A Survey on University Students’ English Vocabulary M-Learning

Mobile-learning can help learners select favorite learning resources at anytime and anywhere,and breaks the limitation of time and space and increase students'interests and initiatives.But it also causes some problems.How to use M-learning to improve the efficiency of vocabulary learning has drawn much attention.Based on theoretical discussion,this paper carried out a survey on university students'vocabulary M-learning situation,the effectiveness and their attitude to M-learning.And then it puts forward some suggestions about vocabulary learning.

Physics informed machine learning: Seismic wave equation

Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.

论线上汉语学习:以YouTube教学视频的应用为例

YouTube平台是诸多为汉语作为外语(CFL)学习的学生提供优质免费学习资源的线上平台之一。本文旨在探讨YouTube视频在CFL教学过程中所扮演的角色和影响。为此,本文首先在西班牙巴塞罗那的汉语学习者中展开了问卷调查,重点调查他们观看YouTube平台上的汉语学习视频的习惯。问卷调查结果不仅揭示了学习者们对于特定类型的视频内容或形式的偏好,同时也反映出只有极少部分的YouTube资源被学生们用来作为对他们的正规语言学习内容的补充。之后,本文重点从视频长度、汉字书写规则以及视频内容所涵盖的语言等级等方面研究了19个汉语学习频道。最后,本文就如何更有效地利用如YouTube视频等宝贵的线上资源进行CFL教学提出了一些建议。

Factors Affecting Online Learning Stickiness from the Perspective of Comprehensive Learning Theory

Based on the concept of learning stickiness,this study constructed a model of influencing factors among the elements of online learning content,interaction,incentive,satisfaction,and learning stickiness from Comprehensive Learning Theory.Structural equation modeling was used to analyze the interaction and influence effects among the factors.It is found that the content,interaction,and incentive in Comprehensive Learning Theory had a significant positive impact on learning stickiness from the total effect analysis.From the direct effect analysis,the influence of content and interaction on learning stickiness was not substantial,but the influence of incentive and satisfaction on learning stickiness was significant.From the perspective of mediation effect analysis,incentive and satisfaction were critical mediating variables for the influence of content and interaction on learning stickiness.This study put forward suggestions and strategies for online teaching,providing a reference for teachers to carry out online education.

Design-Based Research on the Development and Implementation of AI Courses from the Perspective of University-Enterprise Cooperation

In recent years,China has been issuing artificial intelligence(AI)education policies to promote the deep integration between AI and education.Setting AI courses at the basic education phase can cultivate students’AI literacy and enhance the country’s science and technology competitiveness in the future.However,due to shortages of AI teachers and inadequate conditions for practice,the current AI courses in primary and secondary schools are not enough in effectiveness.In light of current problems,this study,based on the foundation of university-enterprise cooperation,integrates the“competition and education”mechanism into informal AI course design and practice.Using a design-based research paradigm,the study proposes the design framework from four dimensions:goals,themes and content,methods and steps,and evaluation,and then further refines the framework through two rounds of iterations,to provide a reference for the development and practice of AI courses.

Multi-Level Cross-Lingual Attentive Neural Architecture for Low Resource Name Tagging

Neural networks have been widely used for English name tagging and have delivered state-of-the-art results. However, for low resource languages, due to the limited resources and lack of training data, taggers tend to have lower performance, in comparison to the English language. In this paper, we tackle this challenging issue by incorporating multi-level cross-lingual knowledge as attention into a neural architecture, which guides low resource name tagging to achieve a better performance. Specifically, we regard entity type distribution as language independent and use bilingual lexicons to bridge cross-lingual semantic mapping. Then, we jointly apply word-level cross-lingual mutual influence and entity-type level monolingual word distributions to enhance low resource name tagging. Experiments on three languages demonstrate the effectiveness of this neural architecture： for Chinese,Uzbek, and Turkish, we are able to yield significant improvements in name tagging over all previous baselines.

Neural Networks with Local Converging Inputs(NNLCI)for Solving Conservation Laws,Part II:2D Problems

In our prior work[10],neural networks with local converging inputs(NNLCI)were introduced for solving one-dimensional conservation equations.Two solutions of a conservation law in a converging sequence,computed from low-cost numerical schemes,and in a local domain of dependence of the space-time location,were used as the input to a neural network in order to predict a high-fidelity solution at a given space-time location.In the present work,we extend the method to twodimensional conservation systems and introduce different solution techniques.Numerical results demonstrate the validity and effectiveness of the NNLCI method for application to multi-dimensional problems.In spite of low-cost smeared input data,the NNLCI method is capable of accurately predicting shocks,contact discontinuities,and the smooth region of the entire field.The NNLCI method is relatively easy to train because of the use of local solvers.The computing time saving is between one and two orders of magnitude compared with the corresponding high-fidelity schemes for two-dimensional Riemann problems.The relative efficiency of the NNLCI method is expected to be substantially greater for problems with higher spatial dimensions or smooth solutions.

Neural Networks with Local Converging Inputs(NNLCI)for Solving Conservation Laws,Part I:1D Problems

A novel neural network method is developed for solving systems of conservation laws whose solutions may contain abrupt changes of state,including shock waves and contact discontinuities.In conventional approaches,a low-cost solution patch is usually used as the input to a neural network for predicting the high-fidelity solution patch.With that technique,however,there is no way to distinguish a smeared discontinuity from a smooth solution with large gradient in the input,and the two almost identical inputs correspond to two fundamentally different high-fidelity solution patches in training and predicting.To circumvent this difficulty,we use local patches of two low-cost numerical solutions of the conservation laws in a converging sequence as the input to a neural network.The neural network then makes a correct prediction by identifying whether the solution contains discontinuities or just smooth variations with large gradients,because the former becomes increasingly steep in a converging sequence in the input,and the latter does not.The inputs can be computed from lowcost numerical schemes with coarse resolution,in a local domain of dependence of a space-time location where the prediction is to be made.Despite smeared input solutions,the output provides sharp approximations of solutions containing shock waves and contact discontinuities.The method works effectively not only for regions with discontinuities,but also for smooth regions of the solution.It is efficient to implement,once trained,and has broader applications for different types of differential equations.