Analysis of Students’ Misconception Based on Rough Set Theory

T. Sheu, Tzu-Liang Chen, Ching-Pin Tsai, J. Tzeng, C. Deng, M. Nagai
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引用次数: 9

Abstract

The study analyzed students’ misconception based on rough set theory and combined with interpretive structural model (ISM) to compare students’ degree of two classes. The study then has provided an effective diagnostic assessment tool for teachers. The participants were 30 fourth grade students in Central Taiwan, and the exam tools were produced by teachers for math exams. The study has proposed three methods to get common misconception of the students in class. These methods are “Deleting conditional attributes”, “Using Boolean logic to calculate discernable matrix”, and “Calculating significance of conditional attributes.” The results showed that students of Class A had common misconceptions but students of Class B had not common misconception. In addition, the remedial decision-making for these two classes of students is pointed out. While remedial decision-making of two classes corresponded to structural graph of concepts, it can be found the overall performance of the Class B was higher than Class A.
基于粗糙集理论的学生误解分析
本研究基于粗糙集理论,结合解释结构模型(ISM)对学生的误解进行分析,比较两班学生的程度。本研究为教师提供了一种有效的诊断评估工具。研究对象为30名中部地区的四年级学生,考试工具由教师制作,用于数学考试。该研究提出了三种方法来了解学生在课堂上常见的误解。这三种方法分别是“删除条件属性”、“使用布尔逻辑计算可分辨矩阵”和“计算条件属性的重要性”。结果表明,A班学生存在普遍的误解,而B班学生没有普遍的误解。此外,还指出了这两类学生的补救决策。虽然两个班级的补救决策对应于概念结构图,但可以发现B班级的整体表现高于A班级。
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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