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Using Data to Drive Change

Using Data to Drive Change. MAG Conference November 2006. How do we use data to influence decisions?. School Level by Bruce Hislop, PGCPS Program Level by Clare Von Secker and Steve Bedford, MCPS Instructional Level by Carolyn Wood, HCPS and Steve Perakis, CCPS

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Using Data to Drive Change

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  1. Using Data to Drive Change MAG Conference November 2006

  2. How do we use data to influence decisions? • School Level by Bruce Hislop, PGCPS • Program Level by Clare Von Secker and Steve Bedford, MCPS • Instructional Level by Carolyn Wood, HCPS and Steve Perakis, CCPS • Interventions by Bob Lissitz, MARCES, UMCP

  3. Determining Reasonable AMO Targets for Schools Far Above or Below State AMO Targets Bruce Hislop Accountability Reporting Officer, PGCPS Maryland Assessment Group Conference November 16, 2006

  4. AMO Scenarios For A Given School

  5. SY07 Target For A “Yellow” School

  6. SY07 Target For A “Yellow” School

  7. SY07 Target For A “Yellow” School

  8. SY07 Target For A “Pink” School

  9. SY07 Target For A “Pink” School

  10. SY07 Target For A “Pink” School

  11. SY07 Target For A “Green” School

  12. SY07 Target For A “Green” School

  13. SY07 Target For A “Green” School

  14. SY07 Target For A “Red” School

  15. SY07 Target For A “Red” School Identification of “Red” Schools • Distance below SY06 AMO • Level in School Improvement (RP, RI)

  16. SY07 Target For A “Red” School Three “Four-year-to-target” Models Linear Growth 70-70-85-100 50-50-75-100

  17. SY07- SY10 Targets For A “Red” School Linear Growth

  18. SY07- SY10 Targets For A “Red” School Linear Growth

  19. SY07- SY10 Targets For A “Red” School 70-70-85-100

  20. SY07- SY10 Targets For A “Red” School 70-70-85-100

  21. SY07- SY10 Targets For A “Red” School 50-50-75-100

  22. SY07- SY10 Targets For A “Red” School 50-50-75-100

  23. Which Model Is Right For You? Model is determined by levels of challenge and opportunity: Percent FARMS Percent classes taught by HQT Years in principalship

  24. Which Model Is Right For You? Degree of each factor is weighted, giving a weighted option scale.

  25. Which Model Is Right For You? Sum of weights determines accountability model

  26. This plan is under development and will most likely undergo more revisions.

  27. Using PSAT Data to Drive Instructional Change Clare Von Secker, Ph.D. and Stephen L. Bedford Montgomery County Public Schools November 16, 2006

  28. Goals of PSAT Testing • Preparation for the SAT • Identify students who • Need additional support • Usually score range of 20 to 35 • Have Honors/AP potential • At or above mean verbal score of 44 • At or above mean math score of 45

  29. PSAT Honors/AP Identification • Identified as many as 2,000 sophomores per year who had Honors/AP potential but • Were enrolled in regular-level English and mathematics courses in Grade 10 • Were still enrolled in regular-level English and mathematics courses in Grade 11

  30. Identify Other Considerations/Factors/Barriers • Enrollment decisions take into account students’ • Enrollment in other Honors courses • Academic performance • Attendance • Interests, motivation, and recommendations

  31. Data Management Needs • Decision-makers needed a way to • Compile all relevant student Honors/AP indicators • Sort quickly and efficiently through large amounts of student-level information • Get buy-in from stakeholders (teachers, students, parents)

  32. Key Indicators • Stakeholders identified • PSAT participation • PSAT verbal, math, and writing scores • Semester 1 enrollment in Honors-level core courses • Total number of semester 1 Honors courses • Semester 1 English courses and marks • Semester 1 mathematics courses and marks • Grade point average (GPA) • Attendance rate • Participation in services for limited English proficiency and special education • Gender and race/ethnicity (to assure equity)

  33. Student variables are listed as column headers in EXCEL The column headers included for each school are: • Grade • High school attended • Student last name, first name, and MCPS id • Yes/No “flag” showing whether student took the PSAT in Grade 10 • PSAT verbal, math, and writing scores • Yes/No “flags” showing enrollment in Honors-level English, mathematics, science, social studies, and any course • Total number of Honors core courses taken semester 1 • Semester 1 English and mathematics courses and final marks • Grade point average (GPA) • Attendance rate • Gender and race/ethnicity • Yes/No flag showing participation in services for limited English proficiency or special education • Name of student’s guidance counselor • Columns for counselors notes

  34. Filters identify all values of each variable The values for the variable showing students’ Semester 1 Grade 10 math course include • Below Algebra 1A (remedial mathematics courses) • Algebra 1A • Geometry A • Honors Geometry A • Algebra 2A • Algebra with Analysis 2a (Honors Algebra 2) • Precalculus • Precalculus with Analysis (Honors Precalculus)

  35. How many of the students with PSAT scores of 45 or higher are not enrolled in Honors mathematics during the first semester (Honors math = No)? The search found 97 out of a total 552 students in the sophomore class who should be considered for Honors-level mathematics in Grade 11.

  36. The search found 33 with PSAT math scores greater than or equal to 45 who were not enrolled in any Honors course during the first semester.

  37. The search found 18 students with • PSAT math scores greater than or equal to 45 • not enrolled in any Honors course during the first semester • with GPAs greater than or equal to 2.5.

  38. Hispanic female student • PSAT scores of 64 (verbal), 54 (math), and 62 (writing) • not enrolled in any Honors course • GPAs greater than or equal to 2.5 • C in English 10 • D in Algebra 2

  39. Mastering Algebra: Using Teacher-Made Assessments to Support Teaching and Learning What We Can Learn About Students and Instruction From Locally-Developed Mid-Term Exams

  40. Meeting the H S A Challenge • Beginning with students entering grade 9 in September, 2005, all students must pass four High School Assessments, including Algebra/Data Analysis, or earn a minimum score on each and a Composite Score as a prerequisite to earning a Maryland high school diploma

  41. What We Know • Students enrolled in grades 7, 8, and 9 during 2005-06 must PASS H S A BUT • In 2006, only 12% of our 7th graders took and passed Algebra and only about 33% of our eighth graders had taken and passed Algebra • In the past, about 40% of students have completed 9th grade without having passed Algebra

  42. The Scope of the Challenge in Algebra: Where We Stand • AS OF SEPTEMBER, 2006— • Current 10th Graders • 1800 students have passed H S A Algebra • 1300 students have yet to pass H S A • Current 9th Graders • 1200 students have passed H S A Algebra • 2000 students have yet to pass H S A

  43. Math Skill among Middle School Algebra Takers is High: 2006 Data

  44. Math Skill Level of Studnts Taking Algebra in High School is Low

  45. Success in Algebra: What It Takes • Curriculum aligned to Core Learning Goals • Instruction aligned with research-based best practice and the individual needs of students • Frequent assessment that is FORMATIVE to inform instruction and motivate student effort

  46. Course Pathways to Mastery of Algebra 1 • Fully prepared middle school students complete Algebra in ONE YEAR (Grade 7) • Somewhat prepared middle school students complete standard Algebra course in TWO YEARS (Grades 7-8 or Grades 8-9) • Struggling students (including those who previously failed H S A) complete Cognitive Tutor Algebra course in Two Years (Grades 8-9 and Grades 9-10) • NOTE Cognitive Tutor was introduced for the first time last year in some schools and grade levels

  47. Cognitive Tutor • Based on cognitive model that simulates student thinking and problem-solving in math • (Partially) Computer-delivered • Individualized to meet needs of student • Continuing assessment to support diagnostic-prescriptive teaching • Simulates one-on-one coaching • Meets “scientifically-based research” requirements of No Child Left Behind

  48. Multiple Roles of Assessment • Certify student attainment of standard • Individual students • Programs (based on aggregated scores) • Identify students for intervention • Placement in an “intervention program” • Instructional assistance as needed • Inform teachers’ instructional decisions (pacing, delivery, amount and kind of content) • Group item-level data—item analysis, item discriminations • Individual item-level data—items and item clusters • Evaluate and improve instructional programs • Success rates on local end-of-course tests • Success rates on state tests

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