As Educational Institutions continue to look for ways to improve their value offering, Big Data and Predictive Analytics have quickly become influencing factors in improving the campus experience for students, both academically and socially. In this feature article, we discuss the role Predictive Analytics and Big Data play in modern education.
What is Predictive Analytics?
Predictive analytics is a form of advanced analytics that encompasses a range of statistical techniques and makes predictions about the future or other unknown events based on the information (Big Data) provided. For educational institutions and organisations, these predictions can be generated based on enrolment, facility-use, class engagement and more.
How Predictive Analytics Helps Higher Education
Having switched focus from maximising semester enrolment numbers to increasing the rate of successful student graduations, Educational Institutions are under increasing pressure to retain and develop students. This change in approach is powered by the state officials demand for students who enter into Higher Education to earn a degree and also the student and their family who are also anxious about successfully graduating due to the financial commitment involved. Bearing this in mind, predictive analytics can be used to add visibility to educational progress on behalf of students, enabling them to proactively react to their development.
3 Predictive Analytics Uses in Education
Informed Student Advising
Early Alert Systems also known as Flags can be used to identify students at-risk of falling below standard attendance levels as early as their first semester, this allows advisors and pastoral support teams plenty of time to reach out and offer students help or support.
Accurate Enrolment Management
By using predictive analytics to identify which students will most likely graduate based on their academic record, educational institutions and internal departments can better prepare for classes of high returning students and make changes to classes that receive poor attendance records.
With access to academic records, lecturers and teachers can identify student learning gaps and customise academic modules to better align with how each student learns. Predictive analytics can also help educators perform better. With feedback occurring more frequently, educators can take action immediately in order to ultimately provide richer, more informed learning experiences.
What Tools are Available?
By using a comprehensive student success platform to manage students, attendance keeping and progress reporting; data can be centralised in one location for insightful student overviews. For educational institutes, predictive analytics can be utilised to identify poor attendance levels, highlight progress and identify areas of potential student success.
As predictive analytics is based on continuous assessment, students can be encouraged to remain on-track by offering them insights into their academic history. If a student is in danger of falling behind, support teams can be called upon to ensure these at-risk students don’t get left behind.