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Assessing mathematics learning achievement using hybrid rough set classifiers and multiple regression analysis

机译：使用混合粗糙集分类器和多元回归分析评估数学学习成绩

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摘要

Education is recognized as the key to individual success. Particularly, elementary education is vital for providing students with basic literacy and numeracy, as well as establishing foundations in mathematics, language, science, geography, history, and other social sciences. Mathematics is fundamental to numerous fields with real life applications, including natural science, engineering, medicine, and social sciences; therefore, student mathematics-learning achievement (MLA) in elementary school is valuable. This study aims to eliminate wastage of educational resources and seek suitable hybrid models for application to education. This study proposes an integrated hybrid model based on rough set classifiers and multiple regression analysis and provides a new trial of such a hybrid model to process MLA problems for elementary schools and their teachers. The proposed model is illustrated by examining a dataset from an elementary school in Taiwan. The experimental results reveal that the proposed model outperforms the listing methods in both classification accuracy and standard deviation. The rough set LEM2 (Learning from Examples Module, version 2) algorithm generates a set of comprehensible decision rules that can be applied in a knowledge-based education system designed for interested parties. Consequently, the analytical results have important implications for strategies related to mathematics teaching/learning and support to achieve teaching goals related to mathematics education.
机译：教育被认为是个人成功的关键。特别是，基础教育对于为学生提供基本的识字和计算能力以及建立数学，语言，科学，地理，历史和其他社会科学的基础至关重要。数学是许多实际应用领域的基础，包括自然科学，工程学，医学和社会科学。因此，小学的学生数学学习成绩（MLA）是有价值的。这项研究旨在消除教育资源的浪费，并寻求适用于教育的混合模型。这项研究提出了一种基于粗糙集分类器和多元回归分析的综合混合模型，并为这种混合模型提供了新的试验，以处理小学及其教师的MLA问题。通过检查台湾一所小学的数据集来说明所提出的模型。实验结果表明，该模型在分类准确度和标准差方面均优于列表方法。粗糙集LEM2（从示例模块中学习，版本2）算法生成一组可理解的决策规则，这些规则可应用于为感兴趣的参与者设计的基于知识的教育系统中。因此，分析结果对与数学教学/学习有关的策略和支持实现与数学教育有关的教学目标具有重要意义。

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