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Abstract：
Automated writing evaluation (AWE) has shown promise in enhancing students\' writing outcomes. However, further research is needed to understand how AWE is perceived by middle school students in the United States, as they have received less attention in this field. This study investigated U.S. middle school students\' perceptions of the MI Write AWE system. Students reported their perceptions of MI Write\'s usefulness using Likert-scale items and an open-ended survey question. We used Latent Dirichlet Allocation (LDA) to identify latent topics in students\' comments, followed by qualitative analysis to interpret the themes related to those topics. We then examined whether these themes differed among students who agreed or disagreed that MI Write was a useful learning tool. The LDA analysis revealed four latent topics: (1) students desire more in-depth feedback, (2) students desire an enhanced user experience, (3) students value MI Write as a learning tool but desire greater personalization, and (4) students desire increased fairness in automated scoring. The distribution of these topics varied based on students\' ratings of MI Write\'s usefulness, with Topic 1 more prevalent among students who generally did not find MI Write useful and Topic 3 more prominent among those who found MI Write useful. Our findings contribute to the enhancement and implementation of AWE systems, guide future AWE technology development, and highlight the efficacy of LDA in uncovering latent topics and patterns within textual data to explore students\' perspectives of AWE.
摘要：
自动写作评估（AWE）在提高学生的写作成果方面表现出了希望。然而,需要进一步的研究来了解美国中学生如何看待AWE，因为他们在这一领域受到的关注较少。这项研究调查了美国中学生对MIWriteAWE系统的看法。学生使用Likert量表项目和开放式调查问题报告了他们对MIWrite\的有用性的看法。我们使用潜在狄利克雷分配(LDA)来识别学生评论中的潜在主题，然后进行定性分析，以解释与这些主题相关的主题。然后，我们检查了同意或不同意MIWrite是有用的学习工具的学生之间的这些主题是否有所不同。LDA分析揭示了四个潜在的话题：(1)学生渴望更深入的反馈，(2)学生渴望增强的用户体验，(3)学生重视MIWrite作为一种学习工具，但渴望更大的个性化，(4)学生希望提高自动评分的公平性。这些主题的分布根据学生对MIWrite有用性的评分而变化，主题1在通常不认为MIWrite有用的学生中更为普遍，而主题3在认为MIWrite有用的学生中更为突出。我们的发现有助于增强和实施AWE系统，指导未来AWE技术的发展，并强调LDA在发现文本数据中的潜在主题和模式以探索学生对AWE的看法方面的功效。
