Student Performance Prediction with Optimum Multilabel Ensemble Model

One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using...

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Autores principales:	Yekun Ephrem Admasu, Haile Abrahaley Teklay
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	edm student performance prediction ensemble model multi-label classification 62p15 62p30 68t10 68t30 Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/24126a8945ba4a68b1aa583562a3f2b8
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id	oai:doaj.org-article:24126a8945ba4a68b1aa583562a3f2b8
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spelling	oai:doaj.org-article:24126a8945ba4a68b1aa583562a3f2b82021-12-05T14:10:51ZStudent Performance Prediction with Optimum Multilabel Ensemble Model0334-18602191-026X10.1515/jisys-2021-0016https://doaj.org/article/24126a8945ba4a68b1aa583562a3f2b82021-04-01T00:00:00Zhttps://doi.org/10.1515/jisys-2021-0016https://doaj.org/toc/0334-1860https://doaj.org/toc/2191-026XOne of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.Yekun Ephrem AdmasuHaile Abrahaley TeklayDe Gruyterarticleedmstudent performance predictionensemble modelmulti-label classification62p1562p3068t1068t30ScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 511-523 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	edm student performance prediction ensemble model multi-label classification 62p15 62p30 68t10 68t30 Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	edm student performance prediction ensemble model multi-label classification 62p15 62p30 68t10 68t30 Science Q Electronic computers. Computer science QA75.5-76.95 Yekun Ephrem Admasu Haile Abrahaley Teklay Student Performance Prediction with Optimum Multilabel Ensemble Model
description	One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.
format	article
author	Yekun Ephrem Admasu Haile Abrahaley Teklay
author_facet	Yekun Ephrem Admasu Haile Abrahaley Teklay
author_sort	Yekun Ephrem Admasu
title	Student Performance Prediction with Optimum Multilabel Ensemble Model
title_short	Student Performance Prediction with Optimum Multilabel Ensemble Model
title_full	Student Performance Prediction with Optimum Multilabel Ensemble Model
title_fullStr	Student Performance Prediction with Optimum Multilabel Ensemble Model
title_full_unstemmed	Student Performance Prediction with Optimum Multilabel Ensemble Model
title_sort	student performance prediction with optimum multilabel ensemble model
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/24126a8945ba4a68b1aa583562a3f2b8
work_keys_str_mv	AT yekunephremadmasu studentperformancepredictionwithoptimummultilabelensemblemodel AT haileabrahaleyteklay studentperformancepredictionwithoptimummultilabelensemblemodel
_version_	1718371662013923328

Student Performance Prediction with Optimum Multilabel Ensemble Model

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