Students’ 2018 PISA reading self-concept: Identifying predictors and examining model generalizability for emergent bilinguals

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Onur Ramazan , Shenghai Dai , Robert William Danielson , Yuliya Ardasheva , Tao Hao , Bruce W. Austin
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引用次数: 1

Abstract

Decades of research have indicated that reading self-concept is an important predictor of reading achievement. During this period, the population of emergent bilinguals has continued to increase within United States' schools. However, the existing literature has tended to examine native English speakers' and emergent bilinguals' reading self-concept in the aggregate, thereby potentially obfuscating the unique pathways through which reading self-concept predicts reading achievement. Furthermore, due to the overreliance of native English speakers in samples relating to theory development, researchers attempting to examine predictors of reading achievement may a priori select variables that are more aligned with native English speakers' experiences. To address this issue, we adopted Elastic Net, which is a theoretically agnostic methodology and machine learning approach to variable selection to identify the proximal and distal predictors of reading self-concept for the entire population; in our study, participants from the United States who participated in PISA 2018 served as the baseline group to determine significant predictors of reading self-concept with the intent of identifying potential new directions for future researchers. Based on Elastic Net analysis, 20 variables at the student level, three variables at the teacher level, and 12 variables at the school level were identified as the most salient predictors of reading self-concept. We then utilized a multilevel modeling approach to test model generalizability of the identified predictors of reading self-concept for emergent bilinguals and native English speakers. We disaggregated and compared findings for both emergent bilinguals and native English speakers. Our results indicate that although some predictors were important for both groups (e.g., perceived information and communications technologies competence), other predictors were not (e.g., competitiveness). Suggestions for future directions and implications of the present study are examined.

学生2018年PISA阅读自我概念:新兴双语者的预测因素和模型推广检验。
数十年的研究表明,阅读自我概念是阅读成绩的重要预测因子。在此期间,美国学校中新兴双语者的人数持续增加。然而,现有文献倾向于综合考察英语母语者和新兴双语者的阅读自我概念,从而可能混淆阅读自我概念预测阅读成就的独特途径。此外,由于在与理论发展相关的样本中过度依赖以英语为母语的人,试图检验阅读成绩预测因素的研究人员可能会先验地选择与以英语为母语的人的经历更一致的变量。为了解决这个问题,我们采用弹性网络,这是一种理论上的不可知论方法和机器学习方法来选择变量,以确定整个人群阅读自我概念的近端和远端预测因子;在我们的研究中,参加2018年国际学生评估项目的美国参与者作为基线组,以确定阅读自我概念的重要预测因素,目的是为未来的研究人员确定潜在的新方向。基于弹性网络分析,发现学生层面的20个变量、教师层面的3个变量和学校层面的12个变量对阅读自我概念的影响最为显著。然后,我们利用多层次建模方法来检验新兴双语者和英语母语者阅读自我概念预测因子的模型泛化性。我们对新兴双语者和母语为英语者的研究结果进行了分类和比较。我们的研究结果表明,尽管一些预测因素对两个群体都很重要(例如,感知信息和通信技术能力),但其他预测因素(例如,竞争力)并不重要。对未来的发展方向和本研究的意义提出了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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