人工智能驱动技术依赖的混合方法分析:在语言学习中,人格调节的途径导致口头英语焦虑
Xiaowei Wang1, Shuaijun Lin1, Bowen Chen1
1Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao.
Acta psychologica
|October 3, 2025
概括
这项研究揭示了技术依赖如何影响学生的口语英语焦虑. 心理负担加重了焦虑,而数字自我效能提供了一些保护,与性格特征,如外向和神经病症发挥关键作用.
科学领域:
- 教育技术的教育技术
- 心理学 心理学 心理学
- 应用语言学 应用语言学
背景情况:
- 智能教育系统普遍存在,但技术依赖和口语英语焦虑之间的心理联系尚未得到充分理解.
- 现有的研究缺乏一个全面的模型来解释这些复杂的关系.
研究的目的:
- 调查心理负担和数字自我效能在技术依赖和口头英语焦虑之间的关系中扮演的调解角色.
- 检查个性特征对这种关系的温和影响.
- 为技术增强的语言学习开发一个双流程模型和特征适应框架.
主要方法:
- 一种混合方法方法,将结构方程建模 (SEM) 与Python XGBoost-SHAP机器学习相结合.
- 调查数据来自中国四个主要城市的716名大学生.
- 在认知负载理论,社会认知理论和特征激活理论中的理论基础.
主要成果:
- 确定了一种双路机制:心理负担充当了补充的调解者,而数字自我效能则充当了竞争的调解者.
- 人格特征显著调节了这些影响,外向增强了性,神经病变增加了脆弱性.
- 混合方法分析通过捕捉非线性相互作用,提高了预测准确度.
结论:
- 该研究提出了一种技术依赖的新型双过程模型和个性化干预措施的特征适应框架.
- 研究结果强调需要"焦虑敏感"的智能辅导系统,这些系统考虑心理负担和人格特征.
- 建议强调适应性调整和个性化策略,以支持在技术增强的学习环境中学习者的心理健康.
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