Predictive analytics is invisible, yet its benefits are highly tangible. The use of predictive analytics can bring a range of advantages to all educational settings; the ability to easily access and interpret results and attainment data is integral to any institution’s forward-planning. A holistic, systematic approach to using data will unquestionably drive improvements to standards across the whole institution.
What are the benefits of predictive analytics in education?
Taken from our ‘Guide to Predictive Analytics in Education’, this blog discloses some of the game-changing benefits of predictive analytics in Higher and Further Education.
1. Identifying and supporting students at risk
Predictive analytics can help to model student behaviour, flagging and predicting issues with student attendance. Institutions can answer questions about how students engage with the campus and wider services, and how this correlates with student outcomes. By modelling existing attendance data, institutions can identify those at risk of dropping out of courses or programmes. The insights can be used to ensure support, time, and resources are given to students most at risk of dropping out.
For example, Tribal reviewed student information, attendance data and online resource activity for a higher education organisation in England to identify students who might not return for a second year. Of the 652 students who were identified as high risk, 517 of these predictions were correct and unfortunately withdrew before the next academic year. From this, we can identify that timely intervention is a vital part of improving attendance, which is integral to student performance - and the better the student performance, the more significant the positive impact will be on the institution’s reputation and ranking.
2. Improving completion rates of apprenticeships
A common issue for apprenticeship training providers, in particular, is a high proportion of learners leaving before completion. Using historical data, providers can create models to understand which current or future learners may be at risk of dropping out of the programme, providing targeted support and intervention to learners most at risk of dropping out. Learner completion rates are integral to funding considerations, and as such, the ability to address and improve these rates is a clear benefit to using predictive analytics within the apprenticeship.
3. Oversight of the entire institution
Data can be collected and analysed from all areas of the institution, blending data from existing student management systems with other student engagement data. Blended data can help to place student performance within the context of the entire organisation. Engaging with these data sets early on gives institutions a fully rounded view of the student experience, beyond just the academic data. By creating predictive models, institutions can understand how all areas of student experience influence student outcomes, ultimately encouraging a holistic approach to improving education provision and the organisation as a whole.
4. Strategic decisions and budgets based on concrete evidence
Enrolment, exam results, attainment, and attendance are all critical measures of an institution’s overall success. Predictive analytics can be used to improve the quality of courses and modules, help support academic performance, and strengthen the student and staff experience as a whole. However, the consistency of data is of the utmost importance here.
For example, one school cannot categorise data in a conflicting way to an alternative school as it will naturally impact results and the reliability of data. Predictive analytics provides a clear benchmark to measure the success of any change. Reports and visualisations help to keep improvements on track and inform best practice change management. This evidence can be shared with key stakeholders as a way of gauging improvements in the institution. Data analytics ensure educational institutions are making evidence-based decisions to achieve continuous growth at a business level. Both investment decisions and efficiency savings can be powered and supported by data-led trusted evidence.
5. More efficient services by understanding trends usage
Education organisations can model the use of classrooms, services and equipment using predictive analytics to better understand campus trends. Tracking peak surges or drops in the use of individual classrooms, computer suites or library services can make site management more efficient, helping institutions know when and where to invest resources for the best possible services. Likewise, predictive models can highlight underused services which might need extra support or development. Maximising service efficiency is a core aim of all educational institutions, allowing for greater investment in the student experience.
6. Continuous improvement in the quality of education
Data analytics plays a crucial role in both monitoring and improving the quality of education provided in the classroom. Data can highlight problem areas for cohorts or flag struggling individuals, ensuring students receive high levels of personalised support, reacting to needs and requirements on an individual basis. Analytics can ensure programs are consistently adapted and improved, highlighting areas requiring focused improvements to learning resources. Analysis might identify, for example, a module with consistently low results from a student cohort, demonstrating the need for further improvement.
7. Enhanced recruitment and enrolment
A model to understand student trends in applications and enrolments can help institutions properly allocate resources for maximum results; enrolment data can help institutions identify the peak times for both applications and enrolment, predicting future surges. Once benchmarked, data can show return on investment of recruitment campaigns, and enable institutions to set clear, achievable goals. They can understand any risks in their current approach and map future campaigns on historical data. Any insights in this area are vital for creating a sustainable source of new students and to ensure recruitment targets for students such as international or disadvantaged are on track. Accurate forecasting of the number of future students directly informs strategic decision-making. Predictive analytics can also provide insight into the kind of student attracted to the institution. Institutions can subsequently alter enrolment and recruitment campaigns to target similar students or attract a more diverse audience. This data-led approach can make the enrolment process more efficient and cost-effective.
8. Monitoring remote delivery
Schools, colleges and universities have all had to increase remote digital delivery due to the impact of the COVID-19 pandemic. In this setting, it has become harder for teachers and lecturers to closely monitor student performance in the same way they could in a traditional classroom setting. Data analytics helps bridge that gap, giving teachers and lecturers insight into student engagement and access to digital learning materials and courses. It provides an overview of the progress of individuals and cohorts, helping the faculty to understand any educational areas for attention. Social distancing rules have severely impacted more traditional ways of gaining student insights. As face-to-face interactions are lowered, staff may find it harder to ‘read the room’ and gain everyday insight that can explain student performance.
This may also impact attendance to lectures or access to resources, which are harder to monitor physically. In this environment, the need for reliable, accessible and trusted data insights is vital to make sound decisions. Once set up, this evidence-based approach to monitoring and improving programme delivery can be continued in the post-pandemic world.
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