The benefits of personalised learning have been known and applied for hundreds of years. But whilst it’s not a recently founded approach, personalisation is becoming paramount. Why is this happening now? Firstly, because the number of learners in a class, physical or virtual, is increasing and it’s common to have hundreds of students following a lecture at any single time. And with this increase, the diversity in learners’ background and learning needs is also widening.

Secondly, because education is becoming more student-centred. One size teaching doesn’t fit all learners and it’s the teaching (and learning) that needs to be tailored to the individual student, not the other way around.

But how would it be possible to meet those diverse needs in order to support each single learner in achieving the best outcomes possible? Adaptive learning could be the solution.

There are two ways to think about adaptive learning:

  1. When the capabilities to adapt which activities or content (and when) are presented to a learner based on certain learner characteristics are built within the learning environment. In this case the learning environment controls the adaptivity of teaching and learning.
  1. When learners are allowed to choose their learning activities and their sequencing based on personal preferences. Here the learner is in control of the adaptability of learning.

Both adaptivity and adaptability aim to deliver personalised learning and the best results are achieved with a blend of these two approaches.

For examples, let’s think about some of the factors that the learning environment can take into account in order to personalise learning:

  • Prior knowledge
  • Level of engagement in the course
  • Performance measured via test or coursework results

These factors can be used to pre-program the types and the sequence of learning activities. Programming can be sophisticated enough to detect changes in student behaviour along the way and offer different learning paths.
On top of this, we could add tools for students to further personalise their learning, like selecting from a range of ways to study, for example, using linear navigation through course materials or using a related concepts approach.  Or the ability to choose who they work with in their peer group and how, where and when they collaborate.  This is where technology can definitely help to enable learners to personalise their learning.

Adaptive learning, while increasing students’ engagement, does not cover all their needs. In-person one-to-one interaction with their teachers continues to be highly sought by learners, especially when it comes to receiving feedback on their work.  The biggest level of student dissatisfaction usually surrounds assessment and feedback. Even when feedback is provided either hand-written or online, some students still prefer to receive feedback in-person.  But how can this be practically achieved in classes of 400+ students?

Again, technology, this time in the form of learning analytics, can play a key role to minimise risk of students becoming demotivated due to perceived lack of sufficient individual attention.  Automated risk analysis running in the background to examine how well students are doing today and how well they’re likely to be performing and engaging in the short-to-medium term, provide another way for teachers and tutors to spot which students need extra attention at any particular time, making it more practical to engage with these at-risk students in a timely manner and give them individual attention.

More and more national education frameworks, like the Teaching Excellence Framework in the UK, have the student at the heart of the system.  It’s a duty for all institutions to find ways to personalise student learning and maximise their chances of success.

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