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Adaptive Starting Points in Video Learning Environments for New Learners Based on Video and Topic Tree Relations

机译:基于视频和主题树关系的新学习者视频学习环境中的自适应起点

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Teaching and learning complex fields of knowledge, including engineering, is rarely easy, particularly because of rapid changes and high interdependences among different topics. In addition, learners are increasingly heterogeneous in terms of different experiences and levels of knowledge. Not dealing with increasingly heterogeneous learners appropriately may result in high dropout rates and poor training quality. A possible way out is addressing learners more individually. Hopefully, digital learning offers, such as video platforms, will help to convey knowledge better and more individually through a broader learning offer. But learners with different levels of knowledge need different starting points into a course based on their level of knowledge. Thus, a crucial problem is finding the most appropriate starting point for learners in such environments. This paper presents a novel approach to identify individual starting points in online video courses for learners with different levels of knowledge. The underlying algorithm identifies topics that are most useful for the concrete learner at the beginning of the course in order to introduce a new field of knowledge appropriately.
机译:教学和学习复杂的知识领域,包括工程,很少容易,特别是因为不同主题的快速变化和高相互依赖性。此外,在不同的经验和知识水平方面,学习者越来越异构。不适合越来越多地处理越来越多的学习者可能会导致率高的辍学率和衡量训练质量。一个可能的方式是单独解决学习者。希望,数字学习优惠,如视频平台,将通过更广泛的学习优惠来帮助更好地传达知识。但是具有不同知识层次的学习者需要基于他们的知识水平的课程不同的起点。因此,至关重要的问题是在这种环境中找到学习者最合适的起点。本文提出了一种新的方法,可以识别具有不同知识水平的学习者在线视频课程中的个人出发点。底层算法识别在课程开始时对混凝土学习者最有用的主题,以便适当地引入新的知识领域。

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