Staff View: Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine

Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine

This paper is aimed to present a conceptual understanding that summarizes higher education analytics lifecycle. This paper explores the establishment of new architecture of technologies, experts, standards, and practices in the complex data infrastructure projects among higher education institutes....

Full description

Saved in:

Bibliographic Details
Main Authors:	Maryam Khanian, Najafabdi, Sarasvathi, Nagalingham, Sayed Mojtaba, Tabibian
Format:	Article
Language:	English
Published:	INTI International University 2019
Subjects:	L Education (General) LB2300 Higher Education QA75 Electronic computers. Computer science
Online Access:	http://eprints.intimal.edu.my/1329/1/ij2019_08.pdf http://eprints.intimal.edu.my/1329/ http://intijournal.newinti.edu.my
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-inti-eprints.1329
record_format	eprints
spelling	my-inti-eprints.13292024-03-23T07:45:00Z http://eprints.intimal.edu.my/1329/ Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine Maryam Khanian, Najafabdi Sarasvathi, Nagalingham Sayed Mojtaba, Tabibian L Education (General) LB2300 Higher Education QA75 Electronic computers. Computer science This paper is aimed to present a conceptual understanding that summarizes higher education analytics lifecycle. This paper explores the establishment of new architecture of technologies, experts, standards, and practices in the complex data infrastructure projects among higher education institutes. The research on higher education analytics converges with the demands from industry to improve the learning education systems by considering the teaching and learning analytics capabilities enhancing the efficiency of higher education. The exploitation of massive volume campus and learning information could be a crucial challenge for the planning of campus resources, personalized curricula and learning experiences. In the field of higher education, institutions look to a future of the unknown and vast speed advancement of technology. Moreover, with more strategic data solutions used in decision making with the over increasing social needs and political changes at national and global, competition within and among universities increase. Higher education needs to expand local and global impact, increase financial and operational efficiency, create the new funding models in a changing economic climate and respond to the greater accountability demands to ensure the success of organizational at all levels and stay on top of the ranks. Research on higher education institutes is also important because it enables maximum benefit and perceptions on students’ performance and learning trajectories to be determined as these two are important in adapting and personalizing curriculum and assessment. The findings of this paper provided a view about modeling students’ performance classification by Machine Learning models and to identify which of the predictors in the dataset contribute towards good prediction on the students’ performance. INTI International University 2019 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1329/1/ij2019_08.pdf Maryam Khanian, Najafabdi and Sarasvathi, Nagalingham and Sayed Mojtaba, Tabibian (2019) Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine. INTI JOURNAL, 2019 (8). ISSN e2600-7320 http://intijournal.newinti.edu.my
institution	INTI International University
building	INTI Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	INTI International University
content_source	INTI Institutional Repository
url_provider	http://eprints.intimal.edu.my
language	English
topic	L Education (General) LB2300 Higher Education QA75 Electronic computers. Computer science
spellingShingle	L Education (General) LB2300 Higher Education QA75 Electronic computers. Computer science Maryam Khanian, Najafabdi Sarasvathi, Nagalingham Sayed Mojtaba, Tabibian Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine
description	This paper is aimed to present a conceptual understanding that summarizes higher education analytics lifecycle. This paper explores the establishment of new architecture of technologies, experts, standards, and practices in the complex data infrastructure projects among higher education institutes. The research on higher education analytics converges with the demands from industry to improve the learning education systems by considering the teaching and learning analytics capabilities enhancing the efficiency of higher education. The exploitation of massive volume campus and learning information could be a crucial challenge for the planning of campus resources, personalized curricula and learning experiences. In the field of higher education, institutions look to a future of the unknown and vast speed advancement of technology. Moreover, with more strategic data solutions used in decision making with the over increasing social needs and political changes at national and global, competition within and among universities increase. Higher education needs to expand local and global impact, increase financial and operational efficiency, create the new funding models in a changing economic climate and respond to the greater accountability demands to ensure the success of organizational at all levels and stay on top of the ranks. Research on higher education institutes is also important because it enables maximum benefit and perceptions on students’ performance and learning trajectories to be determined as these two are important in adapting and personalizing curriculum and assessment. The findings of this paper provided a view about modeling students’ performance classification by Machine Learning models and to identify which of the predictors in the dataset contribute towards good prediction on the students’ performance.
format	Article
author	Maryam Khanian, Najafabdi Sarasvathi, Nagalingham Sayed Mojtaba, Tabibian
author_facet	Maryam Khanian, Najafabdi Sarasvathi, Nagalingham Sayed Mojtaba, Tabibian
author_sort	Maryam Khanian, Najafabdi
title	Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine
title_short	Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine
title_full	Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine
title_fullStr	Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine
title_full_unstemmed	Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine
title_sort	predictive modeling for student performance data using decision tree and support vector machine
publisher	INTI International University
publishDate	2019
url	http://eprints.intimal.edu.my/1329/1/ij2019_08.pdf http://eprints.intimal.edu.my/1329/ http://intijournal.newinti.edu.my
_version_	1794645235859456000
score	13.211869

Predictive Modeling for Student Performance Data Using Decision Tree and Support Vector Machine

Similar Items