Prospective Teachers’ Online Learning Mathematics Activities in The Age of COVID-19: A Cluster Analysis Approach

Eddie M. Mulenga; José M. Marbán

doi:10.29333/ejmste/8345

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Eddie M. Mulenga ¹ ^* , José M. Marbán ²

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¹ PhD Candidate, School of Doctoral Studies, University of Valladolid, SPAIN² Department of Experimental, Social and Mathematical Sciences Teaching, University of Valladolid, SPAIN^* Corresponding Author

Abstract

The learning situation in the Zambian education sector has changed in the age of COVID-19 when the first two cases of COVID-19 infection were detected in the country rising to 45 local and at least 1.8 million infections globally by 13^th April, 2020. Zambia became one of the many countries globally that has prematurely closed all schools. This study examines prospective teachers’ online learning mathematics activities in the age of COVID-19 pandemic. Cluster analysis results revealed that online learning mathematics activities have significant mean differences in clustering. Cluster 2 recorded the best performance, implying that students in this cluster exhibited excellent online learning skills for mathematics in technology-rich environments in which they will be forced to study and work in the future. The study reviews various available online platforms and indicates the one that will be opened by the government which is the Educational Channel on TV.

Keywords

COVID-19
online learning
virtual teaching
mathematics education
prospective teachers
Zambia

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, Volume 16, Issue 9, September 2020, Article No: em1872

https://doi.org/10.29333/ejmste/8345

Publication date: 15 Jun 2020

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Article Downloads: 13498

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References

Abdulamir, A. S., & Hafidh, R. R. (2020). The Possible Immunological Pathways for the Variable Immunopathogenesis of COVID—19 Infections among Healthy Adults, Elderly and Children. Electronic Journal of General Medicine, 17(4), 17-20. https://doi.org/10.29333/ejgm/7850
Agnoletto, R., & Queiroz, V. (2020). COVID-19 and the challenges in Education. Centro de Estudos Sociedade e Technologia, Universidade de Sao Paulo, Bulletin, 5(2), 1-2. Retrieved from http://www.cest.poli.usp.br/download/covid-19-and-the-challenges-in-education/
Ahmed, H., Allaf, M., & Elghazaly, H. (2020). COVID-19 and medical education. The Lancet Infectious Diseases, 3099(March), 36-37. https://doi.org/10.1016/S1473-3099(20)30226-7
Basilaia, G., & Kvavadze, D. (2020). Transition to Online Education in Schools during a SARS-CoV-2 Coronavirus (COVID-19) Pandemic in Georgia. Pedagogical Research, 5(4), em0060. https://doi.org/10.29333/pr/7937
Basilaia, G., Dgebuadze, M., Kantaria, M., & Chokhonelidze, G. (2020). Replacing the Classic Learning Form at Universities as an Immediate Response to the COVID-19 Virus Infection in Georgia. International Journal for Research in Applied Science and Engineering Technology, 8, 2321-9653. https://doi.org/10.22214/ijraset.2020.3021
Bigirwa, J. P., Ndawula, S., & Naluwemba, E. F. (2020). Does the school financing role matter in E-learning adoption? An explanatory sequential study in midwifery schools in Uganda. Contemporary Educational Technology, 12(1), 1-10. https://doi.org/10.30935/cedtech/7630
Burke, J. (2020). Covid-19 Practice in Primary Schools in Ireland Report. April. https://doi.org/10.13140/RG.2.2.14091.03369
Creswell, J. W. 2005. Educational research: Planning, conducting, and evaluating quantitative and qualitative approaches to research. 2nd ed. Upper Saddle River, NJ: Merrill/Pearson Education.
Demuyakor, J. (2020). Coronavirus (COVID-19) and Online Learning in Higher Institutions of Education: A Survey of the Perceptions of Ghanaian International Students in China. Online Journal of Communication and Media Technologies, 10(3), e202018. https://doi.org/10.29333/ojcmt/8286
Edwards, A., & Daniels, H. (2004). Using sociocultural and activity theory in educational research. Educational Review, 56, 107–111. https://doi.org/10.1080/0031910410001693191
Gedera, D., & Williams, P. J. (2016). Activity Theory in Education: Research and Practice. In Activity Theory in Education: Research and Practice. https://doi.org/10.1007/978-94-6300-387-2
Goh, P., & Sandars, J. (2020). A vision of the use of technology in medical education after the COVID-19 pandemic. MedEdPublish, 9(1), 49. https://doi.org/10.15694/mep.2020.000049.1
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means Clustering Algorithm. Journal of the Royal Statistical Society, Series C., 28(1), 100-108. https://doi.org/10.2307/2346830
Huang, R. H., Liu, D. J., Tlili, A., Yang, J. F., Wang, H. H., et al. (2020). Handbook on Facilitating Flexible Learning During Educational Disruption: The Chinese Experience in Maintaining Undisrupted Learning in COVID-19 Outbreak. Beijing: Smart Learning Institute of Beijing Normal University.
Jonassen, D. H., & Rohrer-Murphy, L. (1999). Activity theory as a framework for designing constructivist learning environments. Educational Technology Research and Development, 47(1), 61-79. https://doi.org/10.1007/BF02299477
Kapetlinin, V. (1996). Activity theory: Implications for human-computer interaction. In B. A. Nardi (Ed.), Context and consciousness: Activity theory and human-computer interaction. Cambridge, MA: MIT Press.
Keefe, T. E. (2020). Crises in American Education: WWII, Baby Booms, and CORVID-19. March 2020. Retrieved from https://www.researchgate.net/publication/340166373
Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., ... Brownstein, J. S. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497. https://doi.org/10.1126/science.abb4218
Kuutti, K. (1996). Activity theory as a potential framework for human-computer interaction research. In B. A. Nardi (Ed.), Context and consciousness: Activity theory and human-computer interaction. Cambridge, MA: MIT Press.
Miettinen, R. (1999). Perspectives on Activity Theory. https://doi.org/10.1017/CBO9780511812774.002
Moll, R., & Nielsen, W. (2017). Development and validation of a social media and science learning survey. International Journal of Science Education, Part B: Communication and Public Engagement, 7(1), 14-30. https://doi.org/10.1080/21548455.2016.1161255
Mulenga, E. M., & Marbán, J. M. (2020). Is COVID-19 the Gateway for Digital Learning in Mathematics Education? Contemporary Educational Technology, 12(2), ep269. https://doi.org/10.30935/cedtech/7949
Niess, M. L. (2006). Guest Editorial: Preparing teachers to teach Mathematics with technology. Contemporary Issues in Technology and Teacher Education, 6(2). Retrieved from http://www.citejournal.org/vol6/iss2/mathematics/article1.cfm
Paterson, A. (2020). How education systems can mobilise in response to COVID-19. March 2020. Retrieved from https://www.researchgate.net/publication/339974273
Perienen, A. (2020). Frameworks for ICT Integration in Mathematics Education - A Teacher’s Perspective. Eurasia Journal of Mathematics, Science and Technology Education, 16(6). https://doi.org/10.29333/ejmste/7803
Roy, D. (2020). Trying to homeschool because of coronavirus? Here are 5 tips to help your child learn. The Conversation. Retrieved from https://theconversation.com/trying-to-homeschool-because-of-coronavirus-here-are-5-tips-to-helpyour-child-learn-133773
Schiariti, V. (2020). How to explain to our children and the general public what covid-19 is our role as researchers is to facilitate access to information for the population , especially boys and girls , and the general public access the video here. COVID-19 KT. https://doi.org/10.13140/RG.2.2.15982.56640
Sintema, E. J. (2020). Effect of COVID-19 on the Performance of Grade 12 Students: Implications for STEM Education. Eurasia Journal of Mathematics, Science and Technology Education, 16(7), em1851. https://doi.org/10.29333/ejmste/7893
Smith, E. (2020). 5 ways to keep human connections when moving learning online due to coronavirus. The Conversation.
Teddlie, C., & Tashakkori, A. (2003). Major issues and controversies in the use of mixed methods in the social and behavioral sciences. In A. Tashakkori & C. Teddlie (Eds.), Handbook on mixed methods in the behavioral and social sciences (pp. 3-50). Thousand Oaks, CA: Sage.
Zelkowski, J., Gleason, J., Cox, D. C., & Bismark, S. (2013). Developing and Validating a Reliable TPACK Instrument for Secondary Mathematics Preservice Teachers. Journal of Research on Technology in Education, 46(2), 173-206. https://doi.org/10.1080/15391523.2013.10782618

How to cite this article

APA

Mulenga, E. M., & Marbán, J. M. (2020). Prospective Teachers’ Online Learning Mathematics Activities in The Age of COVID-19: A Cluster Analysis Approach. Eurasia Journal of Mathematics, Science and Technology Education, 16(9), em1872. https://doi.org/10.29333/ejmste/8345

Vancouver

Mulenga EM, Marbán JM. Prospective Teachers’ Online Learning Mathematics Activities in The Age of COVID-19: A Cluster Analysis Approach. EURASIA J Math Sci Tech Ed. 2020;16(9):em1872. https://doi.org/10.29333/ejmste/8345

AMA

Mulenga EM, Marbán JM. Prospective Teachers’ Online Learning Mathematics Activities in The Age of COVID-19: A Cluster Analysis Approach. EURASIA J Math Sci Tech Ed. 2020;16(9), em1872. https://doi.org/10.29333/ejmste/8345

Chicago

Mulenga, Eddie M., and José M. Marbán. "Prospective Teachers’ Online Learning Mathematics Activities in The Age of COVID-19: A Cluster Analysis Approach". Eurasia Journal of Mathematics, Science and Technology Education 2020 16 no. 9 (2020): em1872. https://doi.org/10.29333/ejmste/8345

Harvard

Mulenga, E. M., and Marbán, J. M. (2020). Prospective Teachers’ Online Learning Mathematics Activities in The Age of COVID-19: A Cluster Analysis Approach. Eurasia Journal of Mathematics, Science and Technology Education, 16(9), em1872. https://doi.org/10.29333/ejmste/8345

MLA

Mulenga, Eddie M. et al. "Prospective Teachers’ Online Learning Mathematics Activities in The Age of COVID-19: A Cluster Analysis Approach". Eurasia Journal of Mathematics, Science and Technology Education, vol. 16, no. 9, 2020, em1872. https://doi.org/10.29333/ejmste/8345