Rasch Modeling: A Multiple Choice Chemistry Test

A. Winarti, A. Mubarak
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引用次数: 6

Abstract

The study aimed to reveal the difficulty level of items and the suitability of items of Chemistry test with the Rasch model. In addition to detecting this item quality, the Rasch model shows the student's answer pattern as well, so that the assessment can imply the quality of the instrument as an assessment of chemical learning. As many as 20 numbers of multiple-choice questions in chemical bonding material were analyzed by using WINSTEPS 3.73. The samples consisted of 200 senior high school students in Banjarmasin Indonesia. The results revealed that the average item measure was 0.00 with items (Measure Order = 4.64) which has the highest difficulty level. The Q10 was the item that has a level of conformity with the model, and outliers or misfit in Rasch were MNSQ=+0.97, ZSTD=-0.2, Pt Mean Corr=+0.58. In other words, assessment of learning with test techniques such as multiple choice based on Rasch model analysis was an effective way for teachers to review the progress of students in the learning process, guidelines for designing chemical learning strategies, and identifying students' understanding of chemical material.
Rasch建模:多项选择化学测试
本研究旨在利用Rasch模型揭示化学测验题目的难易程度和题目的适宜性。除了检测这个项目的质量外,Rasch模型还显示了学生的回答模式,因此评估可以暗示作为化学学习评估的仪器的质量。使用WINSTEPS 3.73软件对化学键材料中多达20道选择题进行分析。样本由印度尼西亚班加马辛的200名高中生组成。结果显示,平均项目测量值为0.00,其中难度最高的项目(测量顺序= 4.64)。Q10是与模型有一定程度一致性的项目,在Rasch中异常值或失配值为MNSQ=+0.97, ZSTD=-0.2, Pt Mean Corr=+0.58。换句话说,使用基于Rasch模型分析的多项选择等测试技术对学习进行评估,是教师回顾学生在学习过程中的进展、设计化学学习策略和确定学生对化学材料理解程度的有效途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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12 weeks
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