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Using Business Process Analytics to Leverage Insights from Student Journeys

Prof. Aditya Ghose

University of Wollongong


There has been considerable research (and industry activity) on the problem of business process analytics over the past two decades. These results have important applications in understanding student experience in journeys through - for instance - a university degree, but these have received very little attention.

The original incarnation of this problem was in the form of process mining, where the intent was to reverse engineer (or learn) the best- t process model from execution histories recorded in the form of a process log. Each entry in a process log records the task done, the time-stamp when it was done, and sometimes who did the task (the resource). Process mining technology is now mature, and its use in business computing quite routine.

More recently, there has been progress on the more general process analytics problem where the intent is not only to mine process models from data but also task post-conditions, goals, optimal resource allocations and so on.

The application of these technologies to understanding student journeys offers many tantalising prospects. Examples of questions that could be answered include:

1. What are the common pathways taken by students pursuing a given degree?

2. What is common to pathways that lead to successful completion? This will provide pointers to the kinds of advice that can be given to students.

3. What is common to pathways that lead to negative outcomes (students dropping out, failing etc.)? These can serve as anti-patterns. Monitoring for these anti-patterns can be used to flag potential              problems in specific students.

4. How might we correlate pathways with student goals?

5. If the current partially-complete journey of a student suggests that the outcome will likely be less-than-satisfactory, what is an optimal x that improves the likelihood of a positive outcome?

6. What are the optimal resources to deploy to achieve a positive outcome (a student might enjoy working with a given instructor, hence including subjects taught by that instructor might help)?

7. Much of the hard work involves mapping student milestones to task completion events in the process log.  Ultimately, these technologies can significantly improve student outcomes.

(A copy of this presentation is unfortunately not available).