As a nation, we are fascinated by AI (Artificial Intelligence). We create movies about it and write books and comics on the topic. We acknowledge that it is both wonderfully exciting and a little bit scary. We immediately imagine robot-like people walking around and interacting with us. Images of Star Wars and Wall-E come to mind. We all dream of having our very own Rosie Jetson to clean our homes and cook for us. But, is this really how AI looks today?
What is machine learning?
Machine learning is a form of AI where computers use algorithms to analyze, learn, adapt and predict outcomes to a chosen stimulus without the need of additional programming. Through these algorithms, computers are able to pull information from large data sources, recognize patterns and generate use cases. They can find anomalies in real-time and at scale.
You may not realize that machine learning is at work in your day-to-day life already.
- Have you ever logged onto a website and the advertisements are similar to recent searches or purchased items?
- Have you ever asked questions and received answers from Amazon’s Alexa or an iPhone’s Siri?
- Have you ever received additional log-in requirements when accessing your financial institution?
Each of these examples are using machine learning to acquire knowledge, adapt and predict to you and your unique behaviors.
Some industries have been quicker to make use of machine learning than others. For example, marketing companies use machine learning to make product recommendations to potential customers (as mentioned above). Security companies use it to detect and prevent fraud. And customer relation organizations use machine learning through chat-bots and automated customer support services
Findings in the recently published Global CIO Point of View highlight how companies are “eager to reap the competitive benefits [machine learning] can provide.” The study found that 72% of CIOs are leading digitization efforts and 53% say machine learning is a focus area. There is an expectation that investments in machine learning will nearly double over the next three years to 64% by 2020.
Machine Learning and Education
The education community has not been a fast adopter of machine learning, but that is due to a couple of reasons. First, the education sector has been slow adopters of technology in general. It wasn’t until the 2015-2016 school year that “more state standardized tests for the elementary and middle grades were administered via technology than by paper and pencil”. Second, there are many security concerns when dealing with education data (particularly sensitive student data), since students in grade school, middle school, high school and even some college students are under 18 years old. Lastly, the education community demands high standards. If a deployment was rolled out and immediately failed, it would be hard to prove the usefulness of the functionality. For example, if machine learning were used to grade tests and the algorithm failed, teachers and professors would be hesitant to use it again.
No two people are alike. Therefore, it is easy to surmise that no two people learn and retain knowledge in the same way. When you drill down to what exactly machine learning does, analyze, learn, adapt and predict, you start seeing how these functions could help education. Having the ability to collect data on how a student learns, to be able to generate a learning experience that is personalized to their specific needs, is revolutionary. Machine learning has the power to act as each student’s very own private tutor.
Revolutionary for Teachers and Students
Collecting all these unique data points is a win for the instructors as well. They can now see how students are consuming their lessons. They can see where someone is excelling versus struggling. Instructors have the ability to adjust their lesson plans to ensure more success from their students as a whole. Additionally, the predictive function of machine learning can really help the students learning and understanding experience by isolating learning obstacles for a more targeted teaching approach and providing additional supporting materials when needed, to help them grasp the content.
In the next few years you will see machine learning technologies adopted more and more in educational tools. This generation of students relies heavily on handheld, electronic devices, smartphones, laptop computers and the world-wide web. Applying machine learning and AI to technology utilized by these tools can identify insights in data sets they may have gone unnoticed in traditional education models. This really highlights how machine learning has the power to revolutionize a student’s learning experience!