For higher education institutions, the concepts of predictive modeling informed by machine learning do not differ much from human learning.
Data is fed to computer algorithms and these algorithms look for data patterns that can be attributed to a specific outcome. This is similar to how the human brain learns a subject such as algebra.
We train the brain on how to solve the basics of the equations, and from that point, we can apply this learning to solve similar equations.
The amazing thing about predictive modeling for higher education is the large number of data dimensions that can be used to determine these likely outcomes. Think how well your brain would be trained on algebra if you could do every single algebra problem ever devised.
But, like human learning, if there are events or topics we have never seen before, like COVID-19, there is more uncertainty in understanding what the outcomes may be.
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Like humans, the machines can make some predictions on outcomes, but the confidence levels go down. For example, if I took a multiple-choice test on neurosurgery, I might be able to deduce some answers based on my understanding of the English language. It is not likely I will get as many right answers as I would if I took a class.
So, using this logic in the context of today’s environment with COVID-19, one may conclude that predictive modeling for higher education may be useless because there is not much recent data related to the impact of a global pandemic on student admissions and retention. This would be a hasty conclusion for more than one reason.
From this, we will be better prepared to meet the needs of the 2020 class in retention objectives and better understand and shape the class of 2021 and beyond.
Predicting the past can be as important as predicting the future, especially when unknown events are injected into the problem. With COVID-19, it is more important than ever for higher ed institutions to use predictive analytics to understand the past to plan for the future.