How you do in a specific intro course correlates with subsequent success
Perhaps gen eds build skills and reflect capabilities and future success
Results of data analysis used to increase advising staff which increased student success
Use all your activities around campus to monitor how you are doing
Ethics
Do students know data is being collected? how it is being used?
Security of data systems? Could data be accidently released?
How are results used?
Are results used to fix an issue and help, or make the university look good?
Should data be analyzed and used?
How much should we be tracking and using real-time data?
Additional ideas from Sakai responses
A positive predictor doesn’t guarantee a result for a single student
Sometimes the results don’t match our personal observations, possibly because individuals don’t conform to averages.
But we should keep in mind that anecdotes are small samples compared to big data analytics
The underlying reasons for not doing well in a “predictor” course might be complicated. Does the reason for a pattern matter? It might influence the response.
Assuming students are informed they are being “tracked”, how are security / privacy concerns handled?
Does social belonging really matter? What’s the evidence?
What about the potential for use of data that is not purely beneficial for students?
Sometimes analystics results don’t match intuition
Do analytics and intervention prevent human decision making?
How results are used can be be beneficial or harmful
Even with all the data, it’s the human interaction that has an impact