This brain the most complex thing we know of in the universe. We all have one that is absolutely unique, that doesn't like impersonal, long linear, flat experiences. Yet, in learning we feed it exactly that - long lectures, flat pages of text, one-size-fits-all learning experiences. Learning is a deeply personal experience and most learning theory, in terms of attention, deep processing, self-generation, practice and so on, pointstowards the need for individual needs.
Personalisation is the one great gift that online learning offers teachers and learners. But it comes in many practices, not all of them obvious. In classrooms teachers talk about differentiation then struggle to differentiate, with 30 plus kids. In Universities, the anonymous mass that turns up to mass lectures (or not), is the opposite of personal learning. In the workplace, the compulsory compliance course is far from ideal. But ‘personal’ can mean relevant, timely, location-based, self-determined, self-generated, favourites, targeted, private, even intimate. Learning is rarely any of these. Set-meal courses are the norm. What to do? Personalise.
Personalisation is the one great gift that online learning offers teachers and learners. But it comes in many practices, not all of them obvious. In classrooms teachers talk about differentiation then struggle to differentiate, with 30 plus kids. In Universities, the anonymous mass that turns up to mass lectures (or not), is the opposite of personal learning. In the workplace, the compulsory compliance course is far from ideal. But ‘personal’ can mean relevant, timely, location-based, self-determined, self-generated, favourites, targeted, private, even intimate. Learning is rarely any of these. Set-meal courses are the norm. What to do? Personalise.
1. Timely
Learning delivered when you need it is called performance
support. That specific thing that you need at that timely moment, is often
something precise and special. This is what predictive agents and AI will
deliver for learning. That precise answer, the one you need right there and
then, the time predicted by software.
2. Location-based
Plenty of apps are now location-based and learning can be
delivered tat is sensitive to your personal position. It may be in an art
gallery in front of a painting or when operating a piece of electronics. I know
of one programme that locked people out of the central area of a nuclear power
station if they failed their risk assessment course. It disabled their
electronic badge. Well see more of this I’m sure.
3. Real time adaptive
Personalised delivery can be in realtime, as you move
through a learning experience. The software decides what you need next, based
on predictive analytics, a bit like a SatNav, getting you back on course. This
approach is sensitive to personal needs in terms of attainment, misconceptions,
and so, preventing failure. The personalisation depends on both the individual,
aggregated data and the algorithms used to determine such paths. There will be
no linear course, only vectors through content.
4. Porosity
With AI driven online learning tools you can create courses
that are ‘automatically’ porous, with links out to automatically linked
content. This can be done with YouTube, Wikipedia and other text sources in
WildFire. This allows the learner to move out of the more structured online
e-learning into more detail, where they want to dive deeper. It allows a more
personalised set of routes through the learning experience driven by curiosity,
not deterministic paths. One such tool is WildFire.
5. Generative
Self-generated content has always been a powerful way to
learn. I’ve produced a revision tool, where you simply type in your notes –
from a lecture, class, course, revision – whatever. The software then
automatically creates active online learning, where you answer automatically
generated questions on your own notes. This moves the learner from
reading/underlining to active, recall and remembering.
6. Playlist
Jeff Staes put me on to this – the idea that efficient
learning has less to do with ‘courses’ than personal playlists, like our music
playlists. We all have different needs that vary over time but these needs are
more like personal playlists of skills, that vary over time as our needs
change. Most of our behaviour is personal and so learning should be
individualised. We have preferences, needs, likes, dislikes, bad habits, good
habits – these can be reflected in our playlists.
7. Personal assistant
What we may need is a behind the scenes is a personal service
that does the messy stuff for us. A good example is the Personal Assistant. I
had a PA for years and found it difficult when I stopped running a large company
to go it alone. But try AMY the AI driven personal assistant. It’s uncannily
real. We are now seeing agents enter the learning arena, to help us navigate
our way through what we need to know and learn.
8. Social
Social media is highly personal, with AI dealing with the
delivery of Facebook timelines, Tweets and other social stuff. The billions
that use social media clearly like the fact that it is a personal experience,
not only in terms of their preferences and likes but also personal in terms of
people, who you follow and so on. This is why social learning matters, It is a
set of highly evolved, even intimate, habitats in which one can learn from
friends, relatives, colleagues, experts and others.
9. Assessment
There are all sorts of ways that assessment can be personal
in online learning, especially using contemporary AI techniques. I can identify
you digitally for an online exam using facial recognition, typing patterns and
other forms of digital identification. I can deliver randomised, adaptive test
items to avoid cheating. I can automatically grade and provide feedback on you
essay through automated essay marking. I can spot cheating patterns by teachers
and students through statistical techniques to spot deliberately
de-personalised assessment.
10. Spaced-practice
Now that we have the ability to deliver learning via mobile
devices, spaced-practice is coming of age. Having blown through the 100 million
users mark last year Duolingo is a good example, there are many others. These
learning experiences are finely tuned to your personal reinforcement and needs
in terms of remedial practice. Most AI adaptive systems use spaced-practice in
one form or another. You receive practice items, not only finely tuned to your
performance but also to your availability and pattern of use.
Conclusion
All of the above are examples of the personalisation of
learning but there is one more dimension that takes the personal to another
level and that’s scale. The trick in learning is to personalise, at the same
time as scaling up. It’s pointless personalising for the few when
personalisation means a personal experience for the many. In my view, it is AI
that will accelerate the personalisation agenda by delivering more than just
the illusion of personalisation but the reality.
4 comments:
Enjoyed reading your thoughts on personalized learning. An addition to your points is the intelligent decision-making protocol which enables pedagogical agents (peer, cognitive, and metacognitive) to interact with human learners based on the real time information obtained from human learners. This protocol helps agents to simulate real human tutors while interacting with students with various learning abilities. I beleive this will be the future to personlizing the learning experience for students which will transform how teachers teach and how students learn.
A stimulating article that presents exciting possibilities. Are there any organisations at the forefront of adopting AI as a driver of personalised learning or is it too early for this yet?
I'm working with a few. HE is also heavily involved - ASU etc.
Great piece of information, Thank you for sharing the updated one...
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