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 work force.
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
Universities can capitalise on data to help with one of their business problems – student retention. 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 student taking the same classes.
Improve grading systems
The use of data 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 like academic performance, class attendance, etc. which professors may consider.
Improved Communication Between Departments
Project Programs can have disparate data and siloed 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.
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 Kings College London Case Study
White Paper – 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 them make the right decisions. By bringing together data from across the university’s different operations, they can not only analyse where budget is spent today, but also run scenarios to understand the likely outcomes of different strategic approaches.
This white paper 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 White Paper
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