A Comprehensive Analysis: Classification Techniques for Educational Data mining

Devangam Sai Chaithanya, Kallupalli Lakshmi Narayana, Maheh T R
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Abstract

Data mining is a popular trend these days, and it's being applied in a variety of fields, including student education and learning analytics. Educational Data-Mining (EDM) is a recent field of research that employs data mining (DM) techniques. It uses machine learning (ML) algorithms as well as statistical methodologies to assist the user in deciphering a student's learning habits, success in academics, and, if necessary, further progress. Manually analyzing data and uncovering hidden information is difficult and time-consuming. Classification will be employed in the article to improve educational data mining. We need to improve performance as well as the clarity of the models we acquire. In this study, we will cover various data mining strategies that can be used to forecast student performance levels.
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综合分析:教育数据挖掘的分类技术
如今,数据挖掘是一种流行趋势,它被应用于各种领域,包括学生教育和学习分析。教育数据挖掘(EDM)是一个采用数据挖掘(DM)技术的新兴研究领域。它使用机器学习(ML)算法和统计方法来帮助用户解读学生的学习习惯,在学术上的成功,并在必要时进一步进步。手动分析数据和发现隐藏信息既困难又耗时。本文将使用分类来改进教育数据挖掘。我们需要改进性能以及我们获得的模型的清晰度。在本研究中,我们将介绍可用于预测学生成绩水平的各种数据挖掘策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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