Prediction of students’ performance in e-learning environment using random forest

The need of advancement in e-learning technology causes educational data to become very huge and increase very rapidly. The data is generated daily as a result of students interaction with e-learning environment, especially learning management systems. The data contain hidden information about the p...

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Main Author:	Yusuf, Abubakar
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/85737/1/AbuBakarYusufMFC2017.pdf
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id	my-utm-ep.85737
record_format	uketd_dc
spelling	my-utm-ep.857372020-07-30T07:30:30Z Prediction of students’ performance in e-learning environment using random forest 2017 Yusuf, Abubakar QA75 Electronic computers. Computer science The need of advancement in e-learning technology causes educational data to become very huge and increase very rapidly. The data is generated daily as a result of students interaction with e-learning environment, especially learning management systems. The data contain hidden information about the participation of students in various activities of e-learning which when revealed can be used to associate with the students performance. Predicting the performance of students based on the use of e-learning system in educational institutions is a major concern and has become very important for education managements to better understand why so many students perform poorly or even fail in their studies. However, it is difficult to do the prediction due to the diverse factors or characteristics that influence their performance. This dissertation is aimed at predicting students performance by considering the students interaction in e-learning environment, their assessment marks and their prerequisite knowledge as prediction features. Random Forest algorithm, which is an ensemble of decision trees, has been used for prediction and the comparative analysis shows that the algorithm outperforms the popular decision tree and K-Nearest Neighbor algorithms. However, Naive Bayes outperformed Random Forest. In addition to the performance prediction, Random Forest was also used to identify the significant attributes that influence students performance, which was validated by a statistical test using Pearson correlation. The research therefore, revealed that lab task, assignments, midterm and prerequisite knowledge are significant indicators of students performance predictions. 2017 Thesis http://eprints.utm.my/id/eprint/85737/ http://eprints.utm.my/id/eprint/85737/1/AbuBakarYusufMFC2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:132621 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 Yusuf, Abubakar Prediction of students’ performance in e-learning environment using random forest
description	The need of advancement in e-learning technology causes educational data to become very huge and increase very rapidly. The data is generated daily as a result of students interaction with e-learning environment, especially learning management systems. The data contain hidden information about the participation of students in various activities of e-learning which when revealed can be used to associate with the students performance. Predicting the performance of students based on the use of e-learning system in educational institutions is a major concern and has become very important for education managements to better understand why so many students perform poorly or even fail in their studies. However, it is difficult to do the prediction due to the diverse factors or characteristics that influence their performance. This dissertation is aimed at predicting students performance by considering the students interaction in e-learning environment, their assessment marks and their prerequisite knowledge as prediction features. Random Forest algorithm, which is an ensemble of decision trees, has been used for prediction and the comparative analysis shows that the algorithm outperforms the popular decision tree and K-Nearest Neighbor algorithms. However, Naive Bayes outperformed Random Forest. In addition to the performance prediction, Random Forest was also used to identify the significant attributes that influence students performance, which was validated by a statistical test using Pearson correlation. The research therefore, revealed that lab task, assignments, midterm and prerequisite knowledge are significant indicators of students performance predictions.
format	Thesis
qualification_level	Master's degree
author	Yusuf, Abubakar
author_facet	Yusuf, Abubakar
author_sort	Yusuf, Abubakar
title	Prediction of students’ performance in e-learning environment using random forest
title_short	Prediction of students’ performance in e-learning environment using random forest
title_full	Prediction of students’ performance in e-learning environment using random forest
title_fullStr	Prediction of students’ performance in e-learning environment using random forest
title_full_unstemmed	Prediction of students’ performance in e-learning environment using random forest
title_sort	prediction of students’ performance in e-learning environment using random forest
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2017
url	http://eprints.utm.my/id/eprint/85737/1/AbuBakarYusufMFC2017.pdf
_version_	1747818445455491072

Prediction of students’ performance in e-learning environment using random forest

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