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首页> 外文期刊>Indian Journal of Science and Technology >An ID3 Algorithm for Performance of Decision Tree in Predicting Student’s Absenteeism in an Academic Year using Categorical Datasets
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An ID3 Algorithm for Performance of Decision Tree in Predicting Student’s Absenteeism in an Academic Year using Categorical Datasets

机译:使用分类数据集预测学年缺勤情况的决策树性能ID3算法

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Objective: The main objective of higher educational institutes is to provide quality education to its students and to improve the quality of managerial decisions. Objective of this paper is to use the data mining techniques like decision tree induction, rule mining to predict the student’s behavior. Methods: We have used ID3 algorithm to predict the reason why students take leave from classes. The data collected from 123 students studying in an arts and science college located in semi-rural area in Villupuram district. Data collection is base on the questionnaire in five point scale method. Decision tree induction algorithm (ID3) was used to predict the reason and the decision tree was constructed using Entropy and Information Gain. Findings: The result obtained from Tanagra tool shows that job attribute plays a key role in predicting the absenteeism of students. That is lack of attendance is due to the job attribute. The results can be used to take managerial decisions. Application: Discovering the knowledge from the educational data helps to achieve the highest level of quality in higher education. The results shows that the decision tree induction algorithms can be used to predict the student’s behavior as well as to improve the managerial decision making process.
机译:目的:高校的主要目标是向学生提供优质的教育,并提高管理决策的质量。本文的目的是使用决策树归纳,规则挖掘等数据挖掘技术来预测学生的行为。方法:我们使用ID3算法预测学生休课的原因。数据来自在Villupuram区半农村地区的一所文理学院学习的123名学生。数据收集是基于问卷的五点量表方法。使用决策树归纳算法(ID3)预测原因,并使用熵和信息增益构造决策树。结果:从Tanagra工具获得的结果表明,工作属性在预测学生的缺勤中起着关键作用。缺勤是由于工作属性。结果可用于做出管理决策。应用程序:从教育数据中发现知识有助于实现高等教育的最高质量。结果表明,决策树归纳算法可用于预测学生的行为并改善管理决策过程。

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