重新排序任务的部分评分:线性矩阵的Excel

Amma Kazuo
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引用次数: 0

摘要

在语言测试和教育科学中,对项目重新排序任务的部分分数估计一直被忽视。笔者提出了一种心理上有效的评分方法——MRS (maximum Relative Sequence),并将其移植到Excel中。由于该协议简单地以相对升序拾取元素,它使非专业人员更容易访问和分析计算过程,从而具有教育和实际意义。但是,由于Excel枚举精确地复制了MRS的每一步,因此所消耗的列数随着元素数量的增加而激增。此外,MRS仅仅计算要重新定位的元素的数量;它没有考虑到回收的搬迁距离。本文提出了另一种解决方案LM(线性矩阵),也可以用Excel的基本函数执行,需要消耗的列要少得多。此外,LM优于MRS,因为它是估计两个序列相对相似性的一般协议,其中Kendall 's tau是一个特殊情况;LM可以通过改变所有元素组合的距离权重来调节邻接约束的程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Partial Scoring of Reordering Tasks Revisited: Linearity Matrix by Excel
Estimating a partial score of item reordering tasks has long been neglected in language testing and education sciences. A psychologically valid means of scoring, MRS (Maximal Relative Sequence) was proposed by the author and transplanted to Excel. As the protocol simply picks up elements in relative ascending order, it made easier the non-specialists’ access to and analysis of the calculation process resulting in educational as well as practical significance. However, since the Excel enumeration replicated each step of MRS precisely, the number of columns consumed explodes as the number of elements increases. Moreover, MRS merely counts the number of elements to be relocated; it fails to consider the distance of relocation for recovery. This paper proposes an alternative solution LM (Linearity Matrix), also executable with Excel’s basic functions, with far fewer columns to consume. Further, LM is advantageous over MRS in that it is a general protocol of estimating relative similarity of two sequences of which Kendall’s tau is a special case; LM is adjustable as to the degree of adjacency constraint by changing the distance weight for all combinations of elements.
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