Over the last six months, university professionals have been questioning whether students are ready to take on the challenge of higher education after the pandemic. However, despite the problems that occurred with A-level and BTEC results, the numbers of students in higher education are booming!
Universities might not know the true effect of Covid-19 yet and there is always some risk that students who have been given places won’t make it past the registration processes.
On the other hand, an over-supply of students could be problematic for the system. There is no longer a student number cap in England which has caused a number of universities to grow at a fast pace. This has led to a lack of student accommodation and overcrowded lecture theatres.
The large student population has also caused many students to feel lost amongst the huge crowd of peers. With so many students enrolled, individuals can easily slip off the radar before anyone notices and misses out on critical support. This has been a particular problem during the pandemic when students went for months without any face-to-face contact.
Universities have made efforts to ramp up face-to-face contact and provide social opportunities for their students. However, it is still tricky to keep the social aspect of university alive with the virus still at large.
Covid-19 has brought a wave of new challenges to the higher education space. In particular, it cannot be assumed that students will easily create relationships with their tutors and peers. This is because some students may feel overwhelmed by the act of starting conversation or socialising with new people after years of lockdown restrictions. Furthermore, some students may find it difficult navigating their way around the support systems that are available at university which could cause some students to avoid seeking support.
Universities have been using metrics such as NSS. HESA data and employability data to determine points of internal action. These sources of data are considered to be robust but tend to measure cohorts instead of individuals. There is also no option to go back in time and find the reason behind poor performance or attainment. On the other hand, learning analytics allow universities to track students at an individual level and build a picture of which students might become disengaged. There are several questions surrounding the ethics of using data in this way however, it is important to note the benefits that data collection could have for higher education.
The main issue that could threaten the effectiveness of learning analytics is expecting the data to be ‘perfect’. When this is the case, more efforts are put towards ‘perfecting’ the data than they are towards building a solution to the problems that are underlined. While this is happening, students are not getting the support that they need.
The art of imperfection
Through years of working with universities across the UK, it is evident that no progress is made from perfect data sets. The only datasets that perfection should be applied to are those that are created for purposes other than measuring student engagement, such as monitoring visa compliance or attendance monitoring.
Universities also possess a large amount of data that has a lot of noise but not much signal. For example, the submission of assignments can tell you something but does not cover the whole picture of how students are coping.
The key to reducing student withdrawal at university is not working to perfect data but instead it is having strategic conversations that create a mutually understood definition of good engagement.
Different universities, and different courses within those universities, will have different expectations of their students. These expectations need to be made clear in order to explore how data can be used to provide valuable insight. Universities should be focussed on using the data that they already have to drive action and improve student experience.
The main issue that needs to be addressed is agreeing on how student withdrawal risk should be flagged and what support should be offered to them when they are identified. All educators should understand how to go about helping a student who is flagged by the system.
The monitoring of student engagement should also be transparent. This means that students should be aware of their universities expectations and have access to their personal data. Students should also be made aware when they flagged and be able to understand why that might be.
Making it all possible
Most higher education institutions do not have the time or resources to carry out a large learning analytics projects. Therefore, it is far easier to use existing data that can give information regarding student engagement and performance. Universities must use this available data to respond to any risk of student withdrawal and understand students on an individual level.
Universities can improve their systems by adjusting the data that they already have. Overtime, educators will be able to better understand what an at-risk student looks like according to their personal data. As well as this, students may be able to use the data themselves to understand how they are doing. Instead of waiting for perfection, higher education institutions should start utilising the data that they already have and take action sooner to improve student experiences.
If you want to find out more how StREAM can help with student retention initiatives get in touch today or request a demo.
