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Comparing Growth in Student Performance

Comparing Growth in Student Performance. David Stern, UC Berkeley Career Academy Support Network Presentation to Educating for Careers/ California Partnership Academies Conference Sacramento, March 4, 2011. What I’ll explain.

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Comparing Growth in Student Performance

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  1. Comparing Growth in Student Performance David Stern, UC Berkeley Career Academy Support Network Presentation to Educating for Careers/ California Partnership Academies Conference Sacramento, March 4, 2011

  2. What I’ll explain • Why “value added” is the most valid way to compare academy students’ progress with other students in same school and grade • How to compute value added • Example of application to career academies

  3. ??? What questions do you have at the start?

  4. What is “value added”? • Starts with matched data for individual students at 2 or more points in time • Uses students’ characteristics and previous performance to predict most recent performance • Positive value added means a student’s actual performance is better than predicted • If academy students on average perform better than predicted, academy has positive value added

  5. .93 Correlation between academic performance index and % low-income students in California school districts

  6. Value added is better measure than • Comparing average performance of 2 groups of students without controlling for their previous performance – because one group may have been high performers to start with • Comparing this year’s 11th graders (for example) with last year’s 11th graders – because these are different groups of students!

  7. Creates better incentives • Reduces incentive for academy to recruit or select students who are already performing well • Recognizes academies for improving performance of students no matter how they performed in the past • Provides a valid basis on which to compare student progress, and then ask why

  8. What NOT to do • DON’T attach automatic rewards or punishments to estimates of value added – use them as evidence for further inquiry • DON’T rely only on test scores – analyze a range of student outcomes: e.g., attendance, credits, GPA, discipline, etc. • DON’T use just 2 points in time – analyze multiple years if possible, and do the analysis every year

  9. Recent reports • National Academies of Science: “Getting Value out of Value Added” http://www.nap.edu/catalog.php?record_id=12820 • Economic Policy Institute: “Problems with the Use of Student Test Scores to Evaluate Teachers” http://epi.3cdn.net/b9667271ee6c154195_t9m6iij8k.pdf

  10. How it’s done • Need matched data for each student at 2 or more points in time • Accurately identify academy and non-academy students in each time period • Use statistical regression model to predict most recent performance, based on students’ characteristics and previous performance

  11. Example: comparing teachers • Each point on graph shows one student’s English Language Arts test score in spring 2003 (horizontal axis) and spring 2004 (vertical axis) for an actual high school • Regression line shows predicted score in 2004, given score in 2003 • Students who had teacher #30 generally scored higher than predicted in 2004 – this teacher had positive value added

  12. Scatterplot of 2003 and 2004 English Language Arts scores at one high school

  13. Scatterplot of 2003 and 2004 scores,with regression line Dots above the line represent students who scored higher than predicted in 2004. Dots below the line represent students who scored lower than predicted.

  14. Most students with teacher 30scored higher in 2004than predicted by their 2003 score This student’s 2004 score was higher than predicted This student’s 2004 score was lower than predicted

  15. Example using academies, in a high school with 4 career academies and 4 other programs: Programs 2, 4, 5, and 8 are career academies

  16. Parents’ education differs across programs

  17. Student ethnicity also differs

  18. Students in programs 4, 5, and 8 are • less likely to have college-educated parents • less likely to be white. Comparisons of student performance should take such differences into account.

  19. Grade 11 enrollments, 2009-10 Analysis focused on students in grade 11 who were present in at least 75% of classes.

  20. Mean GPA during junior year, 2009-10

  21. Mean 11th grade test scores, spring 2010

  22. Mean 8th grade test scores for 2009-10 juniors

  23. Juniors in programs 4 and 5 had lower grades and test scores. But comparing 11th grade test scores is misleading because students who entered programs 4 and 5 in high school were already scoring lower at end of 8th grade. More valid comparison would focus on CHANGE in performance during 2009-10.

  24. Numbers of students by change in English lang. arts performance level during 2009-10 Performance levels: far below basic, below basic, basic, proficient, advanced. Only program 8 had more students whose performance level went up than students whose performance level went down.

  25. Change in GPA from grade 8 to 11 Programs 1, 3, and 8 had students with highest GPAs in 8th grade. GPA in 11th grade was lower than in 8th grade for students in these 3 programs.

  26. Predicting 2010 test score based on 2009 score Dots above the line represent students who scored higher than predicted in 2010. Dots below the line represent students who scored lower than predicted.

  27. Predicting 11th grade GPA based on 8th grade Dots above the line represent students who scored higher than predicted in 2010. Dots below the line represent students who scored lower than predicted.

  28. Regression analysis uses prior performance along with other student characteristics to estimate each student’s predicted performance in 2009-10. In this analysis, programs 2-8 are compared to program 1. Positive regression coefficient says, on average, students in that program exceeded prediction more than students in program 1 did.

  29. Value added results for test scores Only program 8 had positive value added compared to program 1. The only statistically significant differences with program 1 were programs 2 and 4, both negative. In these two programs, students scored significantly lower than predicted.

  30. Value added results for GPA Programs 3, 6 and 8 were significantly different from program 1. Average GPA was lower than predicted in these three programs.

  31. Questions for this school • Why did juniors’ GPA in 2009-10 fall below prediction in programs 3, 6, and 8? • Why did juniors’ test scores in English language arts fall below prediction in programs 2 and 4? • Important to see whether these patterns persist for more than one year.

  32. Conclusion • Academy National Standards of Practice: “It is important to gather data that reflects whether students are showing improvement and to report these accurately and fairly to maintain the academy’s integrity.” • Measuring value added will keep academies in the forefront of evidence-based practice

  33. ??? What questions do you have now?

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