University staff look for the best ways to support you in getting good results. But it’s not always easy to know when we should offer extra help.
- Our excellent tutors are there for you when you need support in your module.
- Our student support teams (SST) are highly trained in providing study advice to fit your circumstances.
- Our IT staff have developed sophisticated systems and statistical methods to help us to be proactive in offering individual support when we think it might be needed.
The techniques of learning analytics help us to select specific types of information from the data we have and use it to personalise what we send out to students. That way you don’t get messages that aren’t relevant to you, and we try to send helpful ones at the right time.
To do this we use the data you gave us when you registered - such as your previous educational experience - along with the information we record during your studies, such as when you submitted an assignment.
Our analysis of past students also helps us to decide what might be helpful. If, for example, you’re studying several modules at once we know it’s worth getting in touch to check that you’re ok and let you know about support you might find you need.
Because we’re using your data in this way we’ve produced the Ethical use of Student Data for Learning Analytics Policy. We developed it by reviewing research on learning analytics and consulting with students through a student forum. The policy itself is primarily aimed at staff, but there’s also an associated set of FAQs written from a student perspective.
How we use learning analytics
Student data is used in three main ways: monitoring, early warning indicators and evaluating our teaching.
We try to identify students who meet certain criteria, such as submission of assignments or active engagement with their studies.
2. Early warning indicators
This approach is based on statistical analysis of past students and is an indication of the expected likelihood of something happening; it is not an absolute prediction but an indicator of how likely we think it is (based on the information we know) that a student will be successful at a particular point. No model can predict outcomes with absolute certainty, and there will always be things that affect students’ progress that are beyond the University’s control or knowledge.
However, the predictive models used combine the effects of multiple factors to create the probabilities and have been shown to provide an acceptable level of accuracy at the individual student level. We contact students who are new to study to welcome them, explain about expectations of workload, and encourage them to plan ahead. We analyse information that we hold about all of our students to help us to identify and prioritise students who may benefit from a call that can offer additional support or encouragement. It should be noted that the ethical use of the predictive data is being considered and our use of this data will meet with the university’s ethical and data protection policies.
3. Evaluating our teaching
We evaluate our teaching and learning design and technology by aggregating your data to help us make improvements in, for example, the assessment strategy for a module.