Tag Archives: adaptive learning

Learning Materials Shouldn’t Form Filter Bubbles

Snellmanin ala-asteen luokan oppituntiIn printed learning materials bubbles might be formed between the school book cover and the plastic covering it. However, bubbles can emerge also with digital learning materials. These bubbles are completely different since they isolate learners from each other. That’s why it is very important to avoid and burst them.

Last time I wrote about how artificial intelligence is teacher’s friend not enemy. Intelligent digital tools and materials are not about to replace the teacher. Instead they support the teacher, making her job easier.

Now it is time to address another theme: filter bubbles. Filter bubbles are known from social media and politics. People with similar opinions and attitudes talk solely with each other, causing them to form a bubble which drifts away from other bubbles. In the future the same unwanted mechanism can also be affecting learning.

Politicians, education officials, application developers, publishers, and many others: one of the most central objective for parties working with education is to advance personalized learning. This means that learners are encountered as individuals rather than as homogeneous groups. For example my employer Sanoma Pro as an educational publisher aims to offer diverse materials with something for everyone.

In digital environments personalized materials can be composed and distributed automatically. For example in Bingel the pupil is offered easier or more difficult exercises depending on whether he got previous answers right or wrong.

Personalization cannot jeopardize the equality of learning

Personalization and adaptivity can be taken further than what present Bingel has. This is exactly where the danger of filter bubbles looms. In principle the learners could complete exercises and according to the answers be directed in different routes which might not cross in the future. This could replicate the unwanted scenario from social media with learners in their own bubbles, isolated from each other.

In real life the teacher has a central role in guaranteeing that each learner goes through at least particular topics. Also the curriculum mandates this; personalization has to be implemented respecting he curriculum.

What to personalize then, and on what grounds? Some propose learning styles as the answer. It means offering interactive content for kinesthetic learners, videos for visual learners, and so on. However, learning styles and their positive impact to learning has been debunked several times. Not worth to waste time in them.

Personalization for engagement

My proposal is to personalize in order to engage and motivate the learners. One can assume that a learner is more motivated if she gets offered learning materials tailored for her. For example, exercises should be hard enough to keep the learner interested. They cannot be too difficult, however, so that the learner doesn’t get frustrated. It is natural for people to like both routines and change. Learning materials should reflect this by offering both familiar content and surprises.

To conclude, an important note related to personalization and more generally applications utilizing AI: both the learners and the teachers should have the feeling of being in control and understand the tools and content they use. This is why it is worthwhile to offer recommendations rather than completely automatic adaptivity. Let the learners and teachers to select whether to follow recommendations or not.

Even better if the recommendation includes argumentation of why just that particular content is offered to that particular learner. For example like this: “You seem to be practicing for tomorrow’s geometry exam. You have already mastered areas but there is still some work to do with volumes. Here is a 30 minute exercise package for you and there is a three minute video covering the basics. You can also familiarize yourself with volumes with the following containers found in your kitchen….”

When implemented right, personalized learning motivates the learner, eases teacher’s daily work, and does not isolate learners from each other.

This post was first published in Sanoma Pro’s blog in Finnish.

 

Transparent Personalized Learning

crowd

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

EU Supports Nurturing #Edtech Startups and That’s Great

Open Education Challenge

I used to work as a researcher for more than six years at VTT, the technical research centre of Finland. It is actually the longest gig I’ve done in my career, and a very interesting one at that!

At VTT we used to get funding to our research projects both from companies and governments. One significant source of research funding is the European Commission. We’d send applications to EC’s framework programme calls together with universities, companies, and other research institutes across Europe. Sometimes we got lucky and scored funding for a couple of years.

Now I’ve been working in the private sector since 2008. I must say that looking in retrospect some of our projects lacked true market demand and contact with the end user. That’s why I’m delighted that these days the Commission has also more business-oriented instruments in their portfolio to find new innovations.

One of those is the Open Education Europa. They are currently organizing an edtech startup challenge and incubation program. I am lucky to be one of their mentors in Helsinki.

Next week I’ll be coaching Atta, a social media platform for learning; Cubes Coding, a robot programming platform for as young as three year olds; Domoscio, an adaptive learning engine; Funbrush, an interactive toothbrush(!); GroupMooC, a service for organizing your MOOCs; Harness, a blended learning & classroom flipping service; KLAP, an adaptive learning and analytics product; Think with Things, an application for turning any physical and digital objects to learning resources.

So this should be interesting! I’ll write another post once I am done with them.

 

Artificial Intelligence, Y U Follow Me?

Artificial Intelligence (AI) has the tendency to follow me wherever I go. Maybe I am to blame, since I first went to it when still studying at the universiy. I wrote my Master’s thesis in 1999 about software agents, which at that time were the main manifestation of AI.

These agents were supposed to start roaming the web delivering all sorts of interesting content to their masters, the web users. Then I moved on to researching the Semantic Web, which was intended to provide healthy food for these agents.

Next were context-aware mobile phone apps. The simplest form of context-awareness is based on the user’s location: provide the user with content which is relevant in her current place, e.g. nearby restaurants with good deals.

There are many more attributes describing the user context than mere location, however, and combining those into a meaningful framework is tricky. It’s so tricky that doing it right in my opinion requires quite strong AI. You’d have to know the user’s current goals, social surroundings, mental and physical state, etc. in order to provide her with the most suitable content and services.

I was able to hide from AI for a couple of years, working mainly on market research and social media. Now since more than two years I’ve been in education and AI has yet again found me, this time in the form of personalized learning.

Personalized or adaptive learning is a great goal to pursue! What better for a student than to provide him with the materials and solutions that best fit his skill levels and learning style preferences? By the same token, doing it all the way it is tricky. Very.

Complete understanding of the user context basically requires telepathy. As Nagel pointed out, one person cannot fully understand what it is like to be another person. It cannot be objectively communicated. Similarly for learning: students are unique individuals and facilitating each one best is (at least next to) impossible.

There are many things that can be done, however, and are actually being done already. Adaptive learning solutions are going to enter education the same way intelligence enters the automotive industry: slowly but surely, without us even noticing it.

If you would bring a modern self-parking car with cruise control and windshield wipers adjusting to the weather to say 1940s, locals would treat is as (artificially) intelligent device for sure. However, these innovations have emerged over time and that’s why we are reluctant to claim them as AI.

So keep calm and carry on, adaptive learning solutions are going to pop up here and there. It’s going to be an evolution, not revolution.