时间序列分析作为研究L2情感变量的新兴方法
Dan Xu1,2
1School of Advanced Translation and Interpretation, Dalian University of Foreign Languages, Dalian Liaoning, 116021, China.
Heliyon
|June 19, 2023
概括
线性建模与语言学习动态作斗争. 时间序列分析 (TSA) 提供了一种非线性方法,以捕捉随着时间的推移复杂的心理和情感变化,改善预测.
科学领域:
- 心理语言学 心理语言学
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 线性模型不足以捕捉语言学习中的复杂性和新出现的模式.
- 在语言学习中的心理,社会和语言因素表现出动态和非线性行为.
- 现有的模型往往无法代表语言发展中固有的创造力和不规则性.
研究的目的:
- 引入时间序列分析 (TSA) 作为一种适合语言研究的非线性建模技术.
- 证明TSA在表示语言学习中心理或情感变量的动态性的实用性.
- 探索TSA在预测学习者相关构造的细微变化的潜力.
主要方法:
- 时间序列分析 (TSA) 的理论框架概述.
- 解释TSA的技术特征和分析时间依赖数据的程序.
- 在语言研究中应用TSA的示范研究的审查.
主要成果:
- TSA有效地揭示了时间序列数据中的非线性变化.
- 该方法使复杂的,动态的现象的预测和反预测成为可能.
- 语言学习系统 (TSA) 可以为理解语言学习者随着时间的推移构建的细微变化做出重大贡献.
结论:
- 时间序列分析 (TSA) 是一种强大的工具,用于模拟语言学习中的复杂,非线性动态.
- 这种方法为心理和情感变量提供了比传统的线性建模的进步.
- 建议使用TSA进一步调查与语言相关的情感变量.
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