Adaptive learning is an approach in which a system changes what a learner sees next, such as the topic, difficulty or amount of practice, based on their answers and behavior. The goal is to give each person material matched to what they already know and what they still need to learn.
Why it matters
In a fixed course, some learners are bored and others lost. Adaptive systems try to reduce both by skipping mastered content and adding practice where there are gaps. They rely on a model of the learner, sometimes using machine learning, and on good question banks. Because they collect detailed data, privacy and transparency matter, and the quality of the underlying content still limits results.
Example
A rule-based adaptive quiz:
- Start at medium difficulty.
- Two correct answers in a row: move up one level.
- Two incorrect answers: move down one level and show a short explanation.
- After mastery on a topic at the highest level, skip to the next topic.
More advanced systems estimate the probability that the learner knows each skill and pick the next item accordingly.
Common misconceptions
- It replaces teachers. It supports practice; human feedback and motivation still matter.
- Any personalized recommendation is adaptive learning. True adaptation responds to performance.
- More data automatically means better teaching.
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