STAR-Trak: NG developed a dashboard, which amongst many other things, allows staff and students to see how their actions compare to other members of the same cohort and correlations between those actions and later outcomes (module and degree results). The aim is to provide an early warning to both staff and students and to enable appropriate interventions that may boost students' performance. To do this they look at each student's performance and compare it against those of the whole cohort using a set of criteria to weigh the different aspects of this. Students can both view the results on their personal dashboard as shown below and, if they want, receive email warnings when their status changes together with weekly reminders of their status for each module.
Personal dashboard from STAR-Trek: NG
Tutors are also able to look at the overall performance of students on particular activities, and then drill down and see who has, and has not been active.
Engagement in studies: use of VLE and attendance at lectures
Engagement in studies: use of VLE and attendance at lectures showing attendance figures for Sunday Week 2.
In order to determine students' status it is necessary to configure the system to understand how important various attributes of performance are. For instance, missing a single lecture might not be very significant, whilst missing two consecutive ones might be a much more significant indicator of concern. Similarly, missing a recent lecture might be significant, but four or five weeks later this might be of only marginal interest. The criteria will vary between courses. For instance in distance courses the use of the VLE may be more significant than for campus based courses. They have therefore developed a tool that allows lecturers to specify the importance of each type of learning activity and how that importance decays with time. It is likely that there will be a need for a set of standardised templates for different types of course and different subject areas and that significant training will be required to correctly configure the tool. Equally, it may be possible to use historical data to arrive at appropriate criteria where modules have been run sufficiently long with enough students to ensure that the data is meaningful.
STAR-Trak: NG configurator tool