Feature-based analysis of oral narratives from Afrikaans and isiXhosa children

TL;DR

This study uses machine learning to analyze oral narratives from young Afrikaans and isiXhosa children, identifying linguistic features that predict literacy development and intervention needs across two languages.

Contribution

It introduces a feature-based analysis approach for early narrative assessment in multilingual children, highlighting language-specific and shared predictors.

Findings

Lexical diversity and utterance length are key indicators of development.

Use of goal-directed verbs correlates with lower intervention likelihood.

Cross-linguistic differences and similarities inform early literacy assessment.

Abstract

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.