We are currently trialling a simple system designed to help teaching academics identify students who may be at risk of failing. We are hoping to identify struggling students earlier than we previously could so as to more efficiently target support and academic interventions. Basically we are amalgamating data from the student information system (SIS) and the learning management system (LMS) which in our case, is Moodle. At my university we have a large array of academic disciplines and students can study via a number of modes other than online or distance learning. This makes predictions based on previous student results, student demographics and student patterns of online behavior from the LMS very difficult at best. Our thinking, based in complexity science, is the information needs to be directed to the point and time where it can best be used to influence the outcome. In our case, and for this particular trial, the point of need sits with the academic teacher.
The following is a ‘dummy’ screen grab from the system which is explained further down.
- Mail merge, simply allows the teaching academic to select multiple students in order to send a personalized email.
- Prior fail indicates whether or not this student has failed this particular course previously.
- Pass rate is the number of courses this student has passed out of the number they have attempted.
- Load is the number of courses that this student is attempting this term.
- GPA is a self explanatory.
- Clicking on the student name takes the teacher to another page with more details on the students academic history.
- Week. This is arranged by weeks of the term. (Moodle courses at my university are made available to students two weeks prior to the official term start). The numbers in these columns are simply the number of clicks that these students have made within the course site during that week.
As the title of this post suggests, this is the easy part. Once a student has been identified by the teaching academic as potentially being at risk of failing, then what? The reasons that students fall into the ‘at risk’ category are as extraordinarily diverse as our students. Some may be struggling academically, some financially or personally, and some (like me) are just struggling for time. Every student’s situation is different and a one-size-fits-all approach to intervention is not going to work. This is why I continue to be fascinated by the extraordinary effort universities are putting into refining their ‘at risk’ student identification algorithms. Accurate statistical models are all well and good, but all rather pointless without an intervention strategy that is capable of dealing with diversity and complexity.