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

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.

Relationships Among English Language Proficiency, Self-efficacy, Motivation, Motivational Intensity, and Achievement in an ESP/EAP Context.

This study, the first of the kind in the field of English for specific purposes, examined direct and indirect relationships among English language proficiency, English public speaking (EPS) motivation, motivational intensity, self-efficacy, and EPS achievement. The sample consisted of 189 non-English-major students. The final structural equation model yielded an acceptable fit to the data and explained 23.4% of the variance in EPS achievement. English language proficiency and EPS self-efficacy had both direct and indirect (via, respectively, self-efficacy and motivational intensity) impacts on EPS performance. Ought-to self emerged as the strongest contributor to explaining motivation (R2 = .90), followed by learning experience (R2 = .57), and ideal self (R2 = .32). Implications are discussed.

New Directions in L2 Self-efficacy Research: Comparing Bifactor and Second-Order Models in the English Public Speaking Domain.

Reflecting an alternative model comparison framework and grounded in Bandura's self-efficacy theory and in western and Chinese English Public Speaking (EPS) competences literature, we examine the possibility of a hierarchical structure of the EPS self-efficacy construct. In particular, we contrast an established first-order model of the EPS self-efficacy with two higher-order rival models, testing for subordinate (second-order model) versus nested (bifactor model) relationships in a sample of 203 Chinese EFL college students. From a theoretical perspective, the results supported the bifactor model as a strong conceptual foundation for understanding the EPS self-efficacy construct as nested. From a practical perspective, this conceptualization would allow for gauging EPS self-efficacy development both holistically and by subdomains. From a research methodology perspective, this study is among the first to bring the bifactor model into the broader field of L2 research. Other implications are discussed.