Predicting Reading Self-Concept for English Learners on 2018 PISA Reading

Autor/inn/en	Ramazan, Onur; Dai, Shenghai; Danielson, Robert William; Hao, Tao; Ardasheva, Yuliya
Titel	Predicting Reading Self-Concept for English Learners on 2018 PISA Reading
Quelle	(2022), (16 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Secondary School Students; Foreign Countries; International Assessment; Reading Skills; Self Concept; English Language Learners; Native Speakers; Predictor Variables; Language Usage; Student Characteristics; Teacher Characteristics; Institutional Characteristics; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Sekundarschüler; Ausland; Reading skill; Lesefertigkeit; Selbstkonzept; Muttersprachler; Prädiktor; Sprachgebrauch
Abstract	Reading self-concept plays a significant role in academic achievement. Considering increasing numbers of English learners (ELs) in the United States, there is an urgent need to investigate self-perceptions of ELs in comparison to those of native English speakers (NES). We applied Elastic Net analysis (ENET), a machine learning approach, to PISA 2018 data to identify the proximal and distal predictors of EL and NES students' reading self-concept. Unlike in earlier work, the ENET in the current study was separately employed for ELs and NESs after splitting the dataset for those subgroups. Contributions of ENET-selected predictors of EL and NES students' reading self-concept will be investigated in the full paper by conducting three-level multilevel modeling analyses, separately for each student population. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01