Self-organizin map clustering method for the analysis of e-learning activities

Students‘ interactions with e-learning vary according to their behaviours which in turn, yield different effects to their academic performance. Some students participate in all online activities while some students participate partially based on their learning behaviours. It is therefore important f...

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Main Author:	Bara, Musa Wakil
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/78886/1/MusaWakilBaraMFC2017.pdf
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spelling	my-utm-ep.788862018-09-17T07:15:57Z Self-organizin map clustering method for the analysis of e-learning activities 2017-01 Bara, Musa Wakil QA75 Electronic computers. Computer science Students‘ interactions with e-learning vary according to their behaviours which in turn, yield different effects to their academic performance. Some students participate in all online activities while some students participate partially based on their learning behaviours. It is therefore important for the lecturers to know the behaviours of their students. But this cannot be done manually due to the unstructured raw data in students‘ log file. Understanding individual student‘s learning behaviour is tedious. To solve the problem, data mining approach is required to extract valuable information from the huge raw data. This research investigated the performance of Self-organizing Map (SOM) to analyze students‘ elearning activities with the aim to identify clusters of students who use the e-learning environment in similar ways from the log files of their actions as input. A study on Meaningful Learning Characteristics and its significance on students‘ leaning behaviors were carried out using multiple regression analysis. Then SOM clustering technique was used to group the students into three clusters where each cluster contains students who interact with the E-learning in similar ways. Behaviors of students in each cluster were analyzed and their effects on their learning success were discovered. The analysis shows that students in Cluster1 have the highest number of interactions with the e-learning (Very Active), and having the highest final score mean of 91.12%. Students in Cluster2 have less number of interactions than that of Cluster1 and have final score mean of 75.65%. Finally, students Cluster3 have least number of interactions than the remaining clusters with final score means is 36.57%. The research shows that, students who participate more in Forum activities emerged the overall in learning success, while students with lowest records on interactions have lowest performance. The research can be used for early identification of low learners to improve their mode of interactions with e-learning.MUSA WAKIL BARA 2017-01 Thesis http://eprints.utm.my/id/eprint/78886/ http://eprints.utm.my/id/eprint/78886/1/MusaWakilBaraMFC2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:109634 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Bara, Musa Wakil Self-organizin map clustering method for the analysis of e-learning activities
description	Students‘ interactions with e-learning vary according to their behaviours which in turn, yield different effects to their academic performance. Some students participate in all online activities while some students participate partially based on their learning behaviours. It is therefore important for the lecturers to know the behaviours of their students. But this cannot be done manually due to the unstructured raw data in students‘ log file. Understanding individual student‘s learning behaviour is tedious. To solve the problem, data mining approach is required to extract valuable information from the huge raw data. This research investigated the performance of Self-organizing Map (SOM) to analyze students‘ elearning activities with the aim to identify clusters of students who use the e-learning environment in similar ways from the log files of their actions as input. A study on Meaningful Learning Characteristics and its significance on students‘ leaning behaviors were carried out using multiple regression analysis. Then SOM clustering technique was used to group the students into three clusters where each cluster contains students who interact with the E-learning in similar ways. Behaviors of students in each cluster were analyzed and their effects on their learning success were discovered. The analysis shows that students in Cluster1 have the highest number of interactions with the e-learning (Very Active), and having the highest final score mean of 91.12%. Students in Cluster2 have less number of interactions than that of Cluster1 and have final score mean of 75.65%. Finally, students Cluster3 have least number of interactions than the remaining clusters with final score means is 36.57%. The research shows that, students who participate more in Forum activities emerged the overall in learning success, while students with lowest records on interactions have lowest performance. The research can be used for early identification of low learners to improve their mode of interactions with e-learning.MUSA WAKIL BARA
format	Thesis
qualification_level	Master's degree
author	Bara, Musa Wakil
author_facet	Bara, Musa Wakil
author_sort	Bara, Musa Wakil
title	Self-organizin map clustering method for the analysis of e-learning activities
title_short	Self-organizin map clustering method for the analysis of e-learning activities
title_full	Self-organizin map clustering method for the analysis of e-learning activities
title_fullStr	Self-organizin map clustering method for the analysis of e-learning activities
title_full_unstemmed	Self-organizin map clustering method for the analysis of e-learning activities
title_sort	self-organizin map clustering method for the analysis of e-learning activities
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2017
url	http://eprints.utm.my/id/eprint/78886/1/MusaWakilBaraMFC2017.pdf
_version_	1747818095556165632

Self-organizin map clustering method for the analysis of e-learning activities

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