Supporting student success

Enhancing the student experience and supporting student success are increasingly important for all colleges and universities. There are many ways in which this can be done, and here we will look at some of the ways that activity data can be used to support students. There are two key ways in which activity data can be used to support students
  • Identifying good and poor patterns of online behaviour and supporting students to improve their performance. This is often called academic analytics.
  • Personalising the ways in which they access and use online resources based on their activity.
Supporting study skills
Students vary hugely in their study skills, with some arriving at university with excellent independent learning skills and others needing much support to achieve appropriate study skills. Whether in the virtual world or the real world there are patterns of behaviour which are more effective than others. One of the purposes of higher education is to support students to achieve effective study skills. If we can identify effective patterns and ineffective patterns by the traces that they leave in the log files and matching these to outcomes then we can help students to enhance their learning. There are a number of advantages to using activity data to support improving the experience. These include:
  • The work can be undertaken fully or partly automatically. The application can look at all the information automatically and flag up students with patterns of behaviour that give cause for concern to either the tutor or the student, possibly with suggestions of things that they can do to help themselves.
  • The system can look across all the courses or modules that the student is taking. Activity that might not cause concern if limited to one course might be of concern if it is occurring in several of them.
  • Because the system is looking at very fine grained data from a number of sources it may be able to spot issues or problems earlier than a tutor would and so through earlier remediation prevent problems from becoming greater.
The STAR-Trak: NG project at Leeds Metropolitan University has developed a system that allows students and staff to view their attendance and their use of the VLE in comparison to measures of performance so that they can see if they are high / medium or low users.
On a slightly different note the LIDP has been demonstrating the relationship between the use of library resources and degree outcome.
For project details see:
Personalising interactions
The other key way in which activity data can be used to support student success and enhance the learning experience is to personalise the information that students are presented with. In the introduction to this section we saw that Amazon uses the results of searches to offer additional material that their customers may be interested in buying. In a similar fashion the SALT and projects are using searches undertaken by other people to provide users with resources that may be of particular interest to them that they might not otherwise find. This can help to increase the number and range of resources that students. However, care is needed when undertaking personalisation as students wanted to understand how the recommendations are derived. For instance the students were interested in the grades of the students making the recommendations, as discussed by focus groups undertaken by AEIOU.
For project details see: