An application of predicting student performance using kernel k-means and smooth support vector machine

This thesis presents the model of predicting student academic performances inHigher Learning Institution (HLI).The prediction ofstudentssuccessfulis one of the most vital issues inHLI.In the previous work, thereare many methodsproposed topredictthe performanceof students such as Scholastic Aptitude...

Full description

Bibliographic Details
Main Author:	Sajadin, Sembiring
Format:	Thesis
Language:	English
Published:	2012
Subjects:	QA Mathematics
Online Access:	http://umpir.ump.edu.my/id/eprint/3672/ http://umpir.ump.edu.my/id/eprint/3672/1/CD6309_SAJADIN_SEMBIRING.pdf

id	ump-3672
recordtype	eprints
spelling	ump-36722015-03-03T08:02:32Z http://umpir.ump.edu.my/id/eprint/3672/ An application of predicting student performance using kernel k-means and smooth support vector machine Sajadin, Sembiring QA Mathematics This thesis presents the model of predicting student academic performances inHigher Learning Institution (HLI).The prediction ofstudentssuccessfulis one of the most vital issues inHLI.In the previous work, thereare many methodsproposed topredictthe performanceof students such as Scholastic Aptitude Test (SAT) or American College Test (ACT), Intelligent Test, Fuzzy Set Theory, Neural Network, Decision Tree and Naïve Bayes.However, thefactremainsfound ina variety of debateamongeducators inhigher learning institution, especially those relatedto predictorvariablesthatused and the resulting level of prediction accuracy.This shown that the rule model in predicting student performanceisstilla gapand it is urgent for educators to obtain a more accurate prediction results.The objective of thisstudyis to create a rule model in predicting of students performance based on their psychometric factors. In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. These techniques successfullyapplied in processing large amounts of data, especially on high dimensional data that are nonlinearly separable. The data collection from student academic databases and surveyed the psychometric factors of undergraduatestudentin semester 3 sessions 2007/2008 at Universiti Malaysia Pahang.Theresultof this study indicatesa positive correlation between the proposed predictor variables and the students performance.These predictor variables contributesignificantly in increasing or decreasing student performance that is equalto52.2%(R2=0.522).The studyalsofound the cluster model of students based on their performance. Eachmember of the clusters labeledwith their performance index to describe the current condition of student performance.The prediction accuracy of predicting modelproposed have thelowest accuracy 61%(R2= 0.61)in predicting Good performance indexand thehighest accuracy 93.67% (R2= 0.9367)in predicting Poor Performance index. This studyshowedthat the kernel methodhasa capabilityas data mining technique on educational data mining. The results of this studyaresuitableto beusedinmonitoringthe progression of students performancesemester by semesterand supportedthe decision making process by decision makerinHLI. 2012-08 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/3672/1/CD6309_SAJADIN_SEMBIRING.pdf Sajadin, Sembiring (2012) An application of predicting student performance using kernel k-means and smooth support vector machine. Masters thesis, Universiti Malaysia Pahang.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Sajadin, Sembiring An application of predicting student performance using kernel k-means and smooth support vector machine
description	This thesis presents the model of predicting student academic performances inHigher Learning Institution (HLI).The prediction ofstudentssuccessfulis one of the most vital issues inHLI.In the previous work, thereare many methodsproposed topredictthe performanceof students such as Scholastic Aptitude Test (SAT) or American College Test (ACT), Intelligent Test, Fuzzy Set Theory, Neural Network, Decision Tree and Naïve Bayes.However, thefactremainsfound ina variety of debateamongeducators inhigher learning institution, especially those relatedto predictorvariablesthatused and the resulting level of prediction accuracy.This shown that the rule model in predicting student performanceisstilla gapand it is urgent for educators to obtain a more accurate prediction results.The objective of thisstudyis to create a rule model in predicting of students performance based on their psychometric factors. In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. These techniques successfullyapplied in processing large amounts of data, especially on high dimensional data that are nonlinearly separable. The data collection from student academic databases and surveyed the psychometric factors of undergraduatestudentin semester 3 sessions 2007/2008 at Universiti Malaysia Pahang.Theresultof this study indicatesa positive correlation between the proposed predictor variables and the students performance.These predictor variables contributesignificantly in increasing or decreasing student performance that is equalto52.2%(R2=0.522).The studyalsofound the cluster model of students based on their performance. Eachmember of the clusters labeledwith their performance index to describe the current condition of student performance.The prediction accuracy of predicting modelproposed have thelowest accuracy 61%(R2= 0.61)in predicting Good performance indexand thehighest accuracy 93.67% (R2= 0.9367)in predicting Poor Performance index. This studyshowedthat the kernel methodhasa capabilityas data mining technique on educational data mining. The results of this studyaresuitableto beusedinmonitoringthe progression of students performancesemester by semesterand supportedthe decision making process by decision makerinHLI.
format	Thesis
author	Sajadin, Sembiring
author_facet	Sajadin, Sembiring
author_sort	Sajadin, Sembiring
title	An application of predicting student performance using kernel k-means and smooth support vector machine
title_short	An application of predicting student performance using kernel k-means and smooth support vector machine
title_full	An application of predicting student performance using kernel k-means and smooth support vector machine
title_fullStr	An application of predicting student performance using kernel k-means and smooth support vector machine
title_full_unstemmed	An application of predicting student performance using kernel k-means and smooth support vector machine
title_sort	application of predicting student performance using kernel k-means and smooth support vector machine
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/3672/ http://umpir.ump.edu.my/id/eprint/3672/1/CD6309_SAJADIN_SEMBIRING.pdf
first_indexed	2023-09-18T21:58:05Z
last_indexed	2023-09-18T21:58:05Z
_version_	1777414213809995776

An application of predicting student performance using kernel k-means and smooth support vector machine

Similar Items