Welcome to Schools Count!
Schools Count is a free, four-semester, graduate-level, online course sequence in educational data analysis for school-based data teams.
Schools Count, in the first semester, will teach you the statistical skills to answer the following questions for your school:
- Measuring Academic-Achievement Gaps (Racial, SPED and Socieconomic): Where are the gaps, and are they growing or shrinking?
- Identifying At-Risk Students: What are the educational risk factors, and how do we detect them early so we can intervene early?
- Analyzing Pattern Breakers: Who are the students succeeding despite risk factors, and what can we learn from their success?
Schools Count Mission: For every school, a data team. For every data team, a diverse group of educators (including special educators, physical educators and art educators) who have the skills and knowledge to answer their most pressing educational questions about teaching and learning in their school using their school's quantitave data.
Schools Count is . The site should be fully functional by September, 2013. "Fully functional" means that a school data team with no data-analytic experience can begin analyzing its own data in the first week, Unit 1, and continue progressing one unit per week through the Intermediate Semester. In the meantime, feel free to check out anything and everything.
Getting Started (3 Steps)
- Watch the Unit 1 lecture, here. The Unit 1 lecture is the longest of all, clocking in at 211 minutes. (The Unit 2 lecture is more typical at 97 minutes.) I never said that this course would be easy! Good teachers say to their students, "This is easy. You can do it." The best teachers say to their students, "This is hard. You can do it." I'm working to be the best teacher I can be, as are you. This is hard. You can do it.
- Obtain a dataset, which can be from a database, spreadsheet or gradebook from your school. The dataset should be structured like a gradebook in that it has one row per student. The first few columns are for IDs and/or names. Then, there are columns for things such as scores, grades, sex, race/ethnicity, effort, conduct, attendance or anything really. Save the dataset in .csv format (comma-separated value format), which is a very simple format standard across platforms. One way to save a dataset in .csv format is to open up the dataset in common spreadsheet software (e.g., Microsoft eXcel), and use the "Save as..." menu. (If you cannot obtain a dataset, you can use a provided dataset, but then your work products will be academic exercises instead of -practical resources for your school.)
- Download, install and use free, open-source R and
Rcmdr to analyze your data set.
- Learn how to download and install R and Rcmdr, here.
- Learn how to open your dataset in R and Rcmdr, here.
- Learn how to clean and recode your dataset, here.
- Learn how to analyze your data set using R and Rcmdr, here.
Note that you do not have to take the entire four-semester course to learn a heck of a lot. The first semester alone is a complete introduction to educational data analysis. The first two units alone are a complete introduction to exploratory data analysis.