Tuesday, February 12, 2019

What is ‘adaptive’ learning?

Personalised ‘adaptive’ learning came top of this 2019 survey in L&D. Having spent a few years involved with an adaptive learning company, delivering real adaption to real learners, on scale, I thought I’d try to explain what it is, a taxonomy of adaptive learning. The problem is that the word has been applied to many things from simple pre-test assessment to full-blown algorithmic and machine learning adaption, and lots in-between. 
In essence it means adapting the online experience to the individual’s needs as they learn, in the way a personal tutor would adapt. The aim is to provide, what many teachers provide, a learning experience that is tailored to the needs of you as an individual learner. 
Benjamin Bloom, best know for his taxonomy of learning, wrote a now famous paper, The 2 Sigma Problem, which compared the lecture, formative feedback lecture and one-to-one tuition. It is a landmark in adaptive learning. Taking the ‘straight lecture’ as the mean, he found an 84% increase in mastery above the mean for a ‘formative feedback’ 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 tailored  one-to-one, because of the increase in on-task learning, is huge. 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. Online learning has to date mostly delivered fairly linear and non-adaptive experiences, whether it’s through self-paced structured learning, scenario-based learning, simulations or informal learning. But we are now in the position of having technology, especially AI, that can deliver what Bloom called ‘one-to-one learning’.
Adaption can be many things but at the heart of the process is a decision to present something to the learner based on what the system knows about the learners, learning or context.

Pre-course adaptive
Macro-decisions
You can adapt a learning journey at the macro level, recommending skills, courses, even careers based on your individual needs.
Pre-test
‘Pre-test’ the learner, to create a prior profile, before staring the course, then present relevant content. The adaptive software makes a decision based on data specific to that individual. You may start with personal data, such as educational background, competence in previous courses and so on. This is a highly deterministic approach that has limited personalisation and learning benefits but may prevent many from taking unnecessary courses.
Test-out
Allow learners to ‘test-out’ at points in the course to save them time on progression. This short-circuits unnecessary work but has limited benefits in terms of varied learning for individuals.
Preference
Ask or test the learner for their learning style or media preference. Unfortunately, research has shown that false constructs such as learning styles, which do not exist, make no difference on learning outcomes. Personality type is another, although one must be careful with poorly validated outputs from the likes of Myers-Briggs. The OCEAN model is much better validated. One can also use learner opinions, although this is also fraught with danger. Learners are often quite mistaken, not only about what they have learnt but also optimal strategies for learning. So, it is possible to use all sorts of personal data to determine how and what someone should be taught but one has to be very, very careful.

Within-course adaptive
Micro-adaptive courses adjust frequently during a course to determine different routes based on their preferences, what the learner has done or based on specially designed algorithms. A lot of adaptive software within courses uses re-sequencing. The idea is that most learning goes wrong when things are presented that are either too easy, too hard or not relevant for the learner at that moment. One can us the idea of desirable difficulty here to determine a learning experience that is challenging enough to keep the learner driving forward.
Preference
Decision within a course are determined by user choices or assessed preferences. There is little evidence that this works.
Rule-based
Decisions are based on a rule or set of rules, at its simplest a conditional if… then… decision but I often a sequence of rules that determine the learner’s progress.
Algorithm-based
It is worth introducing AI at this point, as it is having a profound effect on all areas of human endeavour. It is inevitable, in my view, that this will also happen in the learning game. Adaptive learning is how the large tech companies deliver to your timeline on Facebook/Twitter, sell to you on Amazon, get you to watch stuff on Netflix. They use an array of techniques based on data they gather, statistics, data mining and AI techniques to improve the delivery of their service to you as an individual. Evidence that AI and adaptive techniques will work in learning, especially in adaption, is there on every device on almost every service we use online. Education is just a bit of a slow learner.
Decisions may be based simply on what the system thinks your level of capability is at that moment, based on formative assessment and other factors. The regular testing of learners, not only improves retention, it gathers useful data about what the system knows about the learner. Failure is not a problem here. Indeed, evidence suggests that making mistakes may be critical to good learning strategies.
Decisions within a course use an algorithm with complex data needs. This provides a much more powerful method for dynamic decision making. At this more fine-grained level, every screen can be regarded as a fresh adaption at that specific point in the course.
Machine learning adaption
AI techniques can, of course, be used in systems that learn and improve as they go. Such systems are often trained using data at the start and then use data as they go to improve the system. The more learners use the system, the better it becomes.
Confidence adaption
Another measure, common in adaptive systems, is the measurement of confidence. You may be asked a question then also asked how confident you are of your answer.
Learning theory 
Good learning theory can also be baked into the algorithms, such as retrieval, interleaving and spaced practice. Care can be taken over cognitive load and even personalised performance support provided adapting to an individuals availability and schedule. Duolingo is sensitive to these needs and provides spaced-practice, aware of the fact that you may have not done anything recently and forgotten stuff. Embodying good learning theory and practice may be what is needed to introduce often counterintuitive methods into teaching, that are resisted by human teachers.

Across courses adaptive
Aggregated data
Aggregated data from a learner’ performance on a previous or previous courses can be used. As can aggregated data of all students who have taken the course. One has to be careful here, as one cohort may have started at a different level of competence than another cohort. There may also be differences on other skills, such as reading comprehension, background knowledge, English as a second language and so on.
Adaptive across curricula
Adaptive software can be applied within a course, across a set of courses but also across an entire curriculum. The idea is that personalisation becomes more targeted, the more you use the system and that competences identified earlier may help determine later sequencing.

Post-course adaptive
Adaptive assessment systems
There’s also adaptive assessment, where test items are presented, based on your performance on previous questions. They often start with a mean test item then select harder or easier items as the learner progresses.
Memory retention systems
Some adaptive systems focus on memory retrieval, retention and recall. They present content, often in a spaced-practice pattern and repeat, remediate and retest to increase retention. These can be powerful systems for the consolidation of learning.
Performance support adaption
Moving beyond courses to performance support, delivering learning when you need it, is another form of adaptive delivery that can be sensitive to your individual needs as well as context. These have been delivered within the workflow, often embedded in social communications systems, sometimes as chatbots.

Conclusion
There are many forms of adaptive learning, in terms of the points of intervention, basis of adaption, technology and purpose. If you want to experience one that is accessible and free, try Duolingo, with 200 million registered users, where structured topics are introduced, alongside basic grammar 

 Subscribe to RSS

0 Comments:

Post a Comment

<< Home