Semantic model for mining e-learning usage with ontology and meaningful learning characteristics

The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extrac...

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Main Author:	Octaviani, Dewi
Format:	Thesis
Language:	English
Published:	2018
Subjects:	LB2300 Higher Education
Online Access:	http://eprints.utm.my/id/eprint/98239/1/DewiOctavianiPSC2018.pdf
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id	my-utm-ep.98239
record_format	uketd_dc
spelling	my-utm-ep.982392022-11-23T08:08:21Z Semantic model for mining e-learning usage with ontology and meaningful learning characteristics 2018 Octaviani, Dewi LB2300 Higher Education QA75 Electronic computers. Computer science The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extract exact information. Current models for mining e-learning usage have focused on the activities usage but ignored the actions usage. In addition, the models lack the ability to incorporate learning pedagogy, leading to a semantic gap to annotate mining data towards education domain. The other issue raised is the absence of usage recommendation that refers to result of data mining task. This research proposes a semantic model for mining e-learning usage with ontology and meaningful learning characteristics. The model starts by preparing data including activity and action hits. The next step is to calculate meaningful hits which categorized into five namely active, cooperative, constructive, authentic, and intentional. The process continues to apply K-means clustering analysis to group usage data into three clusters. Lastly, the usage data is mapped into ontology and the ontology manager generates the meaningful usage cluster and usage recommendation. The model was experimented with three datasets of distinct courses and evaluated by mapping against the student learning outcomes of the courses. The results showed that there is a positive relationship between meaningful hits and learning outcomes, and there is a positive relationship between meaningful usage cluster and learning outcomes. It can be concluded that the proposed semantic model is valid with 95% of confidence level. This model is capable to mine and gain insight into e-learning usage data and to provide usage recommendation. 2018 Thesis http://eprints.utm.my/id/eprint/98239/ http://eprints.utm.my/id/eprint/98239/1/DewiOctavianiPSC2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:141931 phd doctoral Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	LB2300 Higher Education LB2300 Higher Education
spellingShingle	LB2300 Higher Education LB2300 Higher Education Octaviani, Dewi Semantic model for mining e-learning usage with ontology and meaningful learning characteristics
description	The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extract exact information. Current models for mining e-learning usage have focused on the activities usage but ignored the actions usage. In addition, the models lack the ability to incorporate learning pedagogy, leading to a semantic gap to annotate mining data towards education domain. The other issue raised is the absence of usage recommendation that refers to result of data mining task. This research proposes a semantic model for mining e-learning usage with ontology and meaningful learning characteristics. The model starts by preparing data including activity and action hits. The next step is to calculate meaningful hits which categorized into five namely active, cooperative, constructive, authentic, and intentional. The process continues to apply K-means clustering analysis to group usage data into three clusters. Lastly, the usage data is mapped into ontology and the ontology manager generates the meaningful usage cluster and usage recommendation. The model was experimented with three datasets of distinct courses and evaluated by mapping against the student learning outcomes of the courses. The results showed that there is a positive relationship between meaningful hits and learning outcomes, and there is a positive relationship between meaningful usage cluster and learning outcomes. It can be concluded that the proposed semantic model is valid with 95% of confidence level. This model is capable to mine and gain insight into e-learning usage data and to provide usage recommendation.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Octaviani, Dewi
author_facet	Octaviani, Dewi
author_sort	Octaviani, Dewi
title	Semantic model for mining e-learning usage with ontology and meaningful learning characteristics
title_short	Semantic model for mining e-learning usage with ontology and meaningful learning characteristics
title_full	Semantic model for mining e-learning usage with ontology and meaningful learning characteristics
title_fullStr	Semantic model for mining e-learning usage with ontology and meaningful learning characteristics
title_full_unstemmed	Semantic model for mining e-learning usage with ontology and meaningful learning characteristics
title_sort	semantic model for mining e-learning usage with ontology and meaningful learning characteristics
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing
granting_department	Faculty of Engineering - School of Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/98239/1/DewiOctavianiPSC2018.pdf
_version_	1776100563580092416

Semantic model for mining e-learning usage with ontology and meaningful learning characteristics

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