Although higher education institutions collect large amounts of data, they’re not always able to make good use of it. Traditional data analytics tools are challenging to operate and can be extremely unintuitive.
This means that institutions are not always able to make decisions promptly, and they may be forced to make decisions despite not having all the information they need.
In the face of these challenges, analytics platforms like the Oracle Analytics Cloud are leveraging machine learning to help institutions make timely and data-driven decisions.
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What is Machine Learning?
In the most basic terms, machine learning systems employ algorithms that can learn from data without following any specific instructions. The goal is for the system to imitate how a human learns; the system grows more accurate and valuable the more you engage with it.
In the case of Oracle Analytics Cloud, machine learning enables it to understand the types of data that users are looking for and how they need the data to be presented to them. In the case of universities and colleges, this can include data related to student graduation rates, grades, retention rates, and more. The more a user uses Oracle Analytics Cloud, the more accurate it will be at presenting data in a valuable way.
How Does Machine Learning Work In Oracle Analytics Cloud?
One of the biggest advantages of machine learning in Oracle Analytics Cloud is that it makes compiling and visualizing data less labor-intensive. This means that institutions can spend less time and financial resources on hiring analytics talent to collect data and present it in a meaningful way to key decision-makers within the institution.
Through machine learning, the data collected by an institution can be quickly assembled into powerful visual stories, making it easier for all major decision-makers to understand the data that they’re looking at.
Machine learning makes it possible for the Oracle Analytics Cloud to provide an institution with a powerful predictive analytics tool. With machine learning, institutions can use Oracle Analytics Cloud to predict results and create forecasts quickly based on the data they collect.
For example, machine learning allows Oracle Analytics Cloud to aggregate data related to a student’s academic performance and predict whether that student will successfully graduate. This data can be scaled to look at the forecasted graduation rates of an entire department, campus, or of the entire institution itself.
This allows institutions to understand their data at a department, campus, or institution-wide scale. With more data on hand, institutions can make the necessary informed decisions to drive their progress.
Are There Other Ways that Machine Learning Can Be Used in Higher Ed?
Student retention rates in Higher Ed can become heavily impacted by the data analysis from machine learning. Students may drop out of school for several reasons that are not immediately obvious.
Financial issues, family matters, and poor academic performance are all reasons that a student may drop out of school, but an academic advisor may only be looking at their academic performance.
With machine learning, all possible relevant factors are taken into account. When academic advisors can review the data in a timely manner, they’ll be able to reach out proactively to at-risk students and provide personalized solutions to keep students from needing to cancel their enrollment.
Another compelling machine learning application comes in the form of chatbots for colleges and universities. Several universities use chatbots to increase student satisfaction and increase student retention rates. Bethel University, for example, was able to improve its student retention rate just one semester after it introduced its chatbot.
Their chatbot would use data collected to identify students that were on the verge of failing classes and connect them with the appropriate tutors. It was able to identify students who were struggling with their academic performance or not enjoying their experience due to being lonely or homesick and recommend opportunities for engagement with people with similar interests.
Their chatbot was even able to identify 32 students that we were seriously thinking about dropping out and was able to notify the student services staff. Thanks to early intervention, all 32 students re-enrolled.
With help from machine learning, these schools successfully used valuable student data and reached out to students before they dropped out. Since machine learning systems improve as users interact with them more, the ability to create individualized plans for at-risk students will likely only improve.
How Can Higher Ed Adopt Machine Learning?
Since universities and colleges already collect large amounts of data, adopting machine learning is not as difficult as it may seem at first glance. However, what schools may struggle with are the ethical considerations and limitations that need to be factored in. Machine learning is a powerful tool when it has access to large amounts of data, but the data needs to be used in a careful and measured way.
Stanford University stumbled onto this issue when they used machine learning to successfully infer the sexual orientations of individuals, with an accuracy rate of 81% for men and 71% for women. They then postulated that they could use machine learning to infer an individual’s political orientation or IQ, just through the use of data.
This is not the most appropriate usage for machine learning for higher education institutions. The Stanford incident highlights the importance of setting clear, appropriate goals when employing machine learning in higher ed.
Machine Learning: A Useful Tool for Higher Ed
Machine learning has a variety of applications in higher Ed and can make it easier for institutions to make decisions based on the data that has been collected. Machine learning presents information that can give institutions essential insights into their graduation and retention rates and allow institutions to reach out to at-risk students early.
On platforms like Oracle Analytics Cloud, institutions can quickly build visual stories that make it easy for decision-makers to understand the data they’re seeing. By using this data, institutions can improve a student’s experience. As long as the data is used ethically, higher Ed can significantly benefit from machine learning.
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