Feature-based analysis of oral narratives from Afrikaans and isiXhosa children
It addresses early assessment of narrative proficiency in multilingual contexts, but is incremental as it builds on prior research with simple methods.
This study analyzed oral narratives from Afrikaans- and isiXhosa-speaking children to identify features predicting intervention needs, finding that specific verbs and auxiliaries correlated with reduced likelihood of requiring intervention.
Oral narrative skills are strong predictors of later literacy development. This study examines the features of oral narratives from children who were identified by experts as requiring intervention. Using simple machine learning methods, we analyse recorded stories from four- and five-year-old Afrikaans- and isiXhosa-speaking children. Consistent with prior research, we identify lexical diversity (unique words) and length-based features (mean utterance length) as indicators of typical development, but features like articulation rate prove less informative. Despite cross-linguistic variation in part-of-speech patterns, the use of specific verbs and auxiliaries associated with goal-directed storytelling is correlated with a reduced likelihood of requiring intervention. Our analysis of two linguistically distinct languages reveals both language-specific and shared predictors of narrative proficiency, with implications for early assessment in multilingual contexts.