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
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Publicado: MDPI AG 2021
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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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