基于k -均值和模糊c -均值(FCM)的中职高中生学习风格聚类分析

R. Andrea, Shinta Palupi, Siti Qomariah
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引用次数: 3

摘要

学生无法吸收老师所传达的各种知识,不是因为他的理解能力不强,也不是因为老师不会教,而是因为学生和老师之间的学习风格(learning style)不相容,以至于学生对某些老师的学习感到不舒服,这种情况也发生在SMKN 2 Penajam Paser Utara (PPU)中。研究应用K-means和模糊C-means (FCM)数据挖掘方法分析学生学习的聚类(组)类型。要达到的目标是了解这种类型的学习聚类对发展吸收能力和提高学生成绩的有效性。本研究采用数据挖掘的方法,从数据清洗、数据选择、数据转换、数据挖掘、模式演化、知识(knowledge)等几个方面对学习类型进行聚类。
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Cluster Analysis for Learning Style of Vocational High School Student Using K-Means and FUZZY C-MEANS (FCM)
The inability of students to absorb the various knowledge conveyed by the teacher is not due to the inability of his understanding and not because the teacher is not able to teach, but rather due to the incompatibility of learning styles (learning style) between students and teachers, so that students feel uncomfortable learning to certain teachers, it occurred also in SMKN 2 Penajam Paser Utara (PPU), research to analyze cluster (group) type of student learning by applying data mining method that is K-means and Fuzzy C-means (FCM). The goal to be achieved is to know the effectiveness of this type of learning clustering on the development of absorptive capacity and improvement of student achievement. In this research, the method used to cluster the learning type with data mining process starting from data cleaning, data selection, data transformation, data mining, pattern evolution, and knowledge (knowledge).
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