Understanding Value-Added

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Understanding Value-Added. Lesson 2: How Value-Added Works. What is the Value-Added Metric?. Academic Growth = Student Learning. 2009. 2010. Value-Added is the District’s measure of elementary school growth. Value-Added is a nationally recognized way of measuring growth. - PowerPoint PPT Presentation

Transcript of Understanding Value-Added

  • Understanding Value-AddedLesson 2: How Value-Added Works

  • What is the Value-Added Metric?Value-Added is the Districts measure of elementary school growth.Value-Added is a nationally recognized way of measuring growth.*Emphasizes continual student improvement

    Provides information to understand what drives continual improvement

  • Measuring Growth, Not Attainment*In this school, the percent meeting state standards is 25% in both Year 1 and Year 2.

    Attainment is unchanged but are students learning?

    Chart1

    0.25

    0.25

    Series 1

    Percent Meeting/Exceeding

    Sheet1

    Series 1

    Year 10.25

    Year 20.25

    To resize chart data range, drag lower right corner of range.

  • Accounting for Student PopulationsStudent academic growth varies by grade, prior performance, and demographics.

    The goal of the Value-Added metric is to measure the schools impact on student learning independent of student demographic factors.

    Value-Added accounts for the following student factors:

    Controlling for the factors above gives proper credit for growth to low attainment schools and schools that serve unique populations.

    *

    Prior ISAT Reading ScoreLow-Income StatusPrior ISAT Math ScoreELL StatusGrade LevelIEP StatusGenderHomelessnessRace/EthnicityMobility

  • How it WorksValue-Added is not a comparison to similar schools.We do not look for a comparison group of schools that match each other on all 9 student factorssuch a group might not exist.

    Rather, Value-Added compares growth of students in each school to growth of students across the District, controlling for the list of student factors.

    To do this, we utilize a regression methodology, developed in collaboration between CPS and academic experts from the University of Wisconsin.

    *

  • All ISAT Math Scores for the District3rd to 4th Grade ISAT Test Scores OnlyThis line shows the average gain on ISAT math between 2009 and 2010 for 4th graders.

    It is downward-sloping because at higher levels of prior performance, average growth is smaller.Repeat the process for each grade level.Regression LinesRegression shows how growth relates to another variablein this case prior performance on the ISAT.*

  • All ISAT Math Scores for the District3rd to 4th Grade ISAT Test Scores OnlyThis line shows the average gain on ISAT math between 2009 and 2010 for 4th graders.

    It is downward-sloping because at higher levels of prior performance, average growth is smaller.Repeat the process for each grade level.Regression LinesRegression shows how growth relates to another variablein this case prior performance on the ISAT.*

  • All ISAT Math Scores for the District3rd to 4th Grade ISAT Test Scores OnlyThis line shows the average gain on ISAT math between 2009 and 2010 for 4th graders.

    It is downward-sloping because at higher levels of prior performance, average growth is smaller.Repeat the process for each grade level.Regression LinesRegression shows how growth relates to another variablein this case prior performance on the ISAT.*

  • All ISAT Math Scores for the District3rd to 4th Grade ISAT Test Scores OnlyThis line shows the average gain on ISAT math between 2009 and 2010 for 4th graders.

    It is downward-sloping because at higher levels of prior performance, average growth is smaller.Repeat the process for each grade level.Regression LinesRegression shows how growth relates to another variablein this case prior performance on the ISAT.*

  • Controlling for One Variable*Gain of 4th to 5th Grade students at a single school controlling for prior performance compared to the District averageThis student grew faster than other 5th grade students with the same prior ISAT score.This student grew slower.

  • Regression allows us to control for multiple factors at one time in this case prior performance and ELL status. This line shows the average gain for all students from 4th to 5th grade.The blue line shows the average gain for ELL students between 4th and 5th grade.The orange line shows the average gain of non-ELL students between 4th and 5th grade. Controlling for Multiple Variables*Now we identify which students are English Language Learners

  • Regression allows us to control for multiple factors at one time in this case prior performance and ELL status. This line shows the average gain for all students from 4th to 5th grade.The blue line shows the average gain for ELL students between 4th and 5th grade.The orange line shows the average gain of non-ELL students between 4th and 5th grade. Controlling for Multiple Variables*Now we identify which students are English Language Learners

  • Regression allows us to control for multiple factors at one time in this case prior performance and ELL status. This line shows the average gain for all students from 4th to 5th grade.The blue line shows the average gain for ELL students between 4th and 5th grade.The orange line shows the average gain of non-ELL students between 4th and 5th grade. Controlling for Multiple Variables*Now we identify which students are English Language Learners

  • Regression allows us to control for multiple factors at one time in this case prior performance and ELL status. This line shows the average gain for all students from 4th to 5th grade.The blue line shows the average gain for ELL students between 4th and 5th grade.The orange line shows the average gain of non-ELL students between 4th and 5th grade. Controlling for Multiple Variables*Now we identify which students are English Language Learners

  • Regression allows us to control for multiple factors at one time in this case prior performance and ELL status. This line shows the average gain for all students from 4th to 5th grade.The blue line shows the average gain for ELL students between 4th and 5th grade.The orange line shows the average gain of non-ELL students between 4th and 5th grade. Controlling for Multiple Variables*Now we identify which students are English Language Learners

  • Regression allows us to control for multiple factors at one time in this case prior performance and ELL status. Controlling for Multiple Variables*Although this student grew slower than other 5th graders with the same pretest score, she grew faster than other ELL students with the same pretest score.

  • Now, we can control for other factors besides prior performance for Student A.Based on Student As demographics, adjustments are madeCompared to similar students district-wide, Student A has above average gainControlling for Many Variables at Once*2009 ISAT ScoreScale Score Gain, 2009 to 2010

  • Now, we can control for other factors besides prior performance for Student A.Based on Student As demographics, adjustments are madeCompared to similar students district-wide, Student A has above average gainControlling for Many Variables at Once*2009 ISAT ScoreScale Score Gain, 2009 to 2010

  • Now, we can control for other factors besides prior performance for Student A.Based on Student As demographics, adjustments are madeCompared to similar students district-wide, Student A has above average gainControlling for Many Variables at Once*2009 ISAT ScoreScale Score Gain, 2009 to 2010

  • Summary of RegressionBy measuring the impact of each student factor, the regression model isolates the impact of the school on student growth.

    In other words, some growth is explained by external factors. We can measure the average impact of these external factors on growth at the District level and subtract that impact from the schools absolute growth.

    The growth that is left over after removing the impact of these factors is attributed to the school. This is the value added by the school.*

  • Oak Tree AnalogyFor an illustrative example of regression, view the Oak Tree Analogy presentation at:

    http://research.cps.k12.il.us/cps/accountweb/Research/ValueAdded/

    The Oak Tree presentation illustrates the Value-Added model by using an analogy of two gardeners tending to oak trees.*

  • Some Things to KnowTested StudentsAll students making normal grade progression who took ISAT in both the previous year and current year are included in analysis.

    Mobile StudentsMobile students count towards the Value-Added score in each school they attended, but are weighted in the analysis by the amount of time they were in the school during the year.

    English Language LearnersELL students in Program Years 0 through 5 are excluded from the analysis. This includes students who were in PY0-5 during the pretest year, even if they have since exited the ELL program or moved to PY6.

    Students with DisabilitiesIEP status is differentiated by type of IEP. For example, the impact of a severe and profound disability is considered separately from the impact of a speech and language disability. *

  • Value-Added ScoresValue-Added measures the difference between the growth of students at a school and the growth of similar students across the District.*

  • Standardization of ScoresGrowth on the ISAT is measured in ISAT scale score points

    However, one ISAT scale score point of growth is more difficult to obtain in some grade levels than others.

    As a result, standardization is used to ensure that all Value-Added scores are on the same scale.*Student A grew by 35 ISAT scale score points

  • Standardization of ScoresStandardization is a common statistical process. In this case, it is used to convert ISAT scale score points to a standard scale.

    The unit of measure is the standard deviation which is a measure of distance from the mean.i.e., how much does School As score deviate from the mean?

    This places