In this they aim at going beyond capturing loads of data and reporting on them, but using the data to build a predictive model for student success and acting upon it. The model is based on linear regression using both real-time data from technologies (clicks, log-in times etc.) as static data and socio-economic data (former grades, age etc.). They obtained a nice correlation rate of approx. 60%.
Note: They were some concerns about the validity of using linear regression here since some variables may have a non-normal distribution. Non-parametric methods may be better.
An important rationale for building the model is to improve student learning, by providing timely and accurate warnings for students at risk (yellow and red lights) and suggest actions to tutors and learners. A crisis of under-prepared freshmen in the States (that sounds familiar) underscores the need for such a warning system. Economic reasons are not far away as well, since dropped-out students cost the university in terms of recruitment and marketing money.
Preliminary data from 2 years show a positive impact on retention rates. Students seem to like the additional feedback, although students at risk do not always respond to the warning signals provided. Privacy doesn't appear to be an issue for many students. They may well turn out to be less worried about providing personal details and technology making use of their information than the older generations.
The tool was acquired by SunGard and can only be integrated in BlackBoard, reducing its potential use. The system also assumes that most learning takes place within a learning-management system (LMS), making it less useful in a distributed environment where learners use multiple on-line and off-line tools for their learning. Getting all types data from various departments and sources together in a harmonized format also proves challenging, and makes one curious for a cost-benefit analysis of the programme.