The Impact of the Covid-19 Pandemic on Student Learning Activities at Madrasah Ibtidaiyah

Authors

  • Suhaimi Suhaimi Sekolah Tinggi Agama Islam Al-Azhar Pekanbaru
  • Saidah Saidah Universitas Islam Negeri Sultan Thaha Saifuddin Jambi
  • Rashid Rahman Universiti Sains
  • Haziq Idris Paktia University

DOI:

https://doi.org/10.55849/attasyrih.v10i2.271

Keywords:

Covid-19, Pandemic, Virus Emerged

Abstract

In 2019, the Covid-19 virus emerged, causing all work to be done online, including the learning process. The Covid-19 pandemic greatly disrupted normal learning activities, making it a new thing in the world of education. The purpose of this study was to determine the impact of Covid-19 on student learning activities at Madrasah Ibtidahiyah. This study uses a quantitative method using a survey model and in-depth interviews. The survey used in this study was online-based. The results of this study indicate that student understanding decreases during learning activities during Covid-19, so teachers must know what kind of learning model will be carried out during that period so that student achievement and interest in learning do not decrease. The conclusion of this study explains that due to the impact of Covid-19 on learning activities, student motivation has decreased so that many student achievements are not in accordance with school needs. Therefore, the limitation of this study is that this researcher only conducted research on the impact of the Covid-19 pandemic on student learning activities at Madrasah Ibtidaiyah, the researcher hopes that further researchers can conduct research on solutions on how to overcome the impact of the Covid-19 pandemic on student learning activities.

 

References

Ananda, R., Fadhilaturrahmi, F., & Hanafi, I. (2021). Dampak Pandemi Covid-19 terhadap Pembelajaran Tematik di Sekolah Dasar. Jurnal Basicedu, 5(3), 1689–1694. https://doi.org/10.31004/basicedu.v5i3.1190

Andrew, M., Taylorson, J., J Langille, D., Grange, A., & Williams, N. (2018). Student Attitudes towards Technology and Their Preferences for Learning Tools/Devices at Two Universities in the UAE. Journal of Information Technology Education: Research, 17, 309–344. https://doi.org/10.28945/4111

Aristovnik, A., Kerži?, D., Ravšelj, D., Tomaževi?, N., & Umek, L. (2020). Impacts of the COVID-19 Pandemic on Life of Higher Education Students: A Global Perspective. Sustainability, 12(20), 8438. https://doi.org/10.3390/su12208438

Aubriet, V., Courouble, K., Gros-Jean, M., & Borowik, ?. (2021). Correlative analysis of embedded silicon interface passivation by Kelvin probe force microscopy and corona oxide characterization of semiconductor. Review of Scientific Instruments, 92(8), 083905. https://doi.org/10.1063/5.0052885

Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., Millán, C., Park, H., Adams, C., Glassman, C. R., DeGiovanni, A., Pereira, J. H., Rodrigues, A. V., van Dijk, A. A., Ebrecht, A. C., … Baker, D. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557), 871–876. https://doi.org/10.1126/science.abj8754

Belete, T. M. (2020). A review on Promising vaccine development progress for COVID-19 disease. Vacunas, 21(2), 121–128. https://doi.org/10.1016/j.vacun.2020.05.002

Botchkarev, A. (2019). A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms. Interdisciplinary Journal of Information, Knowledge, and Management, 14, 045–076. https://doi.org/10.28945/4184

Braun, V., & Clarke, V. (2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative Research in Sport, Exercise and Health, 13(2), 201–216. https://doi.org/10.1080/2159676X.2019.1704846

Burkart, S., Parker, H., Weaver, R. G., Beets, M. W., Jones, A., Adams, E. L., Chaput, J., & Armstrong, B. (2022). Impact of the COVID ?19 pandemic on elementary schoolers’ physical activity, sleep, screen time and diet: A quasi?experimental interrupted time series study. Pediatric Obesity, 17(1). https://doi.org/10.1111/ijpo.12846

Challenges Faced by Working Population During Lockdown in Response to Corona Virus Outbreak. (2020). Indian Journal of Forensic Medicine & Toxicology. https://doi.org/10.37506/ijfmt.v14i4.12214

Cuschieri, S., & Grech, V. (2022). A comparative assessment of attitudes and hesitancy for influenza vis-à-vis COVID-19 vaccination among healthcare students and professionals in Malta. Journal of Public Health, 30(10), 2441–2448. https://doi.org/10.1007/s10389-021-01585-z

de Jong, T., Gillet, D., Rodríguez-Triana, M. J., Hovardas, T., Dikke, D., Doran, R., Dziabenko, O., Koslowsky, J., Korventausta, M., Law, E., Pedaste, M., Tasiopoulou, E., Vidal, G., & Zacharia, Z. C. (2021). Understanding teacher design practices for digital inquiry–based science learning: The case of Go-Lab. Educational Technology Research and Development, 69(2), 417–444. https://doi.org/10.1007/s11423-020-09904-z

DeJonckheere, M., & Vaughn, L. M. (2019). Semistructured interviewing in primary care research: A balance of relationship and rigour. Family Medicine and Community Health, 7(2), e000057. https://doi.org/10.1136/fmch-2018-000057

Dewi, W. A. F. (2020). Dampak COVID-19 terhadap Implementasi Pembelajaran Daring di Sekolah Dasar. EDUKATIF?: JURNAL ILMU PENDIDIKAN, 2(1), 55–61. https://doi.org/10.31004/edukatif.v2i1.89

Eirín-Nemiña, R. (2018). Las comunidades de aprendizaje como estrategia de desarrollo profesional de docentes de Educación física. Estudios Pedagógicos (Valdivia), 44(1), 259–278. https://doi.org/10.4067/S0718-07052018000100259

Gralewski, J., & Karwowski, M. (2018). Are Teachers’ Implicit Theories of Creativity Related to the Recognition of Their Students’ Creativity? The Journal of Creative Behavior, 52(2), 156–167. https://doi.org/10.1002/jocb.140

Gupta, S., & Deep, K. (2019). A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Systems with Applications, 119, 210–230. https://doi.org/10.1016/j.eswa.2018.10.050

Handsman, E. (2021). From Virtue to Grit: Changes in Character Education Narratives in the U.S. from 1985 to 2016. Qualitative Sociology, 44(2), 271–291. https://doi.org/10.1007/s11133-021-09475-2

Hutzler, Y., Meier, S., Reuker, S., & Zitomer, M. (2019). Attitudes and self-efficacy of physical education teachers toward inclusion of children with disabilities: A narrative review of international literature. Physical Education and Sport Pedagogy, 24(3), 249–266. https://doi.org/10.1080/17408989.2019.1571183

Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation, 44, 148–175. https://doi.org/10.1016/j.swevo.2018.02.013

Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., & Tien Bui, D. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of The Total Environment, 627, 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266

Kim, S., & Kim, H. Y. (2018). A Computational Thinking Curriculum and Teacher Professional Development in South Korea. Dalam M. S. Khine (Ed.), Computational Thinking in the STEM Disciplines (hlm. 165–178). Springer International Publishing. https://doi.org/10.1007/978-3-319-93566-9_9

Li, S., Zhao, X., & Zhou, G. (2019). Automatic pixel?level multiple damage detection of concrete structure using fully convolutional network. Computer-Aided Civil and Infrastructure Engineering, 34(7), 616–634. https://doi.org/10.1111/mice.12433

Li, X., Zhang, W., & Ding, Q. (2018). A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning. Neurocomputing, 310, 77–95. https://doi.org/10.1016/j.neucom.2018.05.021

Lin, Q., Zhao, S., Gao, D., Lou, Y., Yang, S., Musa, S. S., Wang, M. H., Cai, Y., Wang, W., Yang, L., & He, D. (2020). A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. International Journal of Infectious Diseases, 93, 211–216. https://doi.org/10.1016/j.ijid.2020.02.058

Liu, Z., Magal, P., Seydi, O., & Webb, G. (2020). A COVID-19 epidemic model with latency period. Infectious Disease Modelling, 5, 323–337. https://doi.org/10.1016/j.idm.2020.03.003

Mishra, L., Gupta, T., & Shree, A. (2020). Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. International Journal of Educational Research Open, 1, 100012. https://doi.org/10.1016/j.ijedro.2020.100012

Moutaouakil, A., Jabrane, Y., Reha, A., & Koumina, A. (2021). The Spread of the Corona Virus Disease (Covid-19) and the Launch of 5G Technology in China: What Relationship. Dalam S. Motahhir & B. Bossoufi (Ed.), Digital Technologies and Applications (Vol. 211, hlm. 919–924). Springer International Publishing. https://doi.org/10.1007/978-3-030-73882-2_83

Muthmainnah, A., Rahma, D., Robi’ah, F., & Prihantini, P. (2021). Dampak Pandemi Covid-19 terhadap Kegiatan Ektrskurikuler di Sekolah Dasar. Jurnal Basicedu, 6(1), 394–406. https://doi.org/10.31004/basicedu.v6i1.1964

Ng, E., & Tan, B. (2018). Achieving state-of-the-art ICT connectivity in developing countries: The Azerbaijan model of Technology Leapfrogging. The Electronic Journal of Information Systems in Developing Countries, 84(3), e12027. https://doi.org/10.1002/isd2.12027

Okazaki, S., Muraoka, Y., & Osu, R. (2019). Teacher-learner interaction quantifies scaffolding behaviour in imitation learning. Scientific Reports, 9(1), 7543. https://doi.org/10.1038/s41598-019-44049-x

Rampioni, M., Mo?oi, A. A., Rossi, L., Moraru, S.-A., Rosenberg, D., & Stara, V. (2021). A Qualitative Study toward Technologies for Active and Healthy Aging: A Thematic Analysis of Perspectives among Primary, Secondary, and Tertiary End Users. International Journal of Environmental Research and Public Health, 18(14), 7489. https://doi.org/10.3390/ijerph18147489

Regmi, K., & Jones, L. (2020). A systematic review of the factors – enablers and barriers – affecting e-learning in health sciences education. BMC Medical Education, 20(1), 91. 1115-Article Text-14339-1-9-20240829.docx

Samanta, S., Banerjee, J., Rahaman, S. N., Ali, K. M., Ahmed, R., Giri, B., Pal, A., & Dash, S. K. (2022). Alteration of dietary habits and lifestyle pattern during COVID-19 pandemic associated lockdown: An online survey study. Clinical Nutrition ESPEN, 48, 234–246. https://doi.org/10.1016/j.clnesp.2022.02.007

Sari, R. P., Tusyantari, N. B., & Suswandari, M. (2021). DAMPAK PEMBELAJARAN DARING BAGI SISWA SEKOLAH DASAR SELAMA COVID-19. Prima Magistra: Jurnal Ilmiah Kependidikan, 2(1), 9–15. https://doi.org/10.37478/jpm.v2i1.732

Siregar, R. Y., Gunawan, A. H., & Saputro, A. N. (2021). Impact of the Covid-19 Shock on Banking and Corporate Sector Vulnerabilities in Indonesia. Bulletin of Indonesian Economic Studies, 57(2), 147–173. https://doi.org/10.1080/00074918.2021.1956397

Srivastava, S., Kumar, A., Bauddh, K., Gautam, A. S., & Kumar, S. (2020). 21-Day Lockdown in India Dramatically Reduced Air Pollution Indices in Lucknow and New Delhi, India. Bulletin of Environmental Contamination and Toxicology, 105(1), 9–17. https://doi.org/10.1007/s00128-020-02895-w

Susanto, E., & Deapalupi, A. P. (2020). Analisis Dampak Covid-19 terhadap Implementasi Study From Home (SFH) di Tingkat Sekolah Dasar. Jurnal Pendidikan?: Riset dan Konseptual, 4(4), 536. https://doi.org/10.28926/riset_konseptual.v4i4.274

Tchamyou, V. S., Asongu, S. A., & Odhiambo, N. M. (2019). The Role of ICT in Modulating the Effect of Education and Lifelong Learning on Income Inequality and Economic Growth in Africa. African Development Review, 31(3), 261–274. https://doi.org/10.1111/1467-8268.12388

Wu, S.-Y. (2021). How Teachers Conduct Online Teaching During the COVID-19 Pandemic: A Case Study of Taiwan. Frontiers in Education, 6, 675434. https://doi.org/10.3389/feduc.2021.675434

Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., & Zhou, Q. (2020). Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science, 367(6485), 1444–1448. https://doi.org/10.1126/science.abb2762

Yang, B., Lei, Y., Jia, F., & Xing, S. (2019). An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing, 122, 692–706. https://doi.org/10.1016/j.ymssp.2018.12.051

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2020). Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Computing Surveys, 52(1), 1–38. https://doi.org/10.1145/3285029

Downloads

Published

2025-03-13

How to Cite

Suhaimi, S., Saidah, S., Rahman, R., & Idris, H. (2025). The Impact of the Covid-19 Pandemic on Student Learning Activities at Madrasah Ibtidaiyah. At-Tasyrih: Jurnal Pendidikan Dan Hukum Islam, 10(2), 509–522. https://doi.org/10.55849/attasyrih.v10i2.271