一种潜在的迪里克莱特分配方法,以了解学生对自动化写作评估的看法
Joshua Wilson1, Saimou Zhang1, Corey Palermo2
1School of Education, University of Delaware, 213E Willard Hall Education Building, Newark, DE 19716, United States.
概括
美国的中学生希望从MI Write. Write等自动化写作评估 (AWE) 系统获得更个性化的反和更好的用户体验. 结果指导未来的AWE开发,以改善学生的学习.
科学领域:
- 教育技术的教育技术
- 人与计算机的交互
- 自然语言处理自然语言处理.
背景情况:
- 自动化写作评估 (AWE) 系统显示了提高学生写作能力的潜力.
- 研究中学生对AWE的看法是有限的,特别是在美国.
- 了解学生的观点对于有效实施AWE至关重要.
研究的目的:
- 调查美国中学生对MI Write AWE系统的看法.
- 确定学生反中关于AWE有用性和所需改进的关键主题.
- 根据AWE工具的感知有用性,探索人们对AWE工具的看法差异.
主要方法:
- 利用利克特尺度调查和开放式问题来收集学生的看法.
- 应用隐藏的迪里克莱特分配 (LDA) 来识别定性数据中的隐藏主题.
- 进行定性分析来解释主题,并检查学生群体之间的差异.
主要成果:
- 四个主要主题出现了:对深入反的渴望,增强的用户体验,个性化和更公平的自动评分.
- 学生发现系统有用的突出个性化 (主题3).
- 学生们没有发现系统有用,专注于需要更详细的反 (主题1).
结论:
- 学生对MI Write等AWE系统的看法是多方面的,包括反质量,用户体验,个性化和评分公平性.
- LDA有效地揭示了学生对教育技术的细微观点.
- 结果为改善中学学习者AWE系统设计和实施提供了可操作的见解.
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