Data: Student Tracking And Retention (Next Generation): STAR-Trak: NG
Review own retention strategy (whether explicit or implicit) and create a sense of urgency around the need to provide solutions that will assist in its success
Develop a canonical data model for the domains you are interested in. It surprised us that even within the same department colleagues had different interpretations of data ! We have not formally developed such a model, but have laid the foundations for its development (which is the subject of a different programme) through workshops.
Understand what data is critical to understanding retention. For us it is a subset of the student record data (such as demographics, entry qualifications, whether they came through clearing) and attendance data. We suspect that this data will give us around 90% of the information that we need. The other activity data is almost the icing on the cake – but clearly we need to evaluate this over time.
- our domain knowledge was insufficient,
- the metaphor we were using to identify students at risk was not best- suited to the task,
- the focus of the application was wrong
- we needed to put the student in control of the viewing of their data to maintain appropriate data privacy,
- we need to work on the ease of use and intuitiveness of the application
Internal domain knowledge: we assumed that the business (including IT) understood its data. However, while all parties did understand their data, they all had a more or less different understanding! As we are trying to develop an application that will be useful across the sector, we also had to make intelligent guesses about how other HEIs might in practice construct their ontologies. It was outside of the project scope to investigate this formally, however informal contacts were useful in this area.