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
Here the Adatis team share their latest perspectives on all things advanced data analytics