Abstract:

A major challenge in the promotion of student success is the level of engagement and effort committed by students to their studies. The average student is largely focussed simply on passing coursework rather than aiming at more specific and higher-performance targets.

On the other hand, in all institutions, the appropriate structures are already in place for just such performance-setting. For instance, the classification of a student’s graduation via summa cum laude, cum laude, first class, second class etc. are well defined and are powerful means of incentivising student effort. Unfortunately, students remain largely oblivious to these classifications during their studies.

There are three main challenges to applying such an incentive. First, there is significant computational effort required to evaluate the current status of a student in terms of the track to graduation. Second, if such classifications reveal a lack of performance, the opposite effect may be achieved, viz. the disincentivisation from committing to further effort. Finally, such classifications may seem to be too broad to have meaningful impact on the day-to-day of student operations.

Solutions to these challenges are afforded in the application of an automated student advising system (AutoScholar Advisor). It is demonstrated that the system computes the student status and track to graduation efficiently and presents the result accurately and clearly to the student. Second, computational support affords a nuanced generation of advice to the student by focussing on smaller achievable goals rather than by dwelling on past lack of performance. Finally, the computed-but-not-presented goals are interpreted in the context of upcoming assessments as a way to generate advice that impacts on the immediate events in the life of a student.

The methods are presented in the context of application at three major Higher Education institutions in South Africa. Application to K12 education institutions is also outlined.

Keywords:

Student advising, automation, edtech.