e-Learning Ecologies MOOC’s Updates
Differentiated learning – catering to diversity in learners and learning modes
People learn in different ways and at different rates. Differentiated learning, one of the affordances of the “new learning” paradigm, brings the world of learning to nearly everybody today. With the differentiated learning model, two people could be learning the same material from the same source, but not even doing the same activities; one might, for example, follow the more traditional route of playing the video lectures and making notes, while the other might start with a writing assignment and refer back to the videos as needed. One might progress in a linear fashion, while the other might take the approach of learning topics as they become relevant, which might not be in the same sequence as the learning content is organised. This works well to cater to the diversity of learners today – diversity not just in who is learning, and how they are learning, but why they are learning. Learning is no longer restricted to those who need to learn to fulfil a requirement or to earn a degree – a large proportion of today’s learners are learning because they want to learn, for a variety of reasons. Some are learning to upgrade their skills in their existing roles; others are learning new skills to open new opportunities for themselves; and yet others are learning simply because they can.
This environment lends itself well to learners being able to discover for themselves what works best for them, and hence gain the most out of their learning endeavours. Taking away the restrictions of a traditional classroom removes more than just physical barriers – it enables learners to explore and discover new ways of learning. Let us consider the example of a course on artificial intelligence that I recently took on a popular MOOC, along with some colleagues. I discovered that what worked best for me was to watch the initial concept lectures in each module, and then jump directly to the end-of-module assignment; I would then refer back to individual lectures, or even specific parts of specific lectures to clear up concepts I was struggling with. Others in my cohort followed the lectures in sequence and got to the project at the end of each module. Yet others skipped all of the content entirely and started straight with the project, only coming back to the videos (or to be specific, the transcripts of the videos) for very specific information. In the end we all ended up with the same knowledge, to more or less the same skill level, but discovered that we had reached this goal via fairly different routes. This brought home the power of differentiated learning to me in a very real sense.
It would be amazing if there were eventually an app/AI that could accept video of the learner's physical surroundings and then custom tailor analogies or experiments (even if only thought experiments) using that surrounding environment. Explaining gravity using a stick on the ground near by. Explaining shadows using a chain link fence. The Doppler effect with cars on a nearby road. That would be an incredible level of immersion and access.