Teaching Result Analysis Using Rough Sets and Data Mining

TL;DR

This paper introduces a method combining rough set theory and data mining to analyze student learning results and recommend personalized remedial teaching strategies based on concept maps.

Contribution

It presents a novel approach integrating rough sets with data mining for educational data analysis and personalized teaching recommendations.

Findings

Effective identification of student performance patterns

Personalized remedial teaching sequences generated

Utilization of rough set theory to handle data uncertainty

Abstract

The development of IT and WWW provides different teaching strategies, which are chosen by teachers. Students can acquire knowledge through different learning models. The problem based learning is a popular teaching strategy for teachers. Based on the educational theory, students increase their learning motivation, which can increase learning effectiveness. In this paper, we propose a concept map for each student and staff. This map finds the result of the subjects and also recommends a sequence of remedial teaching. Here, rough set theory is used for dealing with uncertainty in the hidden pattern of data. For each competence the lower and upper approximations are calculated based on the brainstorm maps.