Elementary School ELLs' Reading Skill Profiles Using Cognitive Diagnosis Modeling: Roles of Length of Residence and Home Language Environment

Abstract

The study examined differences in reading achievement and mastery skill development among Grade‐6 students with different language background profiles, using cognitive diagnosis modeling applied to large‐scale provincial reading test performance data. Our analyses revealed that students residing in various home language environments show different reading achievement growth patterns. Earlier gaps in their reading achievement disappear the longer they reside in the target language community. Additionally, students who come from home environments where they use English and another language equally demonstrate higher skill mastery achievement levels, indicating that immigrant students' diverse home language environments do not adversely affect their reading achievement in the longer term. The study results support the evidence that multilingual home language environments are not a cause of low achievement; however, the achievement patterns of Canadian‐born English language learners (ELLs) do differ from their immigrant counterparts, revealing that time alone is not a sufficient condition of reading skill achievement. ELLs' outperformance of monolinguals after 5 years of residence is a result of ongoing instructional support and a rich linguistic environment. The study results hold important policy implications: The evaluation of ELLs' academic achievement and school effectiveness for accountability purposes should be based on longitudinal data that track their developmental growths.

Investigating Linguistically Diverse Adolescents’ Literacy Trajectories Using Latent Transition Modeling

“…This finding is similar to that of Jang et al. (), who found that students learning English as an additional language had the highest level of reading achievement of all students after having been in Canada for five or more years. The second most common grade 6 status for the four multilingual home‐language groups was Stronger in Writing, which is similar to Y.‐H.…”

“…Classifying language learners solely based on birthplace, English proficiency, or immigration status fails to recognize the heterogeneous nature of language learners (Jang et al., ; Lesaux & Kieffer, ). Our sample consisted of students from a range of home‐language environments, from homes where only English was heard and spoken to homes where primarily language(s) other than English were heard and spoken.…”

Long-term effects of Learning English

“…While recent research reports have claimed evidence counter to the hypothesis that there is a critical period for language acquisition (Jang, et al 2013 ;Munoz and Llanes 2014 ;Schimd et al 2014 ;Service et al 2014 ), the available literature is generally consistent with the three generalizations presented above if we concentrate on research that has investigated the role of the age factor in naturalistic learners and in school-based learners.…”

Evaluación de la Comprensión Lectora mediante un Modelo de Diagnóstico Cognitivo

“…Estos últimos han sido mucho más comunes en comprensión de lectura (Buck, Tatsuoka, & Kostin, 1997;Buck, Van Essen, Tatsuoka, Kostin, Lutz, & Phelps, 1998;Hemmati, Baghaei, & Bemani, 2016;Jang, 2009;Ravand, Barati, & Widhiarso, 2013;Ravand, 2016;von Davier, 2005). Algunos conocidos modelos de tipo no compensatorio son el Rule Space Model (Svetina, Gorin, & Tatsuoka, 2011;Tatsuoka, 2009), el Attributes Hierarchy Model o AHM (Wang & Gierl, 2011), el modelo Deterministic-Input, Noisy-And-Gate, DINA, el modelo Reparameterized Unified Model (RUM) o su sutil derivación, el Fusion Model (Jang, 2009) y el modelo Reduced Reparameterized Unified Model, o RRUM (Jang, 2009;Jang, Dunlop, Wagner, Kim, & Gu, 2013;Li, Hunter, & Lei, 2015), también reconocido como Noncompesatory Reparameterized Unified Model, NC-RUM (Ravand & Robitzsch, 2015). Por su parte, un conocido modelo compensatorio es el Deterministic-Input, Noisy-OR-Gate, DINO (Liao, Kuo, & Deenang, 2015;Park & Cho, 2017).…”