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Testing a Power Law Model of Knowledge Propagation: Case Study of the Out of Eden Walk Project

机译:测试知识传播的幂律模型:Out of Eden Walk项目的案例研究

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摘要

To improve teaching and learning, it is important to understand how knowledge propagates. In general, when a new piece of knowledge is introduced, people start learning about it. Since the potential audience is limited, after some time, the number of new learners starts to decrease. Traditional models of knowledge propagation are based on differential equations; in these models, the number of new learners decreases exponentially with time. Recently, a new power law model for knowledge propagation was proposed. In this model, the number of learners decreases much slower, as a negative power of time. In this paper, we compare the two models on the example of readers' comments on the Out of Eden Walk, a journalistic and educational project in which informative messages ("dispatches") from different parts of the world are regularly posted on the web. Readers who learned the new interesting information from these dispatches are encouraged to post comments. Usually, a certain proportion of readers post comments, so the number of comments posted at different times can be viewed as a measure characterizing the number of new learners. So, we check whether the number of comments is consistent with the power law or with the exponential law. To make a statistically reliable conclusion on which model is more adequate, we need to have a sufficient number of comments. It turns out that for the vast majority of dispatches with sufficiently many comments, the observed decrease is consistent with the power law (and none of them is consistent with the exponential law).
机译:为了改善教学,了解知识的传播方式非常重要。通常,当引入新知识时,人们便开始学习它。由于潜在的受众有限,一段时间后,新学习者的数量开始减少。传统的知识传播模型是基于微分方程的。在这些模型中,新学习者的数量随时间呈指数下降。最近,提出了一种新的幂律模型用于知识传播。在此模型中,学习者的数量减少得慢得多,这是负时间的力量。在本文中,我们将比较这两种模型,以读者对“出伊甸园之路”的评论为例,这是一个新闻和教育项目,来自世界各地的信息性消息(“调度”)定期发布在网络上。鼓励从这些分发中获得新的有趣信息的读者发表评论。通常,一定比例的读者发表评论,因此可以将在不同时间发表的评论数量视为表征新学习者数量的一种度量。因此,我们检查注释的数量是否符合幂律或指数律。为了对哪种模型更合适做出统计上可靠的结论,我们需要有足够数量的评论。事实证明,对于绝大多数带有足够多评论的调度而言,观察到的减少与幂定律是一致的(并且它们都不与指数定律一致)。

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