本报告立足语篇教学的视角，探讨以素养发展为导向的外语教育价值与方法。外语教学的语篇视角是外语教育从“碎片化知识传授”转向“综合能力培养”的关键视角，体现对语言学习者核心素养发展的充分关照。

语篇教学将语言视为在特定社会文化语境中传递意义、意涵、意图的语用单位，将外语教学视为培养学生跨文化语言运用和交际能力、形成综合素养的育人过程。语篇教学以语篇为基本单位，强调语言的实际使用和意义建构，突破以词汇和语法为中心的传统教学模式，通过语篇和语用学习，聚焦和实现语言在具体语境中的意义建构与交流，帮助学生建立语言知识与实际运用的关联，使外语教育回归“语言、文化、思维”融合、“工具性与人文性”统一的语言学习和运用的本质。

外语教学的语篇视角秉持语言学习和运用的社会文化观，摒弃孤立学习语言知识、机械操练语言技能的传统语言教学方式，认为外语教学本质上是培养和塑造学生的价值观、文化品格、思维方式、社会情感和行为规范的综合育人活动，旨在培养和提升学生基于情境、场合、话题、对象、目的等要素，在真实世界中有效学习和运用语言表达思想、构建关系、解决问题的社会文化能力。

How to Prepare and Select Research Topic for Your Dissertation如何进行博士论文选题准备？

批评性话语分析在传播学研究中的运用及常见问题

In this talk, we present a novel representation-based transfer learning framework for time series analysis within the vector autoregression paradigm. We develop a two-stage regularized estimation methodology and establish its non-asymptotic theoretical properties. The proposed approach enhances estimation accuracy and forecasting performance in the target domain by effectively leveraging information from related source datasets through representation learning. A distinctive feature of our method is its capability to handle datasets with varying sequence lengths and asynchronous starting/ending points, thereby offering remarkable flexibility in integrating information from diverse datasets. Extensive simulation studies and an empirical analysis conducted on a comprehensive collection of macroeconomic time series across multiple countries demonstrate the superior performance of our transfer learning framework compared to conventional single-task learning methods, particularly in scenarios involving high-dimensional target datasets with limited sample sizes.