Data Visualization and Student Growth Percentiles

Northeastern Educational Research Organization · April 17, 2013

Data Visualization and Student Growth Percentiles — Northeastern Educational Research Organization, April 17, 2013.
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April 17, 2013

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  1. Data Visualization and Student Growth Percentiles Damian W. Betebenner National Center for the Improvement of Educational Assessment Dover, NH 2013 NEERO Annual Conference, Portsmouth, New Hampshire April 17th, 2013 Damian W. Betebenner Data Visualization and SGPs
  2. From a Paucity to a Plethora of Data The Economist: The Data Deluge Historical records of student achievement Historical records of student demographics, teachers, schools, educational programs, . . . Stakeholder interest in an examination of this longitudinal data Interest in examining student achievement over time (student growth) derives from data availability. Damian W. Betebenner Data Visualization and SGPs
  3. Describing Student Growth Measuring student growth, even with a vertical scale, is not a simple task. Some believe a vertical scale simplifies the task of measuring student growth. Even with an interval (or ratio) scale, growth is not easy to interpret. Consider, for example, height. A child might grow 4 inches between ages 3 and 4. 4 inches is a well understood quantity. The 4 inch increase becomes really meaningful only when understood alongside the growth of other 3 to 4 year olds. Student growth percentiles were developed to provide a normative context for describing student growth. Damian W. Betebenner Data Visualization and SGPs
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  14. Student Growth Percentiles Should we be surprised with a child’s current achievement given their prior achievement? Given a student’s prior scale scores and the associated conditional density, their current scale score corresponds to a percentile of that conditional distribution. This percentile is the student’s growth percentile. Growth percentiles are closely related to estimating the probability of observing a student’s current achievement taking account of their past achievement: Pr(Current Achievement |Past Achievement ). As such, growth percentiles describe the rarity of a student’s current achievement conditional upon their prior achievement. Damian W. Betebenner Data Visualization and SGPs
  15. Student Growth Percentiles Should we be surprised with a child’s current achievement given their prior achievement? Student growth percentiles answer this question. Consider a low achieving student with 50th percentile growth and a high achieving student with 50th percentile growth. The low achieving student grew at a rate exceeding 50 percent of similar students. The high achieving student grew at a rate exceeding just 50 percent of similar students. The low achiever’s growth is probabilistically as rare as the high achiever’s. Equally rare does not mean equally good. Judgments about the adequacy of student growth require external criteria. Damian W. Betebenner Data Visualization and SGPs
  16. Model for Student Growth Percentiles Student growth percentiles are calculated using quantile regression with B-spline smoothing, a generalized additive model. Quantile regression is used to model the complete distribution of the response variable (current achievement) B-spline smoothing is uses to accommodate non-linearity and heteroscedasticity of the data. The model/method is descriptive and all about data fit—it’s a data mining procedure. The model quantifies distance = rate · time probabilistically. Damian W. Betebenner Data Visualization and SGPs
  17. Model for Student Growth Percentiles Conditional Decile Regression Curves Conditional Decile Regression Lines 2006−07 Math: Grade 5 versus 6 2006−07 Math: Grade 5 versus 6 1000 Unsatisfactory Partially Prof. 1000 Unsatisfactory Partially Prof. Proficient Proficient Partially Prof. Proficient Advanced Partially Prof. Proficient Advanced Grade 6 Scale Score Grade 6 Scale Score Grade 5 Scale Score Grade 5 Scale Score Damian W. Betebenner Data Visualization and SGPs
  18. Software for Student Growth Percentiles Student Growth Percentiles and Percentile Growth Trajectories are calculated using the open source R Software Environment [?] and the open source SGP package [?] The SGP package is hosted on GitHub and multiple state analysis sites are present there. See https://github.com/SchoolView. In working with state departments of education, the goal is to build state capacity so states can perform and validate their own analyses using software available and easily used by the state. Arizona, Colorado, Georgia, Massachusetts, Idaho, Indiana, Wisconsin are four states (that I know of) who have state staff capable of generating the student growth percentiles together with aggregations that follow from their calculation. State ownership/oversight of the SGP calculations has been a big plus in terms of buy-in from the state, enhancing their ability to explain and work with local stakeholders. Damian W. Betebenner Data Visualization and SGPs