Using embedded formative assessment to predict state summative test scores
Using embedded formative assessment to predict state summative test scores
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使用嵌入式形成性评估来预测州总结性测试分数
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
April Galyardt
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文献类型：
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作者：
Stephen E. Fancsali;Guoguo Zheng;Yanyan Tan;Steven Ritter;Susan R. Berman;April Galyardt
如果我们希望在教学中嵌入问责制评估，我们需要更好地理解不同类型的学习者数据对统计模型的相对贡献，这些模型预测用于问责制目的的评估分数。目前的工作规模扩大，并从现有的文献中扩展数学考试成绩的预测模型，并指定六类模型，将有关学生的先验知识，社会人口统计学和性能的MATHia智能辅导系统内的信息。线性回归和随机森林模型在每个类别中学习，并在迈阿密戴德县公立学校的6年级，7年级和8年级的23，000多名学习者的样本中进行推广。在简要探讨了这些数据的层次模型后，我们讨论了与这项工作相关的各种技术和实际应用、限制和开放性问题，特别是关于MATHia等教学平台作为耗时的标准化考试替代品的潜在用途。
If we wish to embed assessment for accountability within instruction, we need to better understand the relative contribution of different types of learner data to statistical models that predict scores on assessments used for accountability purposes. The present work scales up and extends predictive models of math test scores from existing literature and specifies six categories of models that incorporate information about student prior knowledge, socio-demographics, and performance within the MATHia intelligent tutoring system. Linear regression and random forest models are learned within each category and generalized over a sample of 23,000+ learners in Grades 6, 7, and 8 over three academic years in Miami-Dade County Public Schools. After briefly exploring hierarchical models of this data, we discuss a variety of technical and practical applications, limitations, and open questions related to this work, especially concerning to the potential use of instructional platforms like MATHia as a replacement for time-consuming standardized tests.