IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i22p12461-d676992.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/22/12461/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/22/12461/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wala Bagunaid & Naveen Chilamkurti & Ahmad Salehi Shahraki & Saeed Bamashmos, 2024. "Visual Data and Pattern Analysis for Smart Education: A Robust DRL-Based Early Warning System for Student Performance Prediction," Future Internet, MDPI, vol. 16(6), pages 1-26, June.
    2. Gizéh Rangel-de Lázaro & Josep M. Duart, 2023. "You Can Handle, You Can Teach It: Systematic Review on the Use of Extended Reality and Artificial Intelligence Technologies for Online Higher Education," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    3. Ming Li & Xiangru Wang & Yi Wang & Yuting Chen & Yixuan Chen, 2022. "Study-GNN: A Novel Pipeline for Student Performance Prediction Based on Multi-Topology Graph Neural Networks," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    4. Shivam Gupta & Jazmin Campos Zeballos & Gema del Río Castro & Ana Tomičić & Sergio Andrés Morales & Maya Mahfouz & Isimemen Osemwegie & Vicky Phemia Comlan Sessi & Marina Schmitz & Nady Mahmoud & Mnen, 2023. "Operationalizing Digitainability: Encouraging Mindfulness to Harness the Power of Digitalization for Sustainable Development," Sustainability, MDPI, vol. 15(8), pages 1-37, April.
    5. Dawool Jung & Sungeun Suh, 2024. "Enhancing Soft Skills through Generative AI in Sustainable Fashion Textile Design Education," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    6. Daina Gudoniene & Evelina Staneviciene & Vytautas Buksnaitis & Nicola Daley, 2023. "The Scenarios of Artificial Intelligence and Wireframes Implementation in Engineering Education," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    7. Lavinia Dovleac & Ioana Bianca Chițu & Eliza Nichifor & Gabriel Brătucu, 2023. "Shaping the Inclusivity in the New Society by Enhancing the Digitainability of Sustainable Development Goals with Education," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    8. Jennie C. De Gagne, 2023. "The State of Artificial Intelligence in Nursing Education: Past, Present, and Future Directions," IJERPH, MDPI, vol. 20(6), pages 1-4, March.
    9. Houda Chkarat & Tarek Abid & Loïc Sauvée, 2023. "Conditions for a Convergence between Digital Platforms and Sustainability in Agriculture," Sustainability, MDPI, vol. 15(19), pages 1-14, September.
    10. Aleksandra Klašnja-Milićević & Mirjana Ivanović, 2021. "E-learning Personalization Systems and Sustainable Education," Sustainability, MDPI, vol. 13(12), pages 1-6, June.
    11. Ahsan Bin Tufail & Inam Ullah & Ateeq Ur Rehman & Rehan Ali Khan & Muhammad Abbas Khan & Yong-Kui Ma & Nadar Hussain Khokhar & Muhammad Tariq Sadiq & Rahim Khan & Muhammad Shafiq & Elsayed Tag Eldin &, 2022. "On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    12. Idiano D’Adamo & Assunta Di Vaio & Alessandro Formiconi & Antonio Soldano, 2022. "European IoT Use in Homes: Opportunity or Threat to Households?," IJERPH, MDPI, vol. 19(21), pages 1-18, November.
    13. Shivam Gupta & Jakob Rhyner, 2022. "Mindful Application of Digitalization for Sustainable Development: The Digitainability Assessment Framework," Sustainability, MDPI, vol. 14(5), pages 1-23, March.
    14. Ajda Fošner, 2024. "University Students’ Attitudes and Perceptions towards AI Tools: Implications for Sustainable Educational Practices," Sustainability, MDPI, vol. 16(19), pages 1-15, October.
    15. Eduard Eisner & Cadence Hsien & Mark Mennenga & Zi-Yu Khoo & Jasmin Dönmez & Christoph Herrmann & Jonathan Sze Choong Low, 2022. "Self-Assessment Framework for Corporate Environmental Sustainability in the Era of Digitalization," Sustainability, MDPI, vol. 14(4), pages 1-33, February.
    16. Maria José Sá & Ana Isabel Santos & Sandro Serpa & Carlos Miguel Ferreira, 2021. "Digitainability—Digital Competences Post-COVID-19 for a Sustainable Society," Sustainability, MDPI, vol. 13(17), pages 1-22, August.
    17. María Consuelo Sáiz Manzanares & Juan José Rodríguez Diez & Raúl Marticorena Sánchez & María José Zaparaín Yáñez & Rebeca Cerezo Menéndez, 2020. "Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    18. Sunghwan Hwang, 2022. "Examining the Effects of Artificial Intelligence on Elementary Students’ Mathematics Achievement: A Meta-Analysis," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    19. Florin-Valeriu Pantelimon & Razvan Bologa & Andrei Toma & Bogdan-Stefan Posedaru, 2021. "The Evolution of AI-Driven Educational Systems during the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(23), pages 1-10, December.
    20. Marlen Gabriele Arnold & Alina Vogel & Martin Ulber, 2021. "Digitalizing Higher Education in Light of Sustainability and Rebound Effects—Surveys in Times of the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(22), pages 1-29, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12461-:d:676992. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.