Purpose: Explore how different automated scoring (AS) models score reliably the expressive language and vocabulary knowledge in depth of young second grade Latino English learners. Design/methodology/approach: Analyze a total of 13,471 English utterances from 217 Latino English learners with random forest, end-to-end memory networks, long short-term memory, and other AS models. Findings: Random forest outperformed the other AS models as measured by the mean of quadratic weighted kappa (QWK = 0.70) followed by the end-to-end memory networks-long short-term memory (QWK = 0.69) across all tasks and data points. The QWK between humans was 0.90, while the human-machine agreement of three AS models and humans ranged from 0.66 to 0.70. Practical implications: Examine closely misclassifications between human and machine scoring to better understand the specific words and structures the systems were not capturing. Originality/value: Discuss findings in the context of developing efficient and reliable ways to analyze the natural speech of young English learners. This information could guide the vocabulary and language proficiency instruction in the early grades.

Citation Key: makotosano2020 <标题>: 使用手工转录语音数据和自动评分测量拉美裔英语学习者的表达性语言和词汇