 10 0 Elementary Middle HS 7.1 English Language Arts CST, Spring 2006 All Students, Local District 6 Percent Proficient & Advanced 100 90 80 70 60 50 33

33 40 30 26 20 26 22 24 17 20 17 18 17 19 19 19 18 14 15 20 14 13 10 0 Percent What story do these data tell? Are you satisfied with the level of progress? 2nd 3rd 4th

5th 6th 7th Grade 8th 9th 10th 11th 2004-2005 2005-2006 Mathematics Percent CST, Spring 2006 All Students, Local District 6

Percent Proficient & Advanced 100 90 80 70 60 50 40 30 20 10 0 50 43 2nd 44 47 35 3rd

2004-2005 43 28 4th 5th Grade 2005-2006 34 16 18 6th 12 17 7th Instructional Emphasis

(c) English Language Arts CST 2003-2005 % Tested by Strand/Reporting Cluster 80 70 Written Conventions us c Fo Writing Strategies d se a e cr Literary Response & n I P e rc e n t

60 50 40 30 Analysis Reading Comp. 20 Word Analysis 10 2 3 4 5 6

7 8 9 10 s cu Fo Grade by Strand/Reporting Cluster 11 ed as re ec D 0 English Language Arts

CST 2003-2005 % Tested by Strand/ Reporting Cluster 80 70 Percent 60 50 40 30 18 14 9 6 8 15 15 9

15 20 10 22 16 16 16 13 17 20 13 20 18

16 12 16 17 12 17 17 13 18 9 Written Conventions 13

8 15 17 20 22 15 16 16 17 18 18 18 19

3 4 14 13 11 9 8 8 8 5 6 7

8 9 10 11 Grade by Strand/Reporting Cluster Literary Response & Analysis Reading Comp. 0 2 Writing Strategies Word Analysis Mathematics CST 2003-2005 % Tested by Grade & Strand/Reporting Cluster

Percent 70 60 Algebra & Functions II 50 Statistics, Data Analysis, and Probability Measurement & Geometry 40 Algebra & Functions 30 Number Sense II 20 Number Sense I 10

0 2 3 4 5 6 Grade by Strand/ Reporting Cluster 7 d se a e cr n I De cre

a us c Fo sed Fo cu s Mathematics CST 2003-2005 % Tested by Grade & Strand/Reporting Cluster Percent 70 5 60 7 50

14 40 6 30 4 4 11 16 12 23 16 12 15

15 10 18 17 5 13 19 Algebra & Functions II Statistics, Data Analysis, and Probability Measurement & Geometry Algebra & Functions 10 15 20 17

Number Sense II 10 8 Number Sense I 10 15 16 16 2 3 4 12 15

14 6 7 0 5 Grade by Strand/ Reporting Cluster Linking to CST Supplemental Slides Hand outs Standards Finder (c) Exploding Myths

They (African Americans and Latinos) are not capable of achieving in higher level courses. Dr. Ruth Johnson New York City 9th Graders Passing Regents Science 9433 10000 8794 5878 4496 2227 3499 4087 2209 1500

African American Asian 1994 Source: New York City Chancellors Office 1998 by The Education Trust, Inc. Latino 1995 White Second Grade African American Students Who Scored in the First Quartile in Reading Students Name Language Teacher Score 1999

%ile Teacher 2000 Gender Reading %ile Math %ile 1. Larry C Male 17 60 4 M.P.

O.C. 2. Allen R. Male 22 4 5 M.P. A.F. 3. Donald E. Male 21 69 56

H.L. O.C. 4. Dave S. Male 14 40 17 C.G. O.C. 5. Jan K. Female 22

69 24 M.P. O.C. 6. Joe L. Male 7 45 24 H.L. O.C. 7. Rita D. Female

18 12 15 C.G. E.D. 8. Ben T. Male 9 13 31 C.G. E.D.

All of these students are in the 2nd and 3rd quartiles in math, but at the 1st quartile in reading Exploding Myths They dont have college aspirations. Yes, they do! Dr. Ruth Johnson Figure 4.8 CAREERS, COLLEGE EXPECTATIONS AND COURSE SELECTION Survey of High School Students in One California District Aspires to a career that requires college Expects to attend college Enrolled in college-preparatory courses African American Asian 100%

100% 75% 75% 65% PERCENT OF STUDENTS PERCENT OF STUDENTS 84% 50% 25% 20% 0% CA. Department of Ed. 1990; The Achievement Council in Johnson, R.2002 Using Data

50% 25% 0% 92% 88% 70% Figure4.8 CAREERS, COLLEGEEXPECTATIONSANDCOURSESELECTION Surveyof HighSchool Students inOneCaliforniaDistrict Aspirestoa career that requirescollege Expectstoattendcollege Latino White

PERCENT OF STUDENTS 100% 100% 78% PERCENT OF STUDENTS Enrolledincollege-preparatorycourses 75% 80% 75% 60% 50% 70% 50%

40% 25% 25% 15% 0% 0% Department of Education, 1990; TheAchievement Council. Source: California Highest Achieving Low-Income Students Attend Postsecondary at Same Rate as Bottom Achieving High Income Students Achievement LowHighLevel (in quartiles) Income Income First (Low) 36%

77% Second 50% 85% Third 63% 90% Fourth (High) 78% 97% Primary Source: The Education Trust (2004) IMPROVING ACHIEVEMENT AND CLOSING GAPS BETWEEN GROUPS. Source: NELS: 88, Second (1992) and Third Follow up (1994); in, USDOE, NCES, NCES Condition of Education 1997 p. 64 Exploding Myths They need to be in lower level courses in order to meet their needs. A Rigorous Math Curriculum Improves Scores For All Students 360 240

Pre-Algebra or General Math Algebra I African American Geometry Latino Algebra II Precalculus or Calculus White Source: National Assessment of Educational Progress, 1992 Mathematics Trend Assessment, National Center for Education Statistics. NAEP 1992 Trends in Academic Progress (p 113). Washington, DC: US Department of(c) Education. 1994 1998 by The Education Trust, Inc. 1998 by The Education Trust, Inc. Being Suspicious About Data:

The Other Data - Multiple Sources and Voices Ruth Johnson Use Combination Indicators (c) A Students in High Poverty Schools Score at About the Same Level as C and D Students in Affluent Schools 60 56.5 Test Score 51.7 47 Mostly A's Mostly B's

Mostly D's Mostly

Research Report (p. 3) January 1994. 1998 by The Education Trust, Inc. Number of Students on "Grade-Level" Comparison of Grade Point Averages and SAT/ 9 Performance PERCENT OF STUDENTS 100% 80% 60% 40% 20% 0% 9th A,B,C, Total GPA 10th 11th

GRADE LEVEL SAT/9 Reading at 50%ile or Above SAT/9 Math at 50th %ile or Above Source: The Principals Exchange, Whittier, CA in Johnson, R. 2002 12th Other Indicators Non-academic Program effectivenes (c) Discipline Gap: Consistent Removal from Regular Programs Special Education Pullout Programs Alternative Education Suspensions

Remedial Classes Slide Developed by Denise Collier: Jt Doc Student Over-punishment studies have yet to find that racial disparities in misbehavior sufficient enough to account for the typically wide racial differences in school punishment (Skiba, Michael and Nardo, 2000) Slide Developed by Denise Collier: Jt Doc Student CSULA and UC Irvine Program Fidelity Class Size Small Schools, Schools Within Schools Support Programs Ruth Johnson Unfocused Academic Initiatives Christmas Tree Schools

Showcase schools with many new programs Multiple add-ons with little coordination Little attention to strengthening organizational core Entrepreneurial principals actively seeking resources Source: Consortium on Chicago School Research, July 1993 The Emperors New Clothes SCHOOL INITIATIV ES FUNDED PROGRAMS GOING IN ALL DIRECTIONS MAND AT

ES DISTRICT PR OG ETC. RAMS Reforms, Culture, and Equity: Who Benefits? Class Size Small Schools Support Programs Graduation Dropout Discipline Dr. Ruth Johnson Use of Time An analysis of time is critical. Students in lower achieving schools

have less academic time. Students in low tracks and remedial classes experience less academic time. If we provide more low quality time, the impact on student achievement will probably be minimal. Ruth Johnson Use of Time The Principals Exchange (2001) in their school evaluations found lots of wasted time at the beginning and ending of the school year in lower achieving schools. Whole School Inquiry Stages in Whole School Inquiry Identify objectives and define questions: What do we need to know? Determine what types of data are needed to answer the question(s), how the data will be collected, and who will be responsible for collection? Collect and disaggregate the data.

Ruth Johnson Stages in Whole School Inquiry Collect and disaggregate the data. Determine how the data will be summarized,analyzed, and interpreted. Determine with what audiences the data will be shared and the appropriate presentations. Provide a framework for the data dialogue; design appropriate solutions and an action plan. Ruth Johnson

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