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Transitioning Math Transcript Research in Support of TMP

Transitioning Math Transcript Research in Support of TMP. Dave Pavelchek Washington State University Social & Economic Sciences Research Center Puget Sound Division 360-586-4583 http://www.sesrc.wsu.edu/k12 dpavel@wsu.edu December 2007. Background.

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Transitioning Math Transcript Research in Support of TMP

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  1. Transitioning MathTranscript Research in Support of TMP Dave Pavelchek Washington State University Social & Economic Sciences Research Center Puget Sound Division 360-586-4583 http://www.sesrc.wsu.edu/k12 dpavel@wsu.edu December 2007

  2. Background • SESRC has been providing technical assistance to the TMP project(s) on evaluation issues • SESRC has also been conducting transcript studies for districts, analyzing the relationship between courses and grades and post high school outcomes

  3. The Shoreline Transition Math Partnership • Shoreline School District and Shoreline Community College • Interested in the patterns of students currently making the post-secondary transition • For insight into current dynamics • As a potential baseline to measure change

  4. Study Sample • Graduated from Shoreline District in 2003, 2004 or 2005 • Enrolled in district at least 3 years • SPED & ELL students excluded

  5. Data • High School transcripts for 1,785 students • Community college math placement scores, courses, and grades for the 713 enrolled at Shoreline Community College • Post-secondary followup ranged from 2 to 4 years for the three classes

  6. Definitions of “College Ready” • A student who enrolled in a college-level course at a community college without first taking a remedial course. OR • A student whose placement test score placed them at a college math level

  7. Coding Courses from Transcript to Ordinal Scale

  8. What the Graduates Do

  9. College Type by Gender

  10. College Type by Ethnicity

  11. Math Grades and College Attendance(Note - percentages are of each grade level)

  12. Math Preparation

  13. Math Preparation & College Type(Note – the percents are of each Prep Level)

  14. Composition of Math Preparation for Shoreline Grads at Different College Types

  15. The Traditional College ‘Gateway’

  16. Int 3 Grade & College Type(Note – the percents are of each Grade Level)

  17. Composition of Int 3 Grades for Shoreline Grads at Different College Types

  18. The Senior Math Question

  19. Senior Math Composition for Shoreline Grads at Different College Types

  20. What Level of Math?

  21. Delayed Math in College?

  22. Grades in College Math

  23. Grades in Remedial Math

  24. Remedial Grades & Further Progress

  25. Adaptive Placement Test Components

  26. Placement Results

  27. Is There Math After Placement?

  28. Empirical Evidence for Placement Process

  29. Complexity and the Colinearity Problem • What we would like to know is causation – what ‘gives’ students better math skills and retention • If not that, predictive value – what tells us how what a student should tackle next, and how hard it will be • Unfortunately, the usual suspects travel in packs. The same students who get higher math grades in Int 3, tend to get higher math grades overall, and take more and more advanced math, and are more likely take math in senior year • This is called “colinearity” & it is why we resort to multivariate regression

  30. Research Questions on Remedial Enrollments • How well are Placement Test Scores predicted by high school math preparation? • How well is Enrollment in Remedial Math predicted by high school math preparation? • How well is Enrollment in College Level Math predicted by high school math preparation? • How well are Grades in College Level Math predicted by high school math preparation?

  31. College-Readiness Rates 36% directly into College-Level

  32. College-Readiness Rates and Preparation

  33. Remedial Rates and Four Year Colleges(Northshore Data)

  34. Untangling Complexity, What Factors Predict Best

  35. Factors in the Remedial Process

  36. College Performance Factors

  37. Findings from Similar Projects • Different districts are interested in different questions. • Data availability changes • Methods improve (hopefully).

  38. “Math Atrophy” is Hard to FindNorthshore SD data

  39. Examples of Other Uses of Transcript Research • Investigating Remedial English • Patterns of enrollment & persistence by type of college • Role of CTE in college selection, enrollment & persistence

  40. Transcript Research on Remedial English (Not Shoreline SD Data)

  41. Findings from Regression on English College Readiness(Pooled Data from 2 Districts) • Logistic regression with an R2 value of approximately 20% (N=717) • Highest level English course taken strongest • English GPA was second-most important • ELL, & Ethnicity were also significant • Non-significant variables: gender and taking any English in the senior year

  42. However, remedial English is not just a problem for recent immigrants(Not Shoreline SD Data)

  43. Persistence from First to Second College Year by Level of HS Coursework (Not Shoreline Data)

  44. Percent Transferring from a Local 2-Year to 4-Year College by HS Grades(Not Shoreline SD Data)

  45. For more information To view other WSU-SESRC research reports on education topics please see our website at: http://www.sesrc.wsu.edu/k12/ Email: dpavel@wsu.edu sternpo@wsu.edu

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