在人工智能辅助的词汇学习中,工作记忆能力和词汇记忆作为EFL阅读理解的预测因素
1School of Foreign Languages, Shandong Normal University, Jinan, Shandong, 250358, China.
Acta psychologica
|January 27, 2026
概括
人工智能辅助的词汇学习通过增强词汇保留来提高英语作为外语的阅读理解. 对于具有更高工作记忆能力的学习者来说,收益最强,这表明有针对性的练习是关键.
科学领域:
- 教育技术的教育技术
- 应用语言学 应用语言学
- 认知心理学 认知心理学
背景情况:
- 作为外语英语 (EFL) 学习者经常在获得词汇方面扎,这会影响阅读理解.
- 人工智能辅助工具提供了潜在的解决方案,但它们的有效性和潜在机制需要经验研究.
- 像词汇质量假设和认知负载理论这样的理论框架为我们对词汇学习和理解的理解提供了信息.
研究的目的:
- 研究人工智能辅助词汇学习对中国大学生EFL阅读理解的影响.
- 检查词汇记忆的调解作用和工作记忆能力 (WMC) 在这种关系中的调节作用.
- 确定利用人工智能工具来增强词汇获取和阅读技能的最佳策略.
主要方法:
- 涉及四个中国城市的本科生进行的横截面研究.
- 数据收集包括应用日志参与指标,词汇保留措施 (宽度,深度,延迟回忆),以及自我报告的工作记忆能力和阅读理解.
- 结构方程建模 (SmartPLS) 用于分析拟议的调节调解模型.
主要成果:
- 更高的人工智能辅助词汇学习参与度与更强的词汇记忆和更好的EFL阅读理解正相关.
- 词汇记忆在很大程度上调解了参与度和阅读理解之间的关系.
- 人工智能工具的感知实用性支持词汇保留,并对阅读产生了很小的直接影响.
- 词汇记忆和阅读理解之间的积极联系在具有更高工作记忆能力的人群中更为强烈.
结论:
- 人工智能辅助的词汇学习增强了EFL阅读理解能力,主要是通过提高词汇记忆.
- 工作记忆能力适度了词汇记忆对阅读理解的有效性,突出了个体差异.
- 教育实践应该从专注于参与时间转变为强调间隔复习的坚持,并为低WMC学习者提供支架,以减少认知负载并促进更深层次的融合.
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