PERFORMANCE STUDY OF DATA MINING TECHNIQUES TO IMPROVE THE ADAPTION LEARNING IN E-LEARNING SYSTEM

被引：0

作者：

Hegazy, Abdelfath A. ^{[1
]}

Elfharkrny, Essameldean F. ^{[1
]}

Nafea, Shaimaa M. ^{[1
]}

机构：

[1] Arab Acad Sci & Technol, Coll Comp & Informat Technol, Alexandria, Egypt

来源：

7TH INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE (INTED2013) | 2013年

关键词：

Data mining; Classification techniques; E-Learning; CART; C4.5; JRIP;

D O I：

暂无

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

This paper answers the question of how data mining could be applied in educational systems as a way to predict students' coming performance from their past behavior in perquisite E-learning modules. It helps earlier in detecting the particular dropouts along with students exactly who need special attention and invite the teacher to deliver the appropriate advising/counseling. Data Mining is really a multidisciplinary area focusing on methodologies for extracting valuable knowledge coming from students log files and there are many useful data mining methods to extract data. This knowledge can be used to increase the caliber of education and can be employed for selection making in educational process. It is applied on student's previous performance data to generate the model that can be used to predict the student's coming performance to improve their performance. In this work a comparative study among three different techniques of classification in order to determine the best classifier to be used in predicting coming performance from their past behavior.

引用

页码：2424 / 2434

页数：11

共 12 条

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