Learning Analytics (LA) identifies, collects, measures, analyses and reports data about students and their learning contexts to improve student learning experience.
Thus LA consists of two distinct parts: a robust technology to process and present intelligent and meaningful information; an effective plan to act upon the information.
In short, LA aims to enable students to learn better.
Nowadays there is a great deal of data available. It is impractical to collect and process all of them. The first step is to identify what data is useful. Research has revealed three types of useful data:
|Activity and Performance Indicators||
After the LA application has done all the intelligent work, the information needs to be presented in a meaningful, user friendly and visually appealing way. This is very important as it directly influences whether staff and students have the motivation to look at the information and take actions accordingly.
Interventions and responses
As can be seen, implementing LA effectively cannot be done by a few individuals. It requires careful planning and strategic thinking on an institutional level. Resources are needed to build, run and maintain the LA application as well as to take property actions based on the output.
- Course Signals (Purdue University): College-wide learning analytics approach
- E2Coach (University of Michigan): course specific LA approach
- Check My Activity(The University of Maryland, Baltimore County) : Student centred LA approach
“Benjamin Bloom discovered that with one-on-one or one-on-two tutoring, even average students could perform two standard deviations higher than average students who were taught using conventional group methods.”
The ultimate goal of a LA application is to personalise the educational experience and tailor resources to specific student needs which helps the students to achieve more.