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Analysis of students' incidents in h...

Blasi, Anas H.

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Analysis of students' incidents in higher education using data mining techniques.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Analysis of students' incidents in higher education using data mining techniques./
作者:	Blasi, Anas H.
面頁冊數:	168 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.
Contained By:	Dissertation Abstracts International75-06B(E).
標題:	Engineering, System Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3612769
ISBN:	9781303746291

Analysis of students' incidents in higher education using data mining techniques.
Blasi, Anas H.

Analysis of students' incidents in higher education using data mining techniques. - 168 p.

Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2013.

Institutions of higher educational are the most important environments in which students, families, educators and community members have opportunities to learn, teach, and grow. However, one of the most problems that face the IHE's is the incidents of students' behavior. The objective of this study is to decrease the incidents of students' behavior by identifying the factors which cause the incidents in college campuses.

ISBN: 9781303746291Subjects--Topical Terms:

1018128
Engineering, System Science.

Analysis of students' incidents in higher education using data mining techniques.
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245 1 0 $a Analysis of students' incidents in higher education using data mining techniques.
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500 $a Source: Dissertation Abstracts International, Volume: 75-06(E), Section: B.
500 $a Adviser: Harold W. Lewis, III.
502 $a Thesis (Ph.D.)--State University of New York at Binghamton, 2013.
520 $a Institutions of higher educational are the most important environments in which students, families, educators and community members have opportunities to learn, teach, and grow. However, one of the most problems that face the IHE's is the incidents of students' behavior. The objective of this study is to decrease the incidents of students' behavior by identifying the factors which cause the incidents in college campuses.
520 $a CRISP-DM Methodology has been applied to manage the process of data mining, four data mining techniques: J48 Decision Tree (DT), Naive Bayesian (NB), Artificial Neural Network (ANN), and Multinomial Logistic Regression (MLR) have been used to build the classification models and to generate rules to classify and predict the student's behavior and the location of incident in college campuses which will take into consideration seven factors: Student Academic Major, Student Level, Gender, GPA Cumulative, Local Address, Student Ethnicity, and time of incident by month.
520 $a Finally, all techniques were evaluated and compared. However, based on the evaluation and comparison it was found that the results of the accuracy were high for all the classification models; Multinomial Logistic Regression gave the highest accuracy, second was J48 Decision Tree algorithm, third was Artificial Neural Network, and lastly was Naive Bayesian Classifier.
590 $a School code: 0792.
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