The Intersection of Machine Learning and Higher Education
By Ray Blackwood, Vice President, Product Management, Campus Management Corp. — Back in my days at the University of Advancing Technology, I had an opportunity to study artificial intelligence (AI) as part of my undergraduate studies. As you may know, the true measure of successful AI is to convince a test subject that they are interacting with another human and not computer intelligence. This is known at the Turing Test, named after Alan Turing, the mathematical genius who cracked the Enigma Code for the Allies in World War II.
While at the university, I developed ‘Clark Devereaux’, an AI persona that sent students and administrators needed information. Some of my favorite moments were when people would come into the IT area and ask to speak with Clark. They would ask if he was in and even invite him to meetings. It has been more than 10 years since Clark was invented and seven years since I left the university. Clark is still active today!
Back when Clark was created, we didn’t have access to machine learning like we do today; thus Clark was limited to what we programmed him to do. Today it’s a completely different story. Machine learning, first explored by IBM back in the 1950s, adds a predictive element to artificial intelligence. Instead of a system being hardwired for certain actions and responses, it will also make predictions over time based on the data it receives, what we more often refer to as predictive analytics today.
As an executive for a higher education technology company today, I’m amazed at how far the industry has come in incorporating machine learning into systems and solutions. Where Clark didn’t have the benefit of machine learning to recognize and predict behavior, next-generation RENEE can. She is a virtual guidance counselor who facilitates interactions between institutions and their students. She notifies advisors of students who are at risk as well as the best way to reach them.
As we will see at EDUCAUSE this month, more vendors are beginning to integrate machine learning (and predictive analytics) into their student information systems as institutions seek to gain greater insight, enhance student engagement and improve student outcomes. One example is adding machine learning to the registration assistant logic. Using natural language, students can interact with an AI through voice or chat and receive assistance with class registration.
What is most exciting, is that we keep adding more and more processes to predictive analytics and machine learning. Admissions, retention, reporting, course scheduling and finance all have areas that are ripe for this technology. As the industry continues to adopt machine learning and predictive analytics, we allow for innovative ideas like RENEE to become real value-added benefits to higher education -- even if, like Clark back in the day, they get invited to a meeting or two.
Ray Blackwood is Vice President of Product Management, Campus Management Corp.