论文标题
部分可观测时空混沌系统的无模型预测
PolyGloT: A Personalized and Gamified eTutoring System
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
The digital age is changing the role of educators and pushing for a paradigm shift in the education system as a whole. Growing demand for general and specialized education inside and outside classrooms is at the heart of this rising trend. In modern, heterogeneous learning environments, the one-size-fits-all approach is proven to be fundamentally flawed. Individualization through adaptivity is, therefore, crucial to nurture individual potential and address accessibility needs and neurodiversity. By formalizing a learning framework that takes into account all these different aspects, we aim to define and implement an open, content-agnostic, and extensible eTutoring platform to design and consume adaptive and gamified learning experiences. Adaptive technology supplementing teaching can extend the reach of every teacher, making it possible to scale 1-1 learning experiences. There are many successful existing technologies available but they come with fixed environments that are not always suitable for the targeted audiences of the course material. This paper presents PolyGloT, a system able to help teachers to design and implement a gamified and adaptive learning paths. Through it we address some important issues including the engagement, fairness, and effectiveness of learning environments. We do not only propose an innovative platform that could foster the learning process of different disciplines, but it could also help teachers and instructors in organizing learning material in an easy-access repository