Personalising Learning
24 AI-Speak: How Adaptive Systems Learn the Learner, part 1
When looking at an adaptive learning system, it is very hard to tell where it adapts1. What technology is used and what it is used for also changes across systems.
However, all adaptive learning systems know who they teach (knowledge about the learner), what they teach (knowledge about the domain), and how to teach (knowledge about pedagogy)2.
An ideal ALS adapts itself in multiple ways. In the outer loop, the sequence of learning activities is adapted – similar to Youtube adapting recommended list of videos. The outer loop might also personalise learning approaches and difficulty levels.
In the inner loop, within each activity, the ALS monitors step-by-step progress. It adapts feedback and hints to correct misconceptions, if any. It might also point to additional content if the student has a problem remembering a previously learnt concept. Some experts argue that the inner loop is best left to the instructor: not only is it costly and time-consuming to program all the rules for the specific subject and task, but the teacher’s knowledge and experience will always trump that of the machine3.
How adaptive systems learn the learner
Like all recommendation problems (See How Youtube Learns You Part 1), ALS splits the task into one or more surrogate questions that can be answered by the machine. Again, the choice of what to ask -and thus, what to predict, has a big impact on what recommendation is shown.
Marketing material often mention multiple goals – improved scores, employability and engagement. Given the proprietary nature of the systems, it is usually unclear what questions are coded into the systems, what goals are being optimised for, and how short-term goals are differentiated from long-term goals (example, mastery of a given content before progressing to the next level)4.
Where machine learning is used, regardless of the goals chosen, the prediction itself is based on other learners with similar skill levels and preferences. That is, learners whose models are similar.
The learner model
For creating a student model, developers ask what student characteristics are relevant to the learning process. Unlike teachers who can directly observe their students and adjust their approach, machines are limited to the data that can be collected and processed by them.
Typical characteristics considered in a student model:
- What does the student know – their knowledge level, skills and misconceptions5,2,6. These are usually inferred through assessments, for example, the answer a student submits for a maths problem1. This prior knowledge is then compared with what they will need to know at the end of the learning period.
- How does a student prefer to learn: the learning process and preferences5,6. For example, the number of times a student attempts a question before getting it correct, the types of resources consulted, the ratings they gave for an activity1, or the material t