Transparent Personalized Learning


Personalized/adaptive learning applications can sometimes appear unpleasant or even scary. Teachers can feel threatened about suspicious AI-powered computers taking over their jobs. Students can experience ready-made learning paths as passivating and objectifying.

What to do to prevent this? Transparency and a sense of control are keywords here. Ideally students and teachers should both have:

  • visibility to the collection structures (what materials follow each other to compose the collection)
  • argumentation & reasoning on the collection structures (why these materials are provided to the student in this particular order/route)
  • capabilities to alter the structures (how to take another route than the one recommended by the engine)

For teachers, personalized learning engines are best seen as instruments to differentiate and that way better serve students. They are teachers’ tools, not replacements. They save teachers’ precious time, allowing them to interact more with the students.

Students should approach personalized learning applications as any recommendation engines on the web. Think of how you use TripAdvisor on vacation. When you spot a restaurant recommendation, you are interested not only in the rating, but also the open comments, type of cuisine, location, opening hours, etc. You want to know all the reasons why this particular restaurant gets recommended to you.

Depending on your context, these variables get different weights. Dead tired and without a car, you settle with the closest joint. On your last day on a holiday, you want to make it special and hike to the best restaurant up on the hills.

Similarly for learning: for each recommended learning resource, the student should have access to justifications (why) as well as capabilities to use another resource instead (how). Oftentimes the algorithm probably recommends something the student is happy with. However, there should always be transparency and control to have impact.

Note that what is good for you is not always what you want. It would be good for you to look up that healthy salad bar on TripAdvisor and have your lunch there. Instead, you crave for quattro formaggi and therefore want to go to the nice pizzeria down the street.

Again, ditto re: learning. Sometimes you should make the jump to cold water and struggle with the really difficult exercises, even if you wanted to continue with the easier ones. To learn a new concept, it might make sense to engage in a dialogue with your peers, even if you were an introvert who prefers to study alone.

The engine can propose what’s best for you but you should still make the final decision. As David Wiley writes, we should put the person back in personalization.

Flickr image CC credits: James Cridland

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