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Individualizing E-Learning Content

Successful learning via adaptive learning systems

In order to ensure sustainable successes in corporate learning, e-learning offerings should cover as many individual requirements as possible. Yet even the widely used web-based training (WBT) model is highly standardized and not suited to this need.

Adaptive learning systems

Adaptive learning systems analyze and interpret the user’s activity and adjust the learning content dynamically. Common methods already used, for example to assess the level of knowledge or personal preferences of the user, include Pre-Tests or Profiling (Likert scales).

If you want to take account of individual learning requirements and learning styles you should transform classic WBTs into adaptive learning systems.
Dynamically adjusting learning content

Based on the conclusions from this analysis, content and functions are assembled into an individual learning mix for each learner. Important preconditions for this are a far-reaching content-based and functional modularity and a high level of interactivity.

Users structure their world of learning

Alongside this outward individualization of the learning system, which is technically still in its infancy, the user should also step in to actively structure his world of learning.

A factually oriented user should, for example, be able to structure his e-learning course in such a way that avatars and scenarios can, in general, be edited out of the content.
Franz Rosky , Head of E-Learning at tts
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