Personalising Learning
26 The Flip Side of ALS: Some Paradigms to take note of
Despite the promised potential of adaptive learning systems, many questions remain unanswered. There is not yet enough research or documentation of classroom practices that help broach these issues:
- Recommendation systems are used for suggesting movies to Netflix users. They help consumers home in on the right choice of, say Audio Speakers on Amazon. But can they actually improve learning outcomes for each student in the classroom1?
- Does focussing all the time on performance and individualisation affect a student’s psychological well being2?
- Individualisation demands a lot of discipline and self-regulation from a student. They have to start working by themselves and continue working till they finish all assigned activities. Are all students able to do this without help2?
- How do we balance individualisation with social learning opportunities3?
- How do we go from using ALS as a support for a single topic, to using these systems systematically, across topics and subjects2? What about the curriculum change that will be required for such an incorporation of adaptivity3?
- What about the required infrastructure? What needs to be done about data and privacy, as well as bias and reinforced stereotypes3?
When developing ALS, some principles are used either directly or implicitly. These are not always without consequences.
A paradigm of ALS: old is gold
What do machine learning systems do when they predict or recommend something? They use the student’s past experiences, preferences and performance in order to choose what to recommend to them; they look to the past in order to predict the future. Thus, these systems are always biased towards the past4. Machine learning works best in a static and stable world where the past looks like the future5. ALS, based on machine learning models does more or less the same thing, but now with the addition of pedagogical considerations.
As a consequence, these systems are not able to account for fluctuations in normality like the COVID pandemic, health issues and other problems. They can struggle to take account of age, growth, mastery of new competencies and personal evolution of young humans.
Is student behaviour even predictable? How many times can we repeat a formula that worked well in the past, before it becomes boring, repetitive and impedes progress6? Even if such a prediction were possible, is it even prudent to expose students only to that which they like and are comfortable with? How much novelty is overwhelming and counterproductive6?
It is difficult to decide how similar recommended activities should be, how many new types of activities should be introduced in one session and when would it be productive to push a student to face challenges and explore new interests. The answers do not lie in the students’ pasts alone.
A paradigm of ALS: the explicit reflects the implicit
Even where the past can be used reliably to predict