Automatically assessing oral narratives of Afrikaans and isiXhosa children

• URL: http://arxiv.org/abs/2507.13205v2
• Date: Fri, 18 Jul 2025 07:49:41 GMT
• Title: Automatically assessing oral narratives of Afrikaans and isiXhosa children
• Authors: Retief Louw, Emma Sharratt, Febe de Wet, Christiaan Jacobs, Annelien Smith, Herman Kamper,
• Abstract summary: We present a system for automatically assessing oral narratives of preschool children in Afrikaans and isiXhosa.<n>The system uses automatic speech recognition followed by a machine learning scoring model to predict narrative and comprehension scores.
• Score: 15.669164862460342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing narrative and comprehension skills in early childhood is critical for later literacy. However, teachers in large preschool classrooms struggle to accurately identify students who require intervention. We present a system for automatically assessing oral narratives of preschool children in Afrikaans and isiXhosa. The system uses automatic speech recognition followed by a machine learning scoring model to predict narrative and comprehension scores. For scoring predicted transcripts, we compare a linear model to a large language model (LLM). The LLM-based system outperforms the linear model in most cases, but the linear system is competitive despite its simplicity. The LLM-based system is comparable to a human expert in flagging children who require intervention. We lay the foundation for automatic oral assessments in classrooms, giving teachers extra capacity to focus on personalised support for children's learning.

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