Establishing Effective Remedial Instruction Grouping Using the Rough Set Theory and Grey Structural Modeling

Abstract

In the field of education, the assessment of a student’s learning performance is based on his final course scores. Few people care about what is behind the numbers. Most of the time, the final scores represent the end of the course because students have already passed the subject. Low-level students especially, still have a lot of misconceptions, but they do not know how to make up for their poor grasp of the subject in preparation for future study. Instead of just giving students their scores, teachers are encouraged to provide remedial instruction to students for their future learning. This study aims to establish an effective method using rough set theory and grey structural modeling to determine which attributes affect students’ final scores and to cluster students accordingly. A rough set algorithm generates a set of attributes for an assessment list. Grey structural modeling (GSM) is then used to cluster students who have the same weaknesses in English. GSM changes from one dimension to two dimensions, and calculates the relative distance, so that cluster analysis can be performed. Targeted remedial instruction can then be given to each similar ability student grouping. The results revealed that through integrating the two theories, teachers could more effectively sort students into groups. Students benefit by coming to understand their weaknesses in English instead of just receiving a single score at the end of the semester, and they can learn with their peers as well. Teachers can adjust their teaching strategies and syllabus design based on the analytical results to target the students’ needs.