In one sense all learning is personal as it requires personalattention, effort and practice. But ‘Personalised Learning’ as a term has come to mean the tailoring of learning to the individual, with a sensitivity to their individual needs. The goal being to increase the efficacy of teaching and learning, lower failure rates and increase access with scalable persoanlisation.
First, we need to dispense with the myth that this is about Learning Styles or some other phantom phenomenon. This is one of the commonest myths in education, deeply embedded but completely wrong-headed. Personal needs are far best defined in terms of a whole raft of data about what you know, need to know and your personal context and circumstances. I like to think of Personal learning as a solution to learning difficulties. We all have learning difficulties, as our brains are inattentive, get easily overloaded, forget most of what we’re taught, sleep 8 hours a day, are hopelessly biased, can’t download and can’t network. Then there’s the delivery problems associated with the current model, which is expensive, time-consuming, inefficient and non-scalable. If, as some believe, personalised learning offers a way forward for technology enhanced learning, then let’s give it a chance.
Personalised learning can be delivered offline, but it requires one-to-one tuition. Hence the great interest in Bloom’s famous paper, The 2 Sigma Problem, which compared the lecture, formative feedback lecture and one-to-one tuition. Taking the straight lecture as the mean, he found an:
84% increase in mastery above the mean for a formative approach to teaching and an astonishing
98% increase in mastery for one-to-one tuition.
Google’s Peter Norvig famously said that if you only have to read one paper to support online learning, this is it. In other words, the increase in efficacy for one-to-one, because of the increase in relevant and on-task learning, is immense. This paper deserves to be read by anyone looking at improving the efficacy of learning, as it shows hugely significant improvements by simply altering the way teachers interact with learners.
Personalised promise through technology
However, given the difficulty in scaling up through human one-to-one tutoring, the promise of Personalised Learning is now seen as being practically realised through technology. There’s a whole range of technologies that have emerged over the last few years, that have accelerated progress right across the learning journey – interface advances, engagement, support, content, curation, practice, assessment, tutoring, even wellbeing. Rather than focus on presentation, AI tends to rely more on smart software, to deliver more than just souped-up graphics and gamification. It relies on smart software, AI, to deliver personalised learning, not as one thing but as many things in the complex business of learning.
1. Personal interface
First up is a general move in interface technology, where AI is the new UI. Google, Facebook, Twitter, Amazon, Netflix and almost everything we do online is mediated by AI. Interaction is shifting towards ‘voice’, which is AI-driven. This, we can expect, to have some influence on the delivery of learning. It is even possible that AI-driven speech will radically change learning delivery. At WildFire, we are using voice only navigation and input by learners for the entire learning experience. We create podcasts on the fly, using AI. Alexa, Google Home and other consumer devices use voice as both input and output. Interfaces are becoming more frictionless, which reduced cognitive load for learners making learning more frictionless.
2. Personal engagement
Learners are lazy. There I’ve said it. Procrastination is the norm. Most do not do the pre-reading, leave assignments and essays until the last minute, miss lectures and behaviour in schools can be a real problem. Bots, such as Differ, can be used to engage and nudge students. This ‘push’ side of tech, whether by bots, email or message apps, can be personalised to match the individual’s pattern of needs and behaviour. Face recognition tech can also be used for registration, even spotting learning behaviours. In general, we know how engaging tech is for learners – as we spend most of our time complaining about its distractive effects. Suppose we take some of those engagement tricks and apply them to learning.
3. Personal support
Direct support though bots have already had success as Georgia Tech, where the students not only mistook the bot for an academic teacher but put it up for a teaching award. Bots, such as Otto, embedded on the workflow do a similar job in corporate learning. Support in online learning may come through access to resources, content and people but if it knows what your personal needs are, it helps.
4. Personal content (adaptive)
The personalised delivery of appropriate learning experiences, based on what you need at that exact time, drawn from data on you, others taking the course, even your (and others) behaviour on previous courses, can all be used to decide what you need at that exact moment. This ‘adaptive’ learning is almost certain to increase the efficacy of online learning. It resequences learning, and with formative feedback, can deliver on the promise of personalised learning, educating everyone uniquely. Having been involved in this area, for a number of years, I’ve seen some convincing results from real students in real institutions. This hold great promise.
5. Personal text analysis
It is unfortunate that in most institutions, the only place you’ll find AI technology, in a personalised sense, is in plagiarism checkers, seeing if you have cheated. Actually, what many are waking up to is the possibility of this same technology being used to help learners complete essays and assignments through formative feedback, using a repeated submit and learn model. Personal feedback on specific aspects of your performance, whether writing or performing in a domain specific assignment, could be one of the big wins. Formative feedback accelerates learning.
6. Personal curation
Learning experiences, especially online, often feel over-directed and fixed, a little straightjacketed. Imagine a system that uses AI to automatically provide links to useful outside sources relevant to you at that very moment you need them, as it has spotted that you have a problem. We do this in WildFire, where links to outside resources are created on the fly, making the course more porous and to encourage curiosity.
7. Personal practice
An often forgotten dimension in personalised learning is personalised practice. Spaced practice can be delivered in a logarithmic fashion, based precisely on your needs. Learn now and between now and your exam, new job, whatever, you get a personalised pattern of spaced practice. We have delivered this in WildFire, where you get several bites of the cherry after completing the course, delivered to you by email, to shunt learning from working to long-term memory.
8. Personal assessment
Both personal formative and summative assessment are benefitting from AI. Digital identification, through keyboard strokes (Coursera), face recognition and so on, makes sure that the right person is sitting the exam. Formative assessment, often using AI techniques, is being delivered to large numbers of students. Adaptive testing is also available, to provide personalised assessments. We’ve used assessment bots in WildFire to question learners after their more directed learning experiences. Assessment is by definition personal, it’s about time we made it more personal through technology.
9. Personalised tutors
AI is nowhere near being able to replace teachers. But as the technology begins to know you, talk to you, remember what you said and did and understand the context; then we can expect services like Alexa and tutors to bloom in learning. We see this with Duolingo and many other online services online. They get to know you and deliver what you want at that very moment, free from the tyranny of time and space – anyone, anytime, anyplace. This is what Amazon does for shopping, it’s what Cleo and other bots do in finance, it’s what fitness bots do in health. We can expect moves in this direction in learning. Note that this has nothing to do with robot teachers. That idea is as stupid as having a robot driving a self-driving car.
10. Personal difficulties
We have evidence that young people are feeling the pressure and the statistics for stress, mental health problems, even suicide are rising. We also know that young learners hide this from their parents, teachers and tutors. One solution is to anonymise help. Woebot and Elli have already been used and subjected to clinical trials. The results are encouraging. Beyond this, we already know that personalised learning is a boon to learners with learning difficulties. Individualised responses to physical and psychological issues is now normal in education.
Personalised learning is not one thing, it is many things, an umbrella term for all sorts of applications, especially those driven by AI. We are on the cusp of bringing smart software to deliver smart learning to make people smarter. It will take time, as AI is, at present, only good at very specific, narrow tasks, not general teaching skills. That’s why I’ve tried to break it down into to specific sweet spots. But on one thing I’m clear – this is a worthy path to follow.
Bloom, B. (1984). The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring, Educational Researcher, 13:6(4-16).