在算法中哭泣:通过多层主题构建和ERA效应建模学术压力.
Liwei Ding1, Hongfeng Zhang1, Jinqiao Zhou1
1Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China.
Frontiers in psychology
|October 6, 2025
概括
学生在数字空间中从语义上构建学术压力. 预期-监管-放大 (ERA) 模型解释了外部期望,有限的资源和数字放大如何影响学生的压力和脆弱性.
科学领域:
- 教育心理学教育心理学
- 计算社会科学 计算社会科学
- 心理健康研究 心理健康研究
背景情况:
- 在日益激烈的教育竞争中,学术压力显著影响学生的心理健康.
- 关于学生如何在数字环境中从语义上表达学术压力的研究有限.
- 了解关于学术压力的数字话语对于心理健康干预至关重要.
研究的目的:
- 分析社交媒体评论中学术压力的语义构造.
- 通过使用新型主题建模框架,识别关键压力因素及其相互作用.
- 开发预期-监管-扩大 (ERA) 模型,以了解学生的脆弱性.
主要方法:
- 集成的BERTopic和隐藏的迪里克莱特分配 (LDA) 用于多层主题建模.
- 分析了33,827个用户生成的关于社交媒体对学术压力的评论.
- 在多层次压力理论中进行基础分析.
主要成果:
- 确定了六个相互关联的主题,反映了个人,情境和结构压力因素.
- 揭示了学术压力是如何在语言上复制和情感上加剧在线的.
- 开发了ERA模型,将学术压力概念化为一个动态过程.
结论:
- 该模型为理解学生在数字环境中的脆弱性提供了一个框架.
- 研究结果强调了期望,自我调节和学业压力放大之间的相互作用.
- 提供了针对教育和心理健康支持的有针对性的干预措施的见解.
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