Tell or Retell? The Role of Task and Language in Spanish-English Narrative Microstructure Performance.
Academic Article
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
PURPOSE: This study examined performance of dual language learners (DLLs) on Spanish- and English-language narrative story retells and unique tells. Transcription and analysis focused on comparisons of common microstructural language sample measures in Spanish and English across tasks. Each language sample measure was evaluated for its possible convergence with norm-referenced standardized assessments for DLL children. METHOD: Spanish-English DLLs (N = 133) enrolled in English-only kindergarten or first-grade classrooms completed two-language sample tasks (one in each language), which were transcribed and analyzed using Systematic Analysis of Language Transcripts (Miller & Iglesias, 2017) for measures of syntactic complexity (mean length of utterance [MLU] in words), lexical diversity (number of different words [NDW]), and grammaticality (percent grammatical utterances [PGU]). Students also completed a norm-referenced sentence repetition task (Pea et al., 2014) and expressive vocabulary assessment (Martin, 2013). RESULTS: Comparison of story retells and unique stories revealed similar performance on MLU, NDW, and PGU across elicitation techniques, with one exception: NDW in Spanish was higher in the story retell condition. Predictive models revealed several differences in the relations between the microstructure measures and norm-referenced language measures by elicitation technique, although neither context demonstrated a consistent advantage across all metrics. CONCLUSIONS: Measures derived from story retells and unique tells offer practical findings for speech-language pathologists and other educators to use in assessment of early grade DLLs. This work increases knowledge of procedural differences across narrative assessments and their influence on language variables, supporting school-based speech-language pathologists in making assessment decisions for DLLs on their caseload. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19191278.