Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks

While the use of deep neural networks is popular for predicting students’ learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current lear...

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Autores principales:	Chih-Chang Yu, Yufeng (Leon) Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	early warning system STEM online learning recurrent neural network Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/034873ddc01f4d7d9b3b77a1a09fab7b
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id	oai:doaj.org-article:034873ddc01f4d7d9b3b77a1a09fab7b
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spelling	oai:doaj.org-article:034873ddc01f4d7d9b3b77a1a09fab7b2021-11-25T19:01:17ZEarly Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks10.3390/su1322124612071-1050https://doaj.org/article/034873ddc01f4d7d9b3b77a1a09fab7b2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12461https://doaj.org/toc/2071-1050While the use of deep neural networks is popular for predicting students’ learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current learning management systems (LMSs) operated by colleges cannot provide adequate information. To make the system more feasible, this article proposes a recurrent neural network (RNN)-based framework to identify at-risk students who might fail the course using only a few common learning features. RNN-based methods can be more effective than CNN-based methods in identifying at-risk students due to their ability to memorize time-series features. The data used in this study were collected from an online course that teaches artificial intelligence (AI) at a university in northern Taiwan. Common features, such as the number of logins, number of posts and number of homework assignments submitted, are considered to train the model. This study compares the prediction results of the RNN model with the following conventional machine learning models: logistic regression, support vector machines, decision trees and random forests. This work also compares the performance of the RNN model with two neural network-based models: the multi-layer perceptron (MLP) and a CNN-based model. The experimental results demonstrate that the RNN model used in this study is better than conventional machine learning models and the MLP in terms of F-score, while achieving similar performance to the CNN-based model with fewer parameters. Our study shows that the designed RNN model can identify at-risk students once one-third of the semester has passed. Some future directions are also discussed.Chih-Chang YuYufeng (Leon) WuMDPI AGarticleearly warning systemSTEMonline learningrecurrent neural networkEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12461, p 12461 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	early warning system STEM online learning recurrent neural network Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	early warning system STEM online learning recurrent neural network Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Chih-Chang Yu Yufeng (Leon) Wu Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks
description	While the use of deep neural networks is popular for predicting students’ learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current learning management systems (LMSs) operated by colleges cannot provide adequate information. To make the system more feasible, this article proposes a recurrent neural network (RNN)-based framework to identify at-risk students who might fail the course using only a few common learning features. RNN-based methods can be more effective than CNN-based methods in identifying at-risk students due to their ability to memorize time-series features. The data used in this study were collected from an online course that teaches artificial intelligence (AI) at a university in northern Taiwan. Common features, such as the number of logins, number of posts and number of homework assignments submitted, are considered to train the model. This study compares the prediction results of the RNN model with the following conventional machine learning models: logistic regression, support vector machines, decision trees and random forests. This work also compares the performance of the RNN model with two neural network-based models: the multi-layer perceptron (MLP) and a CNN-based model. The experimental results demonstrate that the RNN model used in this study is better than conventional machine learning models and the MLP in terms of F-score, while achieving similar performance to the CNN-based model with fewer parameters. Our study shows that the designed RNN model can identify at-risk students once one-third of the semester has passed. Some future directions are also discussed.
format	article
author	Chih-Chang Yu Yufeng (Leon) Wu
author_facet	Chih-Chang Yu Yufeng (Leon) Wu
author_sort	Chih-Chang Yu
title	Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks
title_short	Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks
title_full	Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks
title_fullStr	Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks
title_full_unstemmed	Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks
title_sort	early warning system for online stem learning—a slimmer approach using recurrent neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/034873ddc01f4d7d9b3b77a1a09fab7b
work_keys_str_mv	AT chihchangyu earlywarningsystemforonlinestemlearningaslimmerapproachusingrecurrentneuralnetworks AT yufengleonwu earlywarningsystemforonlinestemlearningaslimmerapproachusingrecurrentneuralnetworks
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Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks

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