通过比较各种数据挖掘技术分析学生的智力表现

A. Jain, T. Choudhury, Parveen Mor, A. Sabitha
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引用次数: 7

摘要

学生成绩数据是非常重要的教育数据,需要对其进行分析和研究,以便更好地建立建设性的模型来改进教育。教育数据挖掘(educational Data Mining, EDM)是处理这一问题以获得更好的教育模型的领域。教育数据挖掘是通过对教育数据应用数据挖掘算法来帮助教育组织。我们的研究试图将理论上可能影响学生研究生教育的多种社会因素联系起来。我们试图找到一个数据挖掘模型来最好地分类和预测基于这种相关性的学生表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intellectual performance analysis of students by comparing various data mining techniques
Student's performance data is an intensely important educational data which needs to be analyzed and studied for building better constructive models to improve education. The field which deals with this, to get us better educational model is known as Educational Data Mining (EDM). Educational Data Mining is concerned with helping educational organisations by applying data mining algorithms on educational data. Our research tries to correlate multiple social factors which can theoretically affect a student's graduate education. We try to find a data mining model to best classify and predict students performance based on this correlation.
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