用AI自动评估南非儿童口语叙事，助力教师精准识别需要干预的孩子。
Automatically assessing oral narratives of Afrikaans and isiXhosa children
- 结合语音识别与机器学习模型，自动评分儿童口语叙事
- 大语言模型比线性模型表现更好，接近人工专家水平
- 适合教育科技、多语言语音分析领域的研究者和教师
早期儿童的语言叙事与理解能力发展对后期读写能力至关重要。然而，在大规模幼儿园班级中，教师难以准确识别需要干预的学生。本文提出一种针对阿非利卡语和科萨语儿童口语叙事的自动评估系统，该系统先通过自动语音识别获取转录文本，再利用机器学习评分模型预测叙事与理解得分。我们对比了线性模型与大语言模型（LLM）在评分上的表现，结果显示：尽管结构简单，线性模型仍具竞争力；而基于LLM的系统在多数情况下表现更优，其识别需干预儿童的能力接近人类专家水平。本研究为课堂中的自动化口语评估奠定了基础，使教师能将更多精力投入个性化教学支持。
原文摘要 · Abstract (English)
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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