Impact of The Covid-19 Pandemic on Student Learning Styles: Naïve Bayes and Decision Tree Classification in Education

Zaqi Kurniawan; Rizka Tiaharyadini

doi:10.32736/sisfokom.v13i1.1950

Authors

Zaqi Kurniawan Universitas Budi Luhur Jakarta http://orcid.org/0009-0008-1050-4356
Rizka Tiaharyadini Universitas Budi Luhur Jakarta

DOI:

https://doi.org/10.32736/sisfokom.v13i1.1950

Keywords:

Covid-19 Pandemic, Student Learning Styles, Classification Methods, Naïve Bayes, Decision Tree

Abstract

The Covid-19 pandemic significantly changed education with social distancing and changes in the learning environment. In this study, one strong reason for the significance of the research is the urgency of changes in students' learning styles during the Covid-19 pandemic. Investigating differences in learning styles before and during the pandemic not only provides deep insight into students' adaptation to these changes, but also provides a foundation for the development of more inclusive and adaptive learning strategies in the future. This study aims to analyze the effect of the Covid-19 pandemic on students' learning styles in an educational context, focusing on the comparison of two classification methods, Naïve Bayes and Decision Tree. The study was conducted by collecting data on students' learning styles before and during the Covid-19 pandemic, using various relevant indicators. The data was obtained based on school survey results and online platforms, involving student characteristics and learning preferences. The data was then analyzed using Naïve Bayes and Decision Tree classification methods to identify significant changes in students' learning styles. The results showed the prediction accuracy of learning style changes with Naïve Bayes 68.75% and Decision Tree 87.50%. Recommendations for educators and education policy makers are to develop inclusive and adaptive learning strategies to meet diverse learning preferences.

References

N. Carolina, S. N. Azizah, L. M. N. Sholihah, U. Rosyidah, and P. Purwanti, “Analisis Pengaruh Transisi Pembelajaran Jarak Jauh ke Pembelajaran Tatap Muka terhadap Kesehatan Mental Pelajar Pasca Pandemi Covid-19,” Semin. Nas. Psikol. UAD, vol. 1, no. 0, 2022.

E. ALDIYAH, “Perubahan Gaya Belajar Di Masa Pandemi Covid-19,” CENDEKIA J. Ilmu Pengetah., vol. 1, no. 1, pp. 8–16, 2021, doi: 10.51878/cendekia.v1i1.24.

N. T. Arega, “Mental Health and Psychosocial Support Interventions for Children Affected by Armed Conflict in low-and middle-income Countries: A Systematic Review,” Child Youth Care Forum, 2023, doi: 10.1007/s10566-023-09741-0.

B. L. Brown, “Teaching Style vs Learning Style. Myths and Realities,” ERIC Clear. Adult Career, Vocat. Educ., vol. 26, pp. 3–4, 2003.

B. DePorter and H. Mike, Quantum Teaching Mempraktekkan Quantum Learning di Ruang-Ruang Kelas (Terjemahan). 2004.

B. Saha, S. M. Atiqul Haq, and K. J. Ahmed, “How does the COVID-19 pandemic influence students’ academic activities? An explorative study in a public university in Bangladesh,” Humanit. Soc. Sci. Commun., vol. 10, no. 1, 2023, doi: 10.1057/s41599-023-02094-y.

F. Jaya and A. S. Nurqamarani, “Understanding Student Engagement in Hybrid Learning : An Analysis Impact Digital Literacy and Academic Self-Efficacy,” J. Kependidikan J. Has. Penelit. dan Kaji. Kepustakaan di Bid. Pendidikan, Pengajaran dan Pembelajaran, vol. 9, no. 3, p. 833, 2023, doi: 10.33394/jk.v9i3.7947.

S. Yunus, M. Mariyudi, and M. Bin Abubakar, “Strategies for optimizing learning activities during the pandemic and new normal,” Cogent Soc. Sci., vol. 9, no. 1, 2023, doi: 10.1080/23311886.2023.2175491.

F. T. Dayagbil, D. R. Palompon, L. L. Garcia, and M. M. J. Olvido, “Teaching and Learning Continuity Amid and Beyond the Pandemic,” Front. Educ., vol. 6, 2021, doi: 10.3389/feduc.2021.678692.

J. Singh, K. Steele, and L. Singh, “Combining the Best of Online and Face-to-Face Learning: Hybrid and Blended Learning Approach for COVID-19, Post Vaccine, & Post-Pandemic World,” J. Educ. Technol. Syst., vol. 50, no. 2, pp. 140–171, 2021, doi: 10.1177/00472395211047865.

M. K. Hossen and M. S. Uddin, “Attention monitoring of students during online classes using XGBoost classifier,” Comput. Educ. Artif. Intell., vol. 5, 2023, doi: 10.1016/j.caeai.2023.100191.

L. X. Li and S. S. A. Rahman, “Students’ learning style detection using tree augmented naive Bayes,” R. Soc. Open Sci., vol. 5, no. 7, 2018, doi: 10.1098/rsos.172108.

S. Pokhrel and R. Chhetri, “A Literature Review on Impact of COVID-19 Pandemic on Teaching and Learning,” High. Educ. Futur., vol. 8, no. 1, pp. 133–141, 2021, doi: 10.1177/2347631120983481.

N. S. Mohd Makhtar, J. Idris, M. Musa, Y. Andou, K. H. Ku Hamid, and S. W. Puasa, “High lead ion removal in a single synthetic solution utilising plant-based Tacca leontopetaloides biopolymer flocculant (TBPF),” Process Saf. Environ. Prot., vol. 165, pp. 977–986, 2022, doi: 10.1016/j.psep.2022.05.047.

R. Blanquero, E. Carrizosa, P. Ramírez-Cobo, and M. R. Sillero-Denamiel, “Variable selection for Naïve Bayes classification,” Comput. Oper. Res., vol. 135, 2021, doi: 10.1016/j.cor.2021.105456.

M. S. A. Rahman, N. A. A. Jamaludin, Z. Zainol, and T. M. T. Sembok, “The Application of Decision Tree Classification Algorithm on Decision-Making for Upstream Business,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 8, pp. 660–667, 2023, doi: 10.14569/IJACSA.2023.0140873.

M. Karim and R. M. Rahman, “Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing,” J. Softw. Eng. Appl., vol. 06, no. 04, pp. 196–206, 2013, doi: 10.4236/jsea.2013.64025.

S. N. Safitri, Haryono Setiadi, and E. Suryani, “Educational Data Mining Using Cluster Analysis Methods and Decision Trees based on Log Mining,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 3, pp. 448–456, 2022, doi: 10.29207/resti.v6i3.3935.

L. E. Contreras Bravo, N. Nieves-Pimiento, and K. Gonzalez-Guerrero, “Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods,” Ingeniería, vol. 28, no. 1, p. e19514, 2022, doi: 10.14483/23448393.19514.

G. F. M. de Souza, A. Caminada Netto, A. H. de Andrade Melani, M. A. de Carvalho Michalski, and R. F. da Silva, “Engineering systems’ fault diagnosis methods,” Reliab. Anal. Asset Manag. Eng. Syst., pp. 165–187, 2022, doi: 10.1016/b978-0-12-823521-8.00006-2.

F. J. Yang, “An implementation of naive bayes classifier,” Proc. - 2018 Int. Conf. Comput. Sci. Comput. Intell. CSCI 2018, pp. 301–306, 2018, doi: 10.1109/CSCI46756.2018.00065.

A. Oussous, F. Z. Benjelloun, A. Ait Lahcen, and S. Belfkih, “Big Data technologies: A survey,” J. King Saud Univ. - Comput. Inf. Sci., vol. 30, no. 4, pp. 431–448, 2018, doi: 10.1016/j.jksuci.2017.06.001.

B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 2, no. 01, pp. 20–28, 2021, doi: 10.38094/jastt20165.

S. Tangirala, “Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 2, pp. 612–619, 2020, doi: 10.14569/ijacsa.2020.0110277.

A. Cherfi, K. Nouira, and A. Ferchichi, “Very Fast C4.5 Decision Tree Algorithm,” Appl. Artif. Intell., vol. 32, no. 2, pp. 119–137, 2018, doi: 10.1080/08839514.2018.1447479.

G. S. Reddy and S. Chittineni, “Entropy based C4.5-SHO algorithm with information gain optimization in data mining,” PeerJ Comput. Sci., vol. 7, pp. 1–22, 2021, doi: 10.7717/PEERJ-CS.424.

F. R.-C. Jose Llanos, Víctor A. Bucheli, “Early prediction of student performance in CS1 programming courses,” PeerJ Comput Sci, vol. 9, 2023, doi: 10.7717/peerj-cs.1655.

D. A. Kurniawan and Y. I. Kurniawan, “Aplikasi Prediksi Kelayakan Calon Anggota Kredit Menggunakan Algoritma Naïve Bayes,” J. Teknol. dan Manaj. Inform., vol. 4, no. 1, 2018, doi: 10.26905/jtmi.v4i1.1831.

T. Vafeiadis, K. I. Diamantaras, G. Sarigiannidis, and K. C. Chatzisavvas, “Simulation Modelling Practice and Theory A comparison of machine learning techniques for customer churn prediction,” Simul. Model. Pract. Theory, vol. 55, pp. 1–9, 2015, [Online]. Available: http://dx.doi.org/10.1016/j.simpat.2015.03.003

N. Govind, “Donepezil for dementia due to Alzheimer’s disease,” Br. J. Community Nurs., vol. 25, no. 3, pp. 148–149, 2020, doi: 10.12968/bjcn.2020.25.3.148.

Y. Alpaydin and C. Demirli, “Educational Theory in the 21st Century: Science, Technology, Society and Education,” Palgrave Macmillan, pp. 1–269, 2022, [Online]. Available: https://link.springer.com/10.1007/978-981-16-9640-4

X. Xu, “Revolutionizing Education: Advanced Machine Learning Techniques for Precision Recommendation of Top-Quality Instructional Materials,” Int. J. Comput. Intell. Syst., vol. 16, no. 1, 2023, doi: 10.1007/s44196-023-00361-z.

J. Hu, Y. Peng, X. Chen, and H. Yu, “Differentiating the learning styles of college students in different disciplines in a college English blended learning setting,” PLoS One, vol. 16, no. 5 May, 2021, doi: 10.1371/journal.pone.0251545.

S. Kraus, P. Jones, N. Kailer, A. Weinmann, N. Chaparro-Banegas, and N. Roig-Tierno, “Digital Transformation: An Overview of the Current State of the Art of Research,” SAGE Open, vol. 11, no. 3, 2021, doi: 10.1177/21582440211047576.

C. Wang, Z. Wang, G. Wang, J. Y. N. Lau, K. Zhang, and W. Li, “COVID-19 in early 2021: current status and looking forward,” Signal Transduct. Target. Ther., vol. 6, no. 1, 2021, doi: 10.1038/s41392-021-00527-1.

B. Rittle-Johnson, J. R. Star, and K. Durkin, “How Can Cognitive-Science Research Help Improve Education? The Case of Comparing Multiple Strategies to Improve Mathematics Learning and Teaching,” Curr. Dir. Psychol. Sci., vol. 29, no. 6, pp. 599–609, 2020, doi: 10.1177/0963721420969365.

M. Moss et al., “Development of an Accurate Bedside Swallowing Evaluation Decision Tree Algorithm for Detecting Aspiration in Acute Respiratory Failure Survivors,” Chest, vol. 158, no. 5, pp. 1923–1933, 2020, doi: 10.1016/j.chest.2020.07.051.