当前位置: X-MOL 学术Phys. Rev. Phys. Educ. Res. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Maintaining item banks with the Rasch model: An example from wave optics
Physical Review Physics Education Research ( IF 3.1 ) Pub Date : 2021-02-05 , DOI: 10.1103/physrevphyseducres.17.010105
Džana Salibašić Glamočić , Vanes Mešić , Knut Neumann , Ana Sušac , William J. Boone , Ivica Aviani , Elvedin Hasović , Nataša Erceg , Robert Repnik , Vladimir Grubelnik

Item banks are generally considered the basis of a new generation of educational measurement. In combination with specialized software, they can facilitate the computerized assembling of multiple pre-equated test forms. However, for advantages of item banks to become fully realized it is important that the item banks store a relatively large number of valid test items. In this paper, we demonstrate how the Rasch model is used for integrating new items into an existing wave optics item bank. First, we identified and applied a set of criteria for selecting 18 linking items from our initial item bank. In order to integrate 12 newly developed items, we combined the 18 linking items with the 12 newly developed ones into one test and administered this test to 106 postinstruction physics students from 4 universities in Slovenia, Croatia, and Bosnia and Herzegovina. It was determined that the 12 new items measure the same construct as items from the initial item bank. In addition, all items exhibited good item fit, item reliability was excellent and person reliability was fair. The ratio of standard deviations of linking item difficulties for the new test and existing item bank amounted to 0.89 and correlation of these difficulties amounted to 0.93 which indicated good linking precision. We could conclude that good linking precision can be obtained if linking items are chosen based on the following set of criteria: number of items, item fit, range and spacing of item difficulties, content representativeness, position in test form and interuniversity DIF contrasts.

中文翻译:

使用Rasch模型维护物料库存:波动光学的示例

题库通常被认为是新一代教育评估的基础。与专用软件结合使用,它们可以简化多个预先确定的测试表格的计算机化组装。但是,为了充分发挥物料库的优势,重要的是,物料库存储相对大量的有效测试物料。在本文中,我们演示了如何使用Rasch模型将新项目集成到现有的波光学项目库中。首先,我们确定并应用了一组标准,以从初始项目库中选择18个链接项目。为了整合12个新开发的项目,我们将18个链接的项目与12个新开发的项目合并为一个测试,并对来自克罗地亚斯洛文尼亚4所大学的106名教学后物理学学生进行了该测试 和波斯尼亚和黑塞哥维那。已确定这12个新项目与初始项目库中的项目具有相同的构造。另外,所有物品均显示出良好的物品适合度,物品可靠性极好,人员可靠性还算公道。新测试和现有题库的链接项目难度标准偏差之比为0.89,这些难度的相关性为0.93,表明链接精度良好。我们可以得出结论,如果基于以下标准选择链接项,则可以获得良好的链接精度:项目数,项目适合度,项目难度的范围和间距,内容代表性,测试形式中的位置以及大学DIF对比。另外,所有物品均显示出良好的物品适合度,物品可靠性极好,人员可靠性还算公道。新测试和现有题库的链接项目难度标准偏差之比为0.89,这些难度的相关性为0.93,表明链接精度良好。我们可以得出结论,如果基于以下标准选择链接项,则可以获得良好的链接精度:项目数,项目适合度,项目难度的范围和间距,内容代表性,测试形式中的位置以及大学DIF对比。另外,所有物品均显示出良好的物品适合度,物品可靠性极好,人员可靠性还算公道。新测试和现有题库的链接项目难度标准偏差之比为0.89,这些难度的相关性为0.93,表明链接精度良好。我们可以得出结论,如果基于以下标准选择链接项,则可以获得良好的链接精度:项目数,项目适合度,项目难度的范围和间距,内容代表性,测试形式中的位置以及大学DIF对比。89和这些困难的相关性总计为0.93,这表明良好的链接精度。我们可以得出结论,如果基于以下标准选择链接项,则可以获得良好的链接精度:项目数,项目适合度,项目难度的范围和间距,内容代表性,测试形式中的位置以及大学DIF对比。89和这些困难的相关性总计为0.93,这表明良好的链接精度。我们可以得出结论,如果基于以下标准选择链接项,则可以获得良好的链接精度:项目数,项目适合度,项目难度的范围和间距,内容代表性,测试形式中的位置以及大学DIF对比。
更新日期:2021-02-05
down
wechat
bug