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.

 

 

Find out how Adatis can help in the education sector.

If you’d like to know more and how to get started please give us a call on

+44 (0)203 696 9120 or send us a message