Data Modeling Student Retention Series


I am going to be doing a series of blog posts on data modeling student retention in higher education.  I recently saw a presentation which made me think a lot about data modeling in general and more specifically about student retention.  This series of blog posts will document the process my team is using to determine if the data model I observed is appropriate for our institution.

The first step in creating a data model is understanding the business process.  I will start by laying out the rules and vocabulary when it comes to student retention.

Student retention in higher education is based on an academic year.  New students starting in Fall are "retained" if they attend school the following Fall.  If a first time in college (FTIC) student attended school for the first time in Fall of 2010 and also attended in the Fall 2011, they were retained.

The next important business concept and term is cohort.  For the purpose of student retention a cohort is a group of FTIC students which started in the same Fall term.  Unlike an academic cohort, it is not necessary for the students to be taking the same curriculum.  Therefore, an FTIC student attending their first Fall semester in Fall 2010 will be part of the Fall 2010 Academic Year Cohort.

The rules for which cohort a student belongs to or even if they belong in a cohort get much more involved. For simplicity's sake I will enumerate them below:

1- A student must be a First Time in College (FTIC) student.
2- A student's first semester must be in the Fall or the Summer immediately preceding their first Fall.
3- A student is retained if they take classes the following Fall.
4- Once in a cohort, missing a Fall semester does not drop the student from the cohort but does effect the retention  numbers.
5- The reasons a student would be removed from a cohort are military service, death, permanent disability, left to serve in the Armed Services, left to serve in a Foreign Aid Service of the federal government (such as Peace Corps), or left on a Official Church Mission.

Data Modeling Tip: Handle complexity in the ETL/model building process.  Do not expect end users to remember and adhere to business complexity in the query or in the semantic layer.



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