The 2016 From Bricks to Clicks: the Potential of Data and Analytics in Higher Education report highlighted the enormous opportunities available to higher education organisations if they can make sense of the data behind all of the student information they collect.
It suggested that all universities should consider introducing a learning analytics system so they can respond to changing students’ needs and the growing trend to see ‘students as customers’. Every time a student interacts with their university – be that going to the library, logging into their virtual learning environment or submitting assessments online – they leave behind a digital footprint. What is learning analytics, what makes up a student’s digital footprint and what could the future hold?
What is learning analytics?
Analytics is the systematic computational analysis of data or statistics. Learning analytics is the process of using data to improve learning and teaching and enabling students to discover or learn something for themselves, for example it can provide insights into how and when an individual likes to learn.
So learning analytics helps students and practitioners to monitor progress, and discuss any changes or problems that might put the student at risk of dropping out. It can also help the practitioner tailor and personalise their approach in supporting retention, satisfaction and attainment.
A student’s digital footprint
A typical student has many touch points with an institution. As all of this data is collected learning analytics can allow institutions to monitor activity and interactions at all touch points and understand the patterns, relationships and trends in the data to create predictive models.
If you can predict student outcomes then you can set meaningful targets for students. If you can identify students at risk of dropping out, then you can monitor and check they are performing as expected. Once you have the data behind the student information you can share the findings with key teams across the institution so effective interventions can be made and it can inform and manage the entire student lifecycle.
At Kent, for instance, the university will be able to see when students are using the library, whether they’ve been logging onto the VLE and if they have they been submitting their course work on time. “We have nearly 20,000 students, and much of the university operates in siloes. So you need to have intelligent ways to be able to spot the students who are struggling,” says Nicholas Thurston, senior projects officer, University of Kent.
Using learning analytics across the student lifecycle
Analytics has been recognized as a key differentiator for the private sector that is helping organisations understand their customers better. In the same way, learning analytics can collect a great deal of data about students and what those students are doing and it can be used as a key differentiator for institutions, enabling them to understand students and their needs much more clearly.
Learning analytics can be used for much more than just monitoring progress, or identifying students at risk of dropping out. Let’s look at some key stages of the student lifecycle and how learning analytics can help make sense of the data behind your student information.
There is increasing competition between institutions to find students that are the right fit. Learning analytics can help institutions to understand the patterns in enrolment and optimize student attraction, recruitment and progression.
It can bring into focus what is best for a student’s success so informed decisions on recruitment strategies can be made that increase enrolment numbers and secure revenue. By understanding which types of students enrol at an institution can help identify which groups of students to target with future marketing and recruitment activities.
Students expect a seamless support experience. As the education and technology landscape changes it can be a challenge for institutions to deliver the levels of support that students’ experience elsewhere.
Effectively supporting students can improve student retention and learning analytics can identify students at risk of not meeting expectations and suggest interventions that optimize student outcomes and target precious resources much earlier than would otherwise be possible. By monitoring and tracking which interventions are successful these can be implemented for future cohorts of students.
Learning analytics can help institutions to understand the skills and qualifications required for the labour market, monitor student destinations and reasons for gaining successful employment.
This information can then provide students with expected salaries, job opportunities and prospects following the successful completion of their chosen course and the skills and qualifications required for a career in that field. All of this provides powerful evidence of the results achievable for future cohorts of students.
What could the future hold?
Learning analytics can enable us to go beyond the MIS and use all the data collected across the entire education lifecycle to inform institutions which students are the right ones for them, and inform students which career is the right one for them.
Driven by analytics and historical data it can inform recruitment strategies, plan curriculums, predict student outcomes, intervene earlier, set meaningful targets and ensure future student outcomes.
So for students at school, what career choices do they make? Do they pursue further education, or vocational training? Learning analytics can inform the choice or next step in their education journey.
For students at college once a qualification has been achieved do they continue with further studies at university or enter the world of work? Learning analytics can identify which course, college or qualification is the best fit for the student and help institutions ensure students are competent and ready for work. Historical data from the sector, jobs, and employers that students are matched with can be used to provide evidence and predict typical outcomes for the curriculum or course a student is studying.
For students at university they can assess their options. Is it worth doing a placement? What are the outcomes of this based on previous student choices? Will it help them gain the employment they seek?
Should they take a gap year? What will the outcome be if they do take a gap year based upon historical data for that subject or course?
Learning analytics can make sense of the data to inform decisions and student outcomes. It could be used to provide students with expected salaries, job opportunities and prospects following completion of their course. At each stage of the student journey it could identify students at risk and intervene to ensure successful outcomes and that students are competent and ready for work.
There are certainly enormous opportunities available not just to higher education organisations but all education establishments if they can make sense of the data behind all the student information they collect. Without learning analytics to understand the data, student information is nothing. Data is everyone’s responsibility, and can be everyone’s asset.