Investigating Second Language (L2) Reading Subskill Associations: A Cognitive Diagnosis Approach

ABSTRACT

This study uses a cognitive diagnosis model (CDM) approach to investigate the associations among specific L2 reading subskills. Participants include 1,203 Year-4 English major college students randomly selected from the nationwide test takers of Band 8 of Test for English Majors (TEM8), a large-scale English proficiency test for senior English majors in China. Their English reading was measured using a reading comprehension subtest of the TEM8. Based on the CDM output on latent class size estimates, the chi-square test of independence was used to uncover the associations among reading subskills, and odds ratio estimation was used to determine the strengths of those associations. The CDM output on attribute mastery prevalence was used to establish the stochastic direction of the associations between reading subskills. The study has the following findings: a reading subskill network displaying significant subskill associations together with their strengths and directions can be established through a CDM approach, and the patterns of reading subskill associations based on cognitive levels and local/global comprehension resonate with major reading process models and reflect the hierarchical and compensatory characteristics of reading subskills.

摘要

本研究用认知诊断模型(CDM)研究了二语阅读子技能之间的关系。研究被试为1203位随机抽取的英语专业八级考试(TEM8)的考生。TEM8是一项针对中国大学英语专业四年级学生的大规模英语水平测试, 被试的英语阅读能力是通过该测试的阅读理解部分试题来测量的。以认知诊断分析输出的潜在类别比例为统计依据, 独立卡方检验可以用来揭示阅读子技能之间是否存在关系, 比值比估计可以用来确定这些关系的强度。认知诊断分析输出的属性掌握率则可以用来确立阅读子技能间关系的随机方向。研究发现:认知诊断方法可以揭示阅读子技能间存在的显著关系, 并估计这些关系的强度和方向;以认知水平和理解全局性为依据的阅读子技能关系模式体现了阅读过程的主要理论, 反映了阅读子技能的层次性和补偿性特征。

Acknowledgments

The data of this research were provided by the Committee of Test for English Majors in China

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15434303.2022.2140050

Additional information

Funding

This was funded by National Social Science Foundation in China (17BYY101)