Adaptive learning systems adapt to the learner, for example in terms of learning preferences or learning progress. Overall, the environment in which the learner works should be tailored individually and thus better to the learner.
Adaptive learning paths are an example of how the system can adapt to the learner's learning progress and speed. The system recognises during the learner chapter the level of knowledge acquired and makes suggestions as to which chapter to work on next. For example, the system can suggest repeating or skipping a certain chapter. A competence model is used to model the learner's "knowledge", which is constantly updated by the processing of tasks. This makes it possible to design learning paths even more individually.
With the help of web didactics, the system adapts to the learning type and the learning situation. The contents of chapters are individually prepared for the learner according to the type of learner.
Generic task types are independent of their characteristics. In other words, an author creates the task once and specifies the possible solutions. The system converts them into a specification, for example, a multiple choice task, depending on requirements and learning type. The next time the page is called, an assignment task can be generated from it instead of the multiple choice task.