EvaluationDashboard.com

With spring semester in my rear view mirror, I found some time to use the maptools package to make a proficiency map that can be displayed in Google Earth. It’s essentially a choropleth map in Keyhole Markup Language (KML) format. Google Earth takes visualizing educational outcomes to a whole new level. Distributing proficiency maps in KML format would make it easy for parents, school district employees, policy makers, and students themselves to explore their district’s test scores and those of nearby districts. Additionally, KML proficiency maps could help evaluators of educational programs involve stakeholders and frame questions.

Try it for yourself. You can click on the image below to explore Minnesota’s 3rd grade math proficiency results with Google Earth. After you’ve gotten your fill of zooming around the map, try the following:

• Click on a district to activate a pop-up window containing the district’s name and results.
• Click on the “+” sign next to “2007 Minnesota Comprehensive Assessments (MCA-II)” under the Places sidebar. The district results will expand downward, showing results in tabular format with map links. Double-click a district name in the sidebar to zoom to that location.
• Use the Search -&gt Find Business section of the sidebar to find an after school tutoring program in a district of your choice.

Minnesota school districts: Math proficiency among third graders

Please leave a comment if you have any feedback about this approach.

Each year the Department of Educational Psychology holds Graduate Student Research Day to showcase research conducted by its graduate students. About 90 people attended this year. I was one of six students who delivered a presentation. My fellow presenters–Ruth Swartwood, Cengiz Zopluoglu, Alicia Ayodele, Ben Seipel, and Breanne Byiers–all did an excellent job. Many other students shared their research via posters, all of which were outstanding. My presentation, “A spatial analysis of school district performance in Minnesota, demonstrating spatially enabled evaluation,” consolidated some of my earlier posts to this blog and summarized the paper that I am scheduled to present at the American Educational Research Association (AERA) meeting in San Diego. By looking to other social science disciplines that have become spatially enabled, I identified some ways that applied educational researchers can make better use of geographic mapping and spatial statistics. For example, we could enhance survey data and minimize respondent burden by spatially referencing the primary data and joining secondary data from the Census Bureau. Extending the promising uses, I spatially analyzed school district performance in Minnesota in 2007 and found the following.

• Neighboring school districts influenced performance to a small degree in limited instances (i.e., reading proficiency of third graders in poverty).
• Several school districts were found to have outperformed their neighbors, including Randolph, Medford, Orono, and Royalton.
• Six clusters of adjacent, correlated school districts exhibited low reading proficiency, suggesting they should collaborate to improve preschool and early elementary reading efforts.

You can click on the image to access the full presentation:

If was a band, and if that band had a greatest hits album, and if the album featured R commands instead of songs, then the following commands should be counted among the greatest hits (or the most obscure hits).

• windows(record=T) #place this near the top of your script to record all charts; enables scrolling through and compare charts with page up and page down
• oldpar <- par(no.readonly=T) #assign default graphical parameters to an object, to be called back later with par(oldpar)
• windowsFonts(Garamond = windowsFont(“TT Garamond”)) #add a new font family to the standard database
• file.choose() #allows you to choose a file interactively instead of specifying the path and file name in quotes
• describe() #a better alternative to summary(); from library(psych)
• qq.plot() #plots a quantile-comparison chart with confidence bands; in library(car)
• prplot() #a function in library(faraway) that produces partial residual plots
• Make.Z() #normalizes all variables in a data frame, available in library(QuantPsyc)
• ci.rc() #calculates a regression coefficient confidence interval, in library(MBESS)
• hccm() #another from library(car); calculates heteroscedasticity-corrected covariance matrices for unweighted linear models
• lmer() #a function in library(lme4) that fits linear mixed models
• identify() #use this function in library(graphics) to label points in a scatter plot