TITLE:
Analysis of Students’ Misconception Based on Rough Set Theory
AUTHORS:
Tian-Wei Sheu, Tzu-Liang Chen, Ching-Pin Tsai, Jian-Wei Tzeng, Cing-Ping Deng, Masatake Nagai
KEYWORDS:
Misconception; Rough Set Theory; Interpretive Structural Model; Diagnostic Assessment; Structural Graph
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.5 No.2,
May
20,
2013
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.