标题： 自动评估南非语和科萨语儿童的口头叙述 显示英文标题

标题： Automatically assessing oral narratives of Afrikaans and isiXhosa children

摘要： 在幼儿时期发展叙述和理解能力对以后的读写能力至关重要。 然而，大型幼儿园班级的教师难以准确识别需要干预的学生。 我们提出了一种系统，用于自动评估南非语和科萨语的幼儿口语叙述。 该系统使用自动语音识别，然后是机器学习评分模型来预测叙述和理解分数。 在对预测的转录文本进行评分时，我们将线性模型与大型语言模型（LLM）进行比较。 基于LLM的系统在大多数情况下优于线性模型，但尽管线性系统较为简单，它仍具有竞争力。 基于LLM的系统在标记需要干预的儿童方面与人类专家相当。 我们为课堂中的自动口语评估奠定了基础，使教师能够额外发挥能力，专注于为儿童的学习提供个性化支持。 显示英文摘要

摘要： 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.