IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3517-d1068396.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3517/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3517/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    4. 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.
    5. 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.
    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. Ma. Janice J. Gumasing & Ardvin Kester S. Ong & Maria Angelica D. Bare, 2022. "User Preference Analysis of a Sustainable Workstation Design for Online Classes: A Conjoint Analysis Approach," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    2. S. Ong, Ardvin Kester & Prasetyo, Yogi Tri & Chuenyindee, Thanatorn & Young, Michael Nayat & Doma, Bonifacio T. & Caballes, Dennis G. & Centeno, Raffy S. & Morfe, Anthony S. & Bautista, Christine S., 2022. "Preference analysis on the online learning attributes among senior high school students during the COVID-19 pandemic: A conjoint analysis approach," Evaluation and Program Planning, Elsevier, vol. 92(C).
    3. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Kate Nicole M. Tayao & Klint Allen Mariñas & Irene Dyah Ayuwati & Reny Nadlifatin & Satria Fadil Persada, 2022. "Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines," IJERPH, MDPI, vol. 19(22), pages 1-24, November.
    4. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Kerr Lorenzo Picazo & Kim Aaron Salvador & Bobby Ardiansyah Miraja & Yoshiki B. Kurata & Thanatorn Chuenyindee & Reny Nadlifatin & Anak Agung Ngurah Perwira , 2021. "Gym-Goers Preference Analysis of Fitness Centers during the COVID-19 Pandemic: A Conjoint Analysis Approach for Business Sustainability," Sustainability, MDPI, vol. 13(18), pages 1-17, September.
    5. Ma. Janice J. Gumasing & Francee Mae F. Castro, 2023. "Determining Ergonomic Appraisal Factors Affecting the Learning Motivation and Academic Performance of Students during Online Classes," Sustainability, MDPI, vol. 15(3), pages 1-29, January.
    6. Nattakit Yuduang & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Thanatorn Chuenyindee & Poonyawat Kusonwattana & Waranya Limpasart & Thaninrat Sittiwatethanasiri & Ma. Janice J. Gumasing & Josephine D. , 2022. "Factors Influencing the Perceived Effectiveness of COVID-19 Risk Assessment Mobile Application “MorChana” in Thailand: UTAUT2 Approach," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    7. Yoshiki B. Kurata & Ardvin Kester S. Ong & Christienne Joie C. Andrada & Mariela Nicole S. Manalo & Errol John Aldrie U. Sunga & Alvin Racks Martin A. Uy, 2022. "Factors Affecting Perceived Effectiveness of Multigenerational Management Leadership and Metacognition among Service Industry Companies," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
    8. Haozhe Jiang & A. Y. M. Atiquil Islam & Xiaoqing Gu & Jonathan Michael Spector & Suting Chen, 2022. "Technology-Enabled E-Learning Platforms in Chinese Higher Education During the Pandemic Age of COVID-19," SAGE Open, , vol. 12(2), pages 21582440221, May.
    9. Poonyawat Kusonwattana & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Klint Allen Mariñas & Nattakit Yuduang & Thanatorn Chuenyindee & Kriengkrai Thana & Satria Fadil Persada & Reny Nadlifatin & Kirstie, 2022. "Predicting Factors Affecting the Intention to Prepare for Mitigation of Man-Made Fire Disasters in Chonburi Province, Thailand: An Integration of Structural Equation Modeling and Artificial Neural Net," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    10. Paula Zeah N. Bautista & Maela Madel L. Cahigas, 2024. "Exploring Employee Retention among Generation Z Engineers in the Philippines Using Machine Learning Techniques," Sustainability, MDPI, vol. 16(12), pages 1-21, June.
    11. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    12. Maela Madel L. Cahigas & Ardvin Kester S. Ong & Yogi Tri Prasetyo, 2023. "Super Typhoon Rai’s Impacts on Siargao Tourism: Deciphering Tourists’ Revisit Intentions through Machine-Learning Algorithms," Sustainability, MDPI, vol. 15(11), pages 1-29, May.
    13. Nattakit Yuduang & Ardvin Kester S. Ong & Nicole B. Vista & Yogi Tri Prasetyo & Reny Nadlifatin & Satria Fadil Persada & Ma. Janice J. Gumasing & Josephine D. German & Kirstien Paola E. Robas & Thanat, 2022. "Utilizing Structural Equation Modeling–Artificial Neural Network Hybrid Approach in Determining Factors Affecting Perceived Usability of Mobile Mental Health Application in the Philippines," IJERPH, MDPI, vol. 19(11), pages 1-19, May.
    14. Dana Rad & Lavinia Denisia Cuc & Ramona Lile & Valentina E. Balas & Cornel Barna & Mioara Florina Pantea & Graziella Corina Bâtcă-Dumitru & Silviu Gabriel Szentesi & Gavril Rad, 2022. "A Cognitive Systems Engineering Approach Using Unsupervised Fuzzy C-Means Technique, Exploratory Factor Analysis and Network Analysis—A Preliminary Statistical Investigation of the Bean Counter Profil," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
    15. Martinez-Martinez, Sinuhe & Messai, Nadhir & Jeannot, Jean-Philippe & Nuzillard, Danielle, 2015. "Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 50-57.
    16. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    17. Koel Roychowdhury & Radhika Bhanja & Sushmita Biswas, 2022. "Mapping the research landscape of Covid-19 from social sciences perspective: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4547-4568, August.
    18. Tianhao Zhang & Qianqian Jia & Chao Guo & Xiaojin Huang, 2023. "Abnormal Event Detection in Nuclear Power Plants via Attention Networks," Energies, MDPI, vol. 16(18), pages 1-16, September.
    19. Santosh, T.V. & Srivastava, A. & Sanyasi Rao, V.V.S. & Ghosh, A.K. & Kushwaha, H.S., 2009. "Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 759-762.
    20. Jenalyn Shigella G. Yandug & Erika Mae D. Costales & Ardvin Kester S. Ong, 2023. "A Conjoint Analysis Approach, Implications, and Mitigation Plans in Analyzing Students’ Preferences for Online Learning Delivery Types during the COVID-19 Pandemic for Engineering Students: A Case Stu," Sustainability, MDPI, vol. 15(6), pages 1-15, March.

    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:15:y:2023:i:4:p:3517-:d:1068396. 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.