The availability of AI technologies, machine learning, robotics and so on is happening much faster than people might expect. We are not talking about some science fiction dream that is going to take 20 years to accomplish; almost all of these technologies are already available to various degrees in various industries, and they’ll go to the next level sometime in the next year, or in the next few years.
Educational institutions need to keep up. No matter if we’re talking about kindergartens, elementary schools, high schools or colleges and universities, the need for a learning platform that offers personalized learning experiences for each student is all too real.
Students go to schools with various educational backgrounds, have diverse interests and learning preferences and progress in each subject at different rates. One teacher alone can’t possibly deal with all the things going on regarding each student in an in-depth manner. But with the help of educational technologies, things change.
A personalized learning platform can support teachers in meeting the needs of each and every one of their students, no matter if we’re talking about classes of 15 preschoolers or 15,000 university students. Such a platform can establish the current level of knowledge each student has over any kind of subject, what they need to learn, and make personalized recommendations or give advice on how each student can achieve their learning objectives. Just as importantly, a personalized learning platform can evaluate student progress at every step of the way.
There are three core aspects that define a personalized learning platform: each student must be able to define their own learning goals within it, receive recommendations based on their learning journey and have their progress clearly assessed so that they can see their progress. Let’s explore each of these aspects:
Learning goals can range from extremely specific to very general, or they can be more logistical. Regardless of the type, students should be able to set their own learning goals within the platform, have them set by their teachers or mentors or have them suggested by the platform itself.
Here are a few examples:
Any kind of personalized learning platform needs to be able to accommodate this range of goals.
Recommendations are typically generated using a combination of explicit rules and statistical correlations. One form of recommendations is to leverage automation, so that instructors can program in their own recommendations. For example, a Biology professor can push their book on Photosynthesis to any student that has set the learning goal of mastering that concept. The second kind of recommendation, which is powered by AI, is statistical correlation. In other words, the platform can track and say 85% of students who wanted to get good at photosynthesis, who followed this or that recommendation, improved their scores by 30% within five days.
Here are more examples:
There's a wide variety of ways to find out how well a student actually knows what they think they know. They usually find out that there are still plenty of things that they don’t know. Yet. Anyway, assessing student knowledge is a very important part of progress and personalized learning platforms need to enable more than one way to do it.
NEO Brochure: Assessing students using NEO
Here are a few examples:
The one key thing that ties learning goals, recommendations and assessments together is the concept of competencies.
NEO Guide: Competency-based learning
A personalized learning platform has to be able to figure out what a student is good at, what they’re not good at, which resources can help them at any time, how to assess them, and so on. If instructors can break down everything that they really need students to know into these little bite sized nuggets called competencies, then they can tag the questions for the competencies that they asses.
Every single time that a student gets a recommendation like taking a learning module, watching a video, joining a forum, or gets their knowledge assessed in one way or another, the system is updating measurements of all these competencies and can show them in real time. In time, this builds up a database that knows all about each student’s strengths and weaknesses.
Competencies are the common currency that links learning goals, recommendations and assessments.
To summarize, it's a very exciting time to be an educator. AI and machine learning are going to enable a big improvement into the cloud based learning platforms that we already have. These technologies are real and they’re developing at an incredible fast pace, and they’re going to be available in a way that seamlessly integrates into what educational institutions of all shapes and sizes need and expect.