利用关系挖掘发现学生的学业状况

Haiyang Xia, Jiaxin Han, Jie Kong, Wenjuan Wei, Lei Zhang
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引用次数: 1

摘要

近年来,随着数据挖掘在教育领域的应用越来越广泛,在学生学习情境中寻找关联规则的研究也越来越多。目前的方法通常采用传统的关联规则挖掘技术来识别这些规则。然而,传统的关联规则挖掘技术无法识别不同类型学生学业状况之间的差异。为了解决这一问题,本文采用了一种新的对比目标规则挖掘方法。来自中国某大学计算机科学系的真实数据集,实证结果显示了不同类型学生在学业状况上的差异特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovering the Academic Situation of Students by Relationship Mining
While the data mining in education field gained more and more popularity in recent years, there have many research endeavors to find association rules in students' academic situation. The current methods normally apply traditional association rules mining technique to identify those rules. However, traditional association rules mining technique can not identify difference between different types of students' academic situation. To solve this problems, we applied a novel contrast target rules mining method in this paper. Real world data set from Computer Science department of a university of China, the empirical results show the difference characteristics of different types of students in their academic situation.
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