解释性文本概念选择方法对不同阅读表示的预测能力的影响
Guozhen Ye1, Shijia Zhang1, Xitong Huang1
1Center for Studies of Psychological Application, Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, China.
Acta psychologica
|November 30, 2023
概括
这项研究比较了阅读理解的术语选择方法. 术语分类 (TC) 方法,使用重要术语,比使用所有术语更好地预测阅读理解.
科学领域:
- 认知心理学 认知心理学
- 教育心理学教育心理学
- 自然语言处理自然语言处理.
背景情况:
- 有效的阅读理解依赖于对文本的准确表示.
- 传统方法使用所有术语,可能包括不相关的细节.
- 识别关键词对于构建有意义的阅读表征至关重要.
研究的目的:
- 为了比较两个术语选择方法 (所有术语与术语分类) 在生成阅读表示的有效性.
- 为了评估这些阅读表示如何预测阅读理解.
- 调查术语重要性 (核心,分支,细节) 对预测准确性的影响.
主要方法:
- 三项实验使用了主体内和主体间的设计.
- 使用"所有术语"和"术语分类" (TC) 方法生成阅读表示.
- 阅读理解的预测被评估使用邻近性得分和术语对相关性等级.
主要成果:
- 在实验1中,当使用传统方法时,相邻度评分比相关性评分更好地预测了阅读理解.
- 基于摘要写作的阅读表示是无效的预测器.
- 实验2证实,核心和分支术语比细节术语更强有力的预测.
- 实验3显示TC在相关性测量方面优越,而这两种方法在邻近性方面是平等的.
结论:
- 术语分类 (TC) 方法专注于核心和分支术语,更有效地生成预测阅读理解的阅读表示,特别是在测量术语相关性时.
- 这些发现对教育工具和阅读理解研究有影响.
- 未来的研究应该利用TC方法来改善阅读代表性的产生.
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