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

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  • Chih-Chang Yu

    (Department of Information and Computer Engineering, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli Dist., Taoyuan 320314, Taiwan)

  • Yufeng (Leon) Wu

    (Graduate School of Education, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli Dist., Taoyuan 320314, Taiwan)

Abstract

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.

Suggested Citation

  • Chih-Chang Yu & Yufeng (Leon) Wu, 2021. "Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12461-:d:676992
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    References listed on IDEAS

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    1. Bashir Khan Yousafzai & Sher Afzal Khan & Taj Rahman & Inayat Khan & Inam Ullah & Ateeq Ur Rehman & Mohammed Baz & Habib Hamam & Omar Cheikhrouhou, 2021. "Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network," Sustainability, MDPI, vol. 13(17), pages 1-21, August.
    2. Aras Bozkurt & Abdulkadir Karadeniz & David Baneres & Ana Elena Guerrero-Roldán & M. Elena Rodríguez, 2021. "Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    3. Shivam Gupta & Mahsa Motlagh & Jakob Rhyner, 2020. "The Digitalization Sustainability Matrix: A Participatory Research Tool for Investigating Digitainability," Sustainability, MDPI, vol. 12(21), pages 1-27, November.
    4. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2019. "Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment," Sustainability, MDPI, vol. 11(24), pages 1-12, December.
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