Sociocultural Mediation Through Large Language Models: Enhancing Learner Agency and Identity in Chinese EFL Speaking Contexts

Sociocultural Mediation Through Large Language Models: Enhancing Learner Agency and Identity in Chinese EFL Speaking Contexts This dataset supports a mixed-methods study investigating the role of large language models (LLMs) in mediating learner agency and identity construction in Chinese EFL speaking contexts, grounded in Sociocultural Theory (SCT). Study Details- Intervention: 8-week LLM-based speaking practice (DeepSeek/Kimi Chat) delivered via WeChat to 55 intermediate Chinese EFL university students (CEFR B1-B2).- Data Types: 1. Qualitative: Anonymized interview transcripts (n=12) and human-curated thematic coding sheets. 2. Quantitative: Online Student Engagement Scale (OSE; n=28), Voice Recording Task (VRT) proficiency scores (pre/post/inter-rater reliability; n=12), and weekly comfort/app usage feedback (n=55). 3. Visualizations: Final high-resolution figures of thematic frequencies, correlation matrices, and proficiency gains.- Ethical Compliance: Anonymized data with ethical approval from the College of Foreign Languages and Cultures,University A (Approval Number: CFLC_2025010_00X). All participants provided informed consent for data sharing. Dataset StructureThe dataset is organized into 3 core folders for transparency and replicability:1. Raw Data: Original, unprocessed data collected during the study.2. Processed Data: Human-curated cleaned, coded, and merged data (including statistical appendices).3. Figures: Final figures used in the study’s results section. ReplicabilityThis dataset enables full replication of the study’s mixed-methods findings. No automated scripts are required—all steps can be replicated using standard office software (Microsoft Excel, Word) and statistical tools (SPSS, R, Python) for quantitative analysis. A detailed README.txt file provides step-by-step guidance for replication. ## LicenseCC BY 4.0 (Attribution 4.0 International)《基于大语言模型的社会文化中介：中国英语作为外语口语语境中提升学习者能动性与身份建构》 本数据集支持一项以社会文化理论（Sociocultural Theory, SCT）为基础的混合方法研究，旨在探讨大语言模型（Large Language Model，LLM）在中国英语作为外语（English as a Foreign Language, EFL）口语语境中对学习者能动性与身份建构的中介作用。 研究详情 ——干预方案：面向55名达到欧洲语言共同参考框架（Common European Framework of Reference for Languages, CEFR）B1-B2级的中国高校中级英语外语学习者，通过微信开展为期8周的基于大语言模型的口语练习（DeepSeek/ Kimi Chat）。 ——数据类型： 1. 质性数据：12份匿名化访谈转录文本（n=12）与人工整理的主题编码表； 2. 量化数据：在线学生参与度量表（Online Student Engagement Scale, OSE；n=28）、语音录制任务（Voice Recording Task, VRT）熟练度评分（含前测、后测与评分者信度；n=12），以及每周使用舒适度与应用使用反馈（n=55）； 3. 可视化数据：用于展示主题频率、相关矩阵与熟练度提升幅度的最终高清图表。 ——伦理合规：本数据集为匿名化数据，已获得A大学外国语学院伦理审查批准（批准号：CFLC_2025010_00X）。所有参与者均已签署知情同意书，同意数据共享。 数据集结构 为保障研究透明度与可重复性，本数据集分为3个核心文件夹： 1. 原始数据：研究期间收集的原始未处理数据； 2. 处理后数据：经人工整理、清洗、编码与合并的数据（含统计附录）； 3. 图表：用于研究结果部分的最终图表。 可复现性 本数据集可完整复现本研究的混合方法研究结果。无需使用自动化脚本——所有步骤均可通过标准办公软件（Microsoft Excel、Word）与量化分析统计工具（SPSS、R、Python）完成复现。附带的详细README.txt文件将提供分步复现指南。 授权协议：CC BY 4.0（国际署名4.0协议）