Predictive Analytics in Education: Use of Predictive Analytics in Education

Table of contents

Predictive analytics and other data-based learning systems are playing now more of an important role in school and higher education in the years ahead. Predictive analytics uses statistical analysis and machine learning to predict the probability of a certain event occurring in the future after processing a set of historical data. Educational institutions have plenty of possibilities to gather all sorts of information - schoolchildrens’ grades, assessment results, various reports to governmental institutions, psychological test results, attendance, etc. Schoolchildren, students, parents, and governments involved in the process are going to benefit from them in the terms of educational effectiveness, while universities will benefit from using this as a way of matching students with courses more effectively and showcasing strengths to a wider and more appreciative audience.


Examples of predictive analytics in education

Predictive analytics in k-12 education

School districts use predictive analytics in several ways:

  • To build early warning indicators based on students' attendance, course failure, and behavior to predict dropouts;
  • To predict on-time high school graduation and being on track in Grade;
  • To examine indicators that predict college- and career-readiness and postsecondary success;
  • Recently predictive analytics has also gained momentum in identifying and retaining great teachers.

For enrollees and parents while making a choice of a college or university.

One of the most effective ways to solve selection bias is with predictive learning and other machine learning-based recommendation systems. They help:

  • Making the multiple layers of choices easier to define and understand;
  • Reduce the risk of dropping out, or wasting time/money doing a course, or even modules that aren't suitable;
  • Define which higher education institutions are valued for money, are better for long-term career goals and earnings, and result in higher levels of student satisfaction.

Although there are various apps around - either free or cheap for parents and students - there still isn't enough data without university participation in generating data for future students and other stakeholders.

However, as this is an area of government concern, in the U.S. and other countries, it could soon be the case that universities will need to contribute data to publicly available platforms, effectively ‘open-sourcing’ university results and outcome data.

Predictive analytics in higher education

Universities and educators

  • More easily see which courses are doing well, and which aren’t, and then make corrective changes to how courses are delivered to improve educational outcomes;
  • Demonstrate the ROI of courses (e.g. learning outcomes, earning potential, and other key metrics) to appeal more widely to students and parents;
  • Alter brand bias. Newer institutions, including online ones, can effectively demonstrate why they're a good investment. Whereas older institutions can demonstrate that they're more than a well-known brand name, thereby allowing a higher level and competitive playing field.
  • Lesser-known universities can also use predictive analytics as a chance to increase name recognition and demonstrate how they perform compared to those with more established brand names.

Concluding thoughts

Predictive analytics is a powerful tool for Education that makes many processes easier and more effective. It requires the use of historical data which has to be cleaned and parsed before any analytics algorithms can be used to analyze the data. The question of a centralized system where all the educational institutions can store the data and benefit from it is open, as well as the question of availability of this data to other contributors like private institutions or even private tutors. Nevertheless, the possibility of building a predictive analytics system for a separately taken educational institution is a matter of a couple of months subject with the proviso digitized data sufficiency. For further information on a specific case, please contact our experts, we will be happy to help you with your idea or a current project.