基于k -均值聚类方法的学生学习绩效分析

S. Shankar, Bishal Dey Sarkar, S. Sabitha, D. Mehrotra
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引用次数: 17

摘要

全球所有领域的大量数据都必须进行管理,并由决策者使用,以从中获得一些富有成效的东西。对哈佛大学在线课程14000×5的大数据进行分析,利用k -均值聚类方法找到不同国家注册学生的绩效指标。学生的表现取决于许多因素,成绩不足以代表学生的全面知识。本文旨在分析学生在不同国家的不同属性下的表现。
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Performance analysis of student learning metric using K-mean clustering approach K-mean cluster
The large volume of data in all fields across the globe has to be managed and is used by the decision makers to obtain something productive out of it. The big data of 14000×5 of Harvard University online course is analysed to find the performance metrics of registered students from different countries by means of K-mean clustering method. The performance of the student depends on number of factors and grades are not enough to represent the all-round knowledge of a student. The paper aims to analyse the performance of the students based on different attributes with respect to their country.
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