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.

3 Core aspects of a personalized learning platform

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:

  1. Learning goals

    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:

    • Master Photosynthesis — This is a very specific goal. This is what you might think of as being a competency, it’s like one small little dot in a Biology curriculum.
    • Master Grade 2 Biology — This is specific to the student’s age, or maybe they’ve already mastered Grade 1 so obviously they need to master Grade 2.
    • Prepare for a career as an Astronaut — This is a broad learning goal; that would mean the student has to master various concepts and subjects that would put them on track for such a career.
    • Earn a certificate in Javascript Engineering — This is not necessarily a specific competency but the platform can help the student achieve a certain certificate or another.
    • Dedicate 2 hours a week to learning Quantum Physics — This is a logistical learning goal, spending a specific amount of time learning about a specific thing.

     
    Any kind of personalized learning platform needs to be able to accommodate this range of goals.

  2. Recommendations

    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:

    • Watch the video Wind Farms — One simple recommendation would be to watch a particular video related to the learning goal.
      Take the course Synthetic Biology — Another thing that the platform might do is to suggest taking various online courses that are available around the world or within the educational organization.
    • Take the module Sustainable energy — Many courses that are taught at university really should be available as individual modules that students can take on demand. Ideally these courses should be broken down into little digestible modules so learning platforms should be able to suggest to students a certain module that’s exactly what they need to progress.
    • Join the forum Nuclear Fusion — One of the things that a learning platform should be able to do is to recommend students forums to join. For example, if a student is very good at Physics and their current focus is Nuclear Fusion, if there was a really good community forum for nuclear fusion, then the platform should be able to suggest joining it.
    • Take the learning path Speak French — If a student is interested in mastering French, and there’s a really good sequence of an introductory video, a certain book, a series of online courses and then a specialized forum, the platform should be able to suggest that particular learning path. Ideally, students should be able to share successful learning paths with others.
    • Ask Mary Collins to mentor your in Algebra — Last but not least, the human touch is still very important in the learning process, and technology will never replace it. A personalized learning platform might suggest to interested students that there’s a specific teacher who’s really good at mentoring them in Algebra, or any other subject, for that matter.
  3. Assessments

    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:

    • Take a pop quiz — The classic one would be a pop quiz, so you should be able to generate from multiple question banks a nice personalized quiz tailored any moment in the student’s learning journey. As long as those questions are tagged with specific skills that they are testing, the system starts to learn about the student’s strengths and weaknesses.
    • Provide a self-assessment — This is a little bit dangerous, as some students might overvalue their competencies, but doing a self-assessment can also be useful in some situations.
    • Assessment from peers / instructor — This is another important type of assessment, from your peers, and from your instructor or professor. Students learn a lot in social settings, so having the pertinent opinion of the right people can really help them.
    • Third-party certificate
    • University credit
    • Grade from a formal course — these last three don’t necessarily offer a fine grain level of detail if a student really is good at Photosynthesis, Algebra or another subject, but they do give the system a general idea about that student’s areas of strength and weakness.

The pixie dust tying everything together: Competencies

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

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.

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