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Investigating Preceding Determinants Affecting Primary School Students Online Learning Experience Utilizing Deep Learning Neural Network

Author

Listed:
  • Ardvin Kester S. Ong

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Jelline C. Cuales

    (School of Graduate Studies, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Jose Pablo F. Custodio

    (Young Innovators Research Center, Mapúa University, 658 Muralla Street, Intramuros, Manila 1002, Philippines)

  • Eisley Yuanne J. Gumasing

    (Young Innovators Research Center, Mapúa University, 658 Muralla Street, Intramuros, Manila 1002, Philippines)

  • Paula Norlene A. Pascual

    (Young Innovators Research Center, Mapúa University, 658 Muralla Street, Intramuros, Manila 1002, Philippines)

  • Ma. Janice J. Gumasing

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

Abstract

The pandemic has caused all of the programs that are offered in primary schools to be interrupted. Evaluating the student’s learning at this level is essential because education development throughout the epidemic is critical, as there was no other educational alternative available during the pandemic. This study examines the use of deep learning neural network (DLNN) to evaluate the parameters influencing primary school students’ online learning experiences during the COVID-19 pandemic. The researchers considered this issue since primary students’ online learning experiences needed more attention. To carefully analyze the relationships between the parameters of primary students’ learning experience, an online questionnaire was utilized, subject to parents’ participation. A total of 385 Filipino elementary school students were selected and surveyed using a purposive sampling method. Participants in this research ranged in age from seven to thirteen and were supervised by their parents or legal guardians. The result of the study showed that open communication, social presence, design and organization, and facilitation had the most impact on predicting students’ experiences with online education, having a high accuracy from DLNN of 96.12%. This demonstrates the significance of open communication, draws attention to the importance of helping students feel welcomed and appreciated, and demonstrates the influence that instructors have on the overall positive learning experiences of their students. Finally, the findings of this study gave a strong framework and clear conclusions that both schools and the government’s education department could use to improve the way primary education is taught online across the country. Finally, the results and findings of this study could be applied and extended to other related education studies worldwide.

Suggested Citation

  • Ardvin Kester S. Ong & Jelline C. Cuales & Jose Pablo F. Custodio & Eisley Yuanne J. Gumasing & Paula Norlene A. Pascual & Ma. Janice J. Gumasing, 2023. "Investigating Preceding Determinants Affecting Primary School Students Online Learning Experience Utilizing Deep Learning Neural Network," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3517-:d:1068396
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    References listed on IDEAS

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    1. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Nattakit Yuduang & Reny Nadlifatin & Satria Fadil Persada & Kirstien Paola E. Robas & Thanatorn Chuenyindee & Thapanat Buaphiban, 2022. "Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand," IJERPH, MDPI, vol. 19(13), pages 1-28, June.
    2. Yogi Tri Prasetyo & Ardvin Kester S. Ong & Giero Krissianne Frances Concepcion & Francheska Mikaela B. Navata & Raphael Andrei V. Robles & Isaiash Jeremy T. Tomagos & Michael Nayat Young & John Franci, 2021. "Determining Factors Affecting Acceptance of E-Learning Platforms during the COVID-19 Pandemic: Integrating Extended Technology Acceptance Model and DeLone & McLean IS Success Model," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
    3. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Michael Nayat Young & John Francis T. Diaz & Thanatorn Chuenyindee & Poonyawat Kusonwattana & Nattakit Yuduang & Reny Nadlifatin & Anak Agung Ngurah Perwira , 2021. "Students’ Preference Analysis on Online Learning Attributes in Industrial Engineering Education during the COVID-19 Pandemic: A Conjoint Analysis Approach for Sustainable Industrial Engineers," Sustainability, MDPI, vol. 13(15), pages 1-20, July.
    4. Santosh, T.V. & Vinod, Gopika & Saraf, R.K. & Ghosh, A.K. & Kushwaha, H.S., 2007. "Application of artificial neural networks to nuclear power plant transient diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1468-1472.
    5. Pilhyoun Yoon & Junghoon Leem, 2021. "The Influence of Social Presence in Online Classes Using Virtual Conferencing: Relationships between Group Cohesion, Group Efficacy, and Academic Performance," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
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