Analysis of Students’ Misconception Based on Rough Set Theory

• 作者: Ching-Pin Tsai Cing-Ping Deng Jian-Wei Tzeng Masatake Nagai Tian-Wei Sheu Tzu-Liang Chen
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 医学
• 关键词: MISCONCEPTION ROUGH Set Theory Interpretive STRUCTURAL Model DIAGNOSTIC Assessment STRUCTURAL GRAPH
• 年，卷（期）: 2013,（2）
• 页码范围: 67-83
• 页数: 17页
• 分类号: R73

摘要

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.