What’s Behind Predictive Analysis in Higher Ed?

Higher education institutions have had immense pressure to retain more learners in the past couple of years, especially public institutions. Federal and state officials have decided to allocate financial support for these institutions based on the number of learners annually earning a degree.

With this in mind, and because retention costs are rising for colleges, predictive analysis is a choice method for these institutions. We will discuss the concept behind predictive analysis and how it can help.

The Idea of Predictive Analysis

The predictive analysis focuses on determining outcomes based on performance and demographic data. The concept behind this method is that higher education institutions can make better decisions by acknowledging how most learners will be earning a degree for that year.

Predictive analysis also uses AI, modeling, and machine learning to assess outcomes along with the data above. 

What Are Predictive Analysis Models?

These differ in definition but also in the reliability of the predicted outcome. The decision models consider many different elements, including the potential decision itself and data that is already known and that stimulated the decision in the first place. 

These are used in business for determining rules and in higher education institutions to make a curriculum that can be suitable for many learners.

Descriptive models go a long way in terms of arranging learners into a couple of different groups—this is of great importance, especially when it comes to those that are a minority, given the fact that state and federal officials put pressure on institutions for more degrees to come from these groups. 

Lastly, predictive models take a particular learner (unit) and the characteristics of this sample and use those elements to predict a potential outcome.

Register Learners That Need to Be Advised

One of the significant advantages of using predictive analytics in higher education is that it can positively affect the caseload ratio versus full-time advisors at colleges. 

Certain learners need more individualized attention, so using predictive analysis to determine who they are and thus increasing their chances of getting a degree is something colleges are starting to do.

Adaptive Learning

Another advantage is that predictive analytics can help professors determine the areas that learners struggle with. By doing this, the classroom will be capable of quickly going through what has already been learned and investing more time in adaptive learning platforms. 

Conclusion

There is no doubt that predictive analysis holds massive potential for higher education institutions. However, for it to be successful, colleges should be using all of the aforementioned models, including predictive, descriptive, and decision models, to ensure precise assessments.

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