通过自动化写作评估程序,提高基于书面表达的课程测量可行性
Michael Matta1, Milena A Keller-Margulis1, Sterett H Mercer2
1Department of Psychological, Health, and Learning Sciences, University of Houston.
School psychology (Washington, D.C.)
|March 20, 2025
概括
自动化写作评估 (AWE) 工具准确地评分学生的写作,与更简单的指标的人类评分密切匹配. 这些自动化得分显示没有偏见,并有效地预测国家写作测试的未来表现.
科学领域:
- 教育技术的教育技术
- 心理测量 心理测量 心理测量
- 写作评估 写作评估
背景情况:
- 自动写作评估 (AWE) 程序为学生的写作提供了可行的替代方案.
- 现有的AWE工具需要严格验证准确性,预测有效性和潜在偏差.
研究的目的:
- 评估基于书面表达的课程测量 (WE-CBM) 的自动成绩的准确性,预测有效性和偏差.
- 将自动化WE-CBM指标与传统的手工计分指标进行比较.
主要方法:
- 采集了2-5年级的722名学生的写作样本,使用3分钟的WE-CBM任务.
- 手工评分了四个WE-CBM指标,并使用基于计算机的方法为相同的指标生成了自动评分.
- 将自动化得分与手工得分的指标进行比较,并分析了对国家强制性写作测试绩效的预测.
主要成果:
- 简单的自动化指标 (词汇总数,正确拼写的词汇) 与手工计算的分数非常接近.
- 在更复杂的指标 (正确的单词序列) 中发现了小小的差异.
- 自动化得分准确地预测了州考试成绩,类似于手工得分的指标,没有针对非洲裔美国人和西班牙裔学生的偏见.
结论:
- 自动化WE-CBM评分表现出高准确性和预测有效性,与传统方法相比.
- 自动评分显示没有证据表明少数族裔学生群体的偏见.
- 调查结果支持在书面评估中用于教育决策的自动分数的使用.
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