An assay of teachers' attainmentusing decision tree based classification techniques

R. Lawrance, V. Shanmugarajeshwari
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引用次数: 9

Abstract

Data mining is one of the potential research fields regarding interdisciplinary aspects. Educational data mining is one the developing discipline in the present junction. Classification techniques in the data mining plays an important role in the area of educational data mining. The main goal regarding this work is to predict the teachers' attainment by using the relevant features. The proposed methodology consists of the phases like preprocessing, attribute selection, classification based on decision tree and performance evaluation. In the data preprocessing phase, the missing values have been removed. The attributes are remodel into a categorized format using the categorization process. Gain ratio, chi square and information gain feature selection methods are tested on preprocessed data. The suitable attributes selected are predicted using classification techniques. In this paper, one of the classification techniques are described and based on ID3, C4.5 and C5.0 is used to predict the teachers' attainment in educational data mining.
基于决策树分类技术的教师素养分析
数据挖掘是一个具有跨学科潜力的研究领域。教育数据挖掘是当前发展中的一门学科。数据挖掘中的分类技术在教育数据挖掘领域起着重要的作用。本研究的主要目的是利用相关特征来预测教师的素养。该方法包括预处理、属性选择、基于决策树的分类和性能评价四个阶段。在数据预处理阶段,缺失的值已经被去除。使用分类过程将属性重新建模为分类格式。在预处理数据上测试了增益比、卡方和信息增益特征选择方法。使用分类技术预测选择的合适属性。本文描述了其中一种分类技术,并基于ID3、C4.5和C5.0对教育数据挖掘中的教师素养进行了预测。
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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