使用多面模型和神经自动化作文得分测试连接作文写作测试
1The University of Electro-Communications, Tokyo, Japan. uto@ai.lab.uec.ac.jp.
Behavior research methods
|August 20, 2024
概括
这项研究引入了一种新的神经自动化作文评分方法,用于链接物品响应理论 (IRT) 模型. 这种新的方法准确地将不同受试者群体的评分参数连接起来,克服了传统测试连接的实际局限性.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 教育中的人工智能
背景情况:
- 文章评分受到评分者的变化 (严重性,一致性) 的挑战.
- 项目响应理论 (IRT) 模型,就像多面的拉什模型一样,在能力估计中解决评价者效应.
- 当不同的受试者-评分组存在时,测试链接对于统一规模至关重要,但传统方法通常是不切实际的.
研究的目的:
- 开发一种新的方法,将IRT模型与评级参数联系起来.
- 为了克服传统的测试链接方法在现实世界的场景中的不切实际性.
- 为了准确地统一测量尺度跨不同的受试者-评分组.
主要方法:
- 使用神经自动化作文评分技术.
- 开发了一种新的方法来链接IRT模型和评级参数.
- 将新方法与传统的线性链接进行了比较.
主要成果:
- 拟议的方法成功完成了测试链接.
- 新方法的准确性与线性链接相当.
- 即使只有少数共同的受试者,也实现了有效的联系.
结论:
- 神经自动化作文评分为教育测量中的复杂测试链接提供了可行的解决方案.
- 这种新方法提高了在作文测试中的能力估计的准确性和实用性.
- 这种方法有助于在不同评级组之间进行更可靠的评分比较.
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