Zhang Jianheng, Goh Tiong-Thye, Chen Dexin, Gong Yuan, Yang Bing, Pan Liqin, Song Ting, Yu Shiqi, Li Hanzhen
School of Computer Science, Hubei University, Wuhan, China.
School of Information Management, Victoria University of Wellington, Wellington, New Zealand.
Front Psychol. 2025 Aug 11;16:1494111. doi: 10.3389/fpsyg.2025.1494111. eCollection 2025.
Primary school is a critical period for children's language development, coinciding with rapid cognitive growth that supports the emergence of writing skills. Understanding how children's cognitive structures manifest in writing is essential for improving instructional strategies.
This study employed epistemic network analysis (ENA) to encode and analyze six years of student writing data. Cognitive network maps were constructed to examine developmental trends and differences across grades and genders from both subject-matter and cognitive perspectives.
The analysis demonstrates ENA's effectiveness in visualizing the cognitive features embedded in written texts. Distinct patterns emerged across subjects, grades, and genders, revealing a complex and nuanced cognitive network structure.
These findings highlight important nuances in children's writing development. Recognizing subject-specific, developmental, and gender-related cognitive differences can inform more personalized and effective writing instruction.
小学是儿童语言发展的关键时期，这一时期恰逢认知快速增长，有助于书写技能的出现。了解儿童的认知结构如何在写作中体现，对于改进教学策略至关重要。
本研究采用认知网络分析（ENA）对六年的学生写作数据进行编码和分析。构建认知网络图，从主题和认知两个角度研究各年级和性别的发展趋势及差异。
分析表明ENA在可视化书面文本中嵌入的认知特征方面有效。不同学科、年级和性别呈现出不同模式，揭示了一个复杂而细微的认知网络结构。
这些发现凸显了儿童写作发展中的重要细微差别。认识到特定学科、发展阶段和性别相关的认知差异，可为更个性化和有效的写作教学提供依据。
