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.
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.
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.
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.
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.
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.
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.
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.
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.
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.