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首页> 外文会议>IEEE International Conference on Advanced Learning Technologies >Indexing Learning Scenarios by the Most Adapted Contexts: An approach Based on the Observation of Scenario Progress in Session
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Indexing Learning Scenarios by the Most Adapted Contexts: An approach Based on the Observation of Scenario Progress in Session

机译：通过适应性最强的上下文为学习情景建立索引：一种基于会话情景观察进度的方法

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摘要

The BASAR project offers a repository of blended learning scenarios. This project aims to reuse and capitalize good teaching practices. A teacher-designer would have the ability to choose a scenario that matches his needs, to be modified, used and refined. Specializing the scenario to a given context often improves the learning quality. On the other hand, it increases the difficulty to reuse it in a different context. Knowing the appropriate contexts for a scenario is essential for better reusing a part of this scenario or all of it (granularity). So, how can we characterize the learning scenarios with their most appropriate contexts based on the observation of the learning sessions progress in order to enhance scenario retrieval? This paper proposes a multi-faceted approach to index learning scenarios using the context trees formalism. The main objective of this indexing is to facilitate the learning scenarios design by and for reuse.
机译：BASAR项目提供了混合学习方案的存储库。该项目旨在重用和利用良好的教学实践。教师设计者将能够选择符合其需求的方案，然后对其进行修改，使用和完善。将方案专门用于给定的上下文通常可以提高学习质量。另一方面，它增加了在不同上下文中重用它的难度。了解场景的适当上下文对于更好地重用此场景的一部分或全部（粒度）至关重要。那么，我们如何基于对学习会话进度的观察，以最合适的情境来表征学习场景，以增强场景检索？本文提出了一种使用上下文树形式主义的多角度方法来进行索引学习的方案。该索引的主要目的是通过重复使用来促进学习场景的设计。

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