Predictive Analytics in the Education Sector
Today enabling a data-driven approach to improving student & pupil experiences and better utilisation of educational resources is vital.
In recent years leading schools and universities have had to become data-led. Market competition means a far greater focus on rankings for research, student services, employability and the influential National Student Survey. As well as the on-going need to provide relevant curricula that will ensure graduates are correctly skilled to find jobs and enter the workforce.
Maintaining consistent academic growth and accurate records aren’t just priorities for modern educators – they’re necessities. More personalised learning plans based on individual data, optimised timetables and resource allocation throughout the student journey are becoming the norm.
Read our Higher Education whitepaper: Using Data to Support Strategic Decision Making in Higher Education
Improved Student Retention
Some universities are now improving student retention with data with data-driven analytics. With the use of predictive analytics, universities can track students’ progress on their course and assess the likelihood of them being successful versus dropping out. If certain students are flagged early on, advisors and professors can reach out and attempt to correct the course before it becomes a problem.
Better understand student insights
Generally, at the end of the year or semester, students
are asked to provide feedback about their classes and lecturers. This data is helpful for future lesson planning or to improve managerial efforts to highlight poor performers within a college department. A big data platform makes it easy to analyse this data and find correlations and similar opinions between students taking the same classes.
Improve grading systems
The use of predictive data analytics in the education sector can also help to improve grading systems. Many education professionals have raised questions on a biased grading system– as there may be scenarios where different teachers would give different grades for the same student or piece of work. To remove such biases, universities can adopt a machine learning model to grade students which can eliminate the risk of other biasing factors, such as academic performance and class attendance.
Improved Communication Between Departments
Project Programs can have disparate data and siloed data coming from a variety of sources, which can make it hard to share information between professors and faculty staff, let alone other departments. Data Analytics can help universities increase communication and build a more collaborative culture.
Adatis were proud to host Advancing Academia – a Data and AI event designed for Higher Education institutions.
Based in Central London, the event brought together a variety of universities to showcase the latest insights in data strategy, data governance, data science and ethical AI.
We were joined by speakers from Microsoft UK, King’s College London, The University of Plymouth and experts from our own data team at Adatis, to discuss how the new digital age is reshaping education organisations and transforming their data strategies.
Read the full blog
King’s College London embraces predictive analytics with new pan-university data platform
Siloed data meant King’s was unable to get a clear picture of its operations for strategic planning. As part of a wider digital transformation, it has worked with Adatis to implement a pan-university data lake and analytics platform in Azure – creating a sustainable foundation for predictive analytics and evidence-based decision-making
Read the King's College London Case Study
King’s College London and Adatis explore how Machine Learning can help to predict Student Withdrawals
An innovative pilot project confirms the transformational impact of an Azure-based data lake and machine learning on predicting potential student course withdrawals.
‘The results of the different models from the proof of concept were well beyond our expectations. They unambiguously affirmed the potential of this solution and very quickly we moved into thinking about how we could bring it into full production and leverage the value from the insights the model provided.’
Richard Salter, Director of Analytics – King’s College London
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Whitepaper – Using Data to Support Strategic Decision Making in Higher Education
The UK’s Higher Education sector is facing challenges on all fronts—from increased competition at home and abroad, to prospective cuts in funding and tuition fees.
University leaders will need data to help them make the right decisions. By bringing together data from across the university’s different operations, they can not only analyse where the budget is spent today, but also run scenarios to understand the likely outcomes of different strategic approaches.
This whitepaper offers advice for universities on how to build a cost-effective central analytics platform to support strategic financial and operational decision-making.
Read our Higher Education Whitepaper
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