Whether we use digital learning platforms to learn skills, solve problems, fulfill personal interests, understanding the mechanisms at work is vital. Be it better outcomes or more creative uses of technology for students, understanding how personalised learning works brings us closer to providing richer educational experiences through artificial intelligence.
Through data, the platform monitors your accuracy so that corrections can be made on the fly, and predicts where gaps and strengths are and suggest a path forward. The personalised learning paths that algorithms take on each platform are nuanced. For each user, the algorithm adjusts the readings to be accurate for that session. But it doesn’t do this for everything listed, just for the things that matter the most to the individual user. In doing so, the algorithm allows each learner to move through their content in a personalised way that takes all the variables of that learner and tries to optimise it in an efficient manner.
It is crucial to understand that an effective AI algorithm that facilitates the personalised learning paths taken on online learning platforms is not just random but contains many educational theories embedded in them. For instance, the theory that learners learn best when they know what they know and don’t know is used as a starting point for many algorithms to get to familiarise itself with the learner. You may have encountered this in the form of a short introductory quiz or tests or surveys. The platform captures data concerning accuracy and confidence level and time. The platform takes this data into account, serving up content that not only helps each learner increase their accuracy, but also their awareness around their accuracy so that they walk away knowing what they know and don’t know. The second principle, the theory of deliberate practice, holds that understanding where we are the weakest helps us focus our practice. To address this, the platform continually adjusts the content to focus on each individual’s weaknesses, ensuring that time is used efficiently and effectively. The theory of fun for game design holds that learners are most engaged when challenged, but not too challenged. If too many questions are answered wrong in a row, for instance, the platform will serve up a question that proves a quick win for the learner and builds his confidence. The Ebbinghaus Forgetting Curve holds that to truly learn something, learners need to commit it to long-term memory and that the best time to do so is just before learners are about to forget it. Incorporating this theory, the platform uses data to predict when someone is most likely to lose a concept from short-term memory and recharge it so that the learner commits it to long-term memory. It also uses elements of gamification to keep the learners engaged and coming back for more. After all, if the learner does not log in, then everything else becomes irrelevant. Learner engagement and ownership should constantly remain at the forefront.
In my earlier article, I had talked about the role that machines and AI will play in our workplace. The move from machines as tools to Colleagues will be a seismic shift. While the concept of machines working alongside you on a routine basis may seem far-fetched, it is not. Humans and machines will be working as colleagues in the near future – and I mean really near. In its basic form, it has been happening for several decades but now it is going deeper, and we will see instances where we rely on a machine’s input to move forward. It is my belief that we should prepare our learners for this from a very early age. After all, they will most certainly have machines as their colleagues and the curriculum should encompass this new way of life. I have stated before and I will do so again – machines should augment our potential not dumb us down. They should help us to free up time from routine and complicated tasks, that require a lot of computing power, to focus on more creative and complex pursuits. If we have this mindset – that machines will augment our capabilities rather than worry about losing our jobs to machines – we can thrive. At its very basic, Artificial Intelligence is the science and practice of building intelligent computers and robotics. We first became aware of its potential in the early days of home computers, but today, AI has become an everyday part of our lives with AI growing in number and power exponentially. AI uses data to create an artificial intelligence model, which is a set of rules that describes how to conduct a task. The more data a model has, the more complex the AI model becomes. So as more students begin using adaptive learning platforms, the more effective it becomes.
What is Personalised Learning?
All of the theories that underpin the personalised learning platform will generally align with the type of learning that a student is meant to experience. Learners have considerable agency over their learning. For example, the learner inputs his or her specific goals and interests. The platform then creates a framework that prioritises the learner’s content experiences as the learner moves through the platform. For example, the learner defines that while they like to be challenged, they don’t want to be pushed too far. The platform then prioritises a series of experiences that provide both challenges and rewards, but not at the expense of the learner losing interest.
After months of being exposed to the personalised learning paths, what should be obvious to most people is that personalised learning does not end when the subject is successfully completed or the person leaves a platform. As these technologies get better we will soon see that recommendations come up for us ‘just in time’ about skills that we need depending on what we need to do.
The future of learning is to achieve deep wholistic learning for everyone, effectively. We are not there yet as current practices revolve around academics and related skills. The equally important other areas of development such as Social, Emotional, Physical and Spiritual are not factored in yet. When this happens, we are entering a revolutionary age of learning. The future is in the hands of today’s educator.The teacher will always be the mentor to her students, hence it is important that she constantly updates her own skill set by being in constant learning mode. Rather than seeing the teacher as the only element that can aid their learning journey, teachers will be augmented by AI who can be viewed as an assistant teacher for each student.
Finally, it will be interesting to observe how this plays out beyond schools. For example, corporates could be utilising adaptive learning to tailor content that is specific to the needs of their employees rather than have everyone undergo the same training. The resulting analytics could be used to find employees that are best suited for a particular task or project. By designing adaptive learning experiences, employees can continue their routine work, get feedback, and navigate challenging content even without direct or immediate access to a trainer or a professional development coach. In addition, they will have the opportunity to master their learning, explore unique learning sequences and work at a comfortable asynchronous pace while having ownership of their learning. This is not the distant future but something that we are stepping into right now. Machines are learning, and fast at that. It is time for us to learn from them to ensure that we are upgrading our skill sets and be in the driver’s seat rather than being dumbed down. We should always remember that technology exists to augment human potential, not replace us altogether. The technology that we have currently, and the ones that are emerging are very powerful. And as the saying goes, with great power comes great responsibility. In my book ‘Serene Strength: the Role of Education and Learning in the making of a Person of Substance’, I expounded the idea that we need Persons of Substance – those who do the right thing simply because it is the right thing to do. We need them to lead us through this monumental technological shift that is taking place. Our future is being shaped here.
The future is neither unseen nor unknown. It is what we make of it. What work we do with our two hands today will shape the future of our nation. Our children’s tomorrow has to be created by us today.
– His Majesty the King of Bhutan, November 2008
References
“The Forgetting Curve: Why We Forget, and What We Can Do about It.” 2015. Mindtools.com. 2015. https://www.mindtools.com/pages/article/forgetting-curve.htm.
Peng, Hongchao, Shanshan Ma, and Jonathan Michael Spector. 2019. “Personalized Adaptive Learning: An Emerging Pedagogical Approach Enabled by a Smart Learning Environment.” Smart Learning Environments 6 (1). https://doi.org/10.1186/s40561-019-0089-y.
Europarl.europa.eu. 2021. [online] Available at: <https://www.europarl.europa.eu/meetdocs/2004_2009/documents/dv/dsas20081202_bhutan_/DSAS20081202_Bhutan_EN.pdf>
“When Adaptive Learning Is Effective Learning: Comparison of an Adaptive Learning System to Teacher-Led Instruction.” 2020. Interactive Learning Environments. 2020. https://www.tandfonline.com/doi/abs/10.1080/10494820.2020.1808794