Research Article

Educational Preparedness of the Home Environment: A Technological Perspective Amidst Coronavirus (COVID-19) Outbreak

Edgar John Sintema 1 * , Derick Singogo 2
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1 Universidad de Valladolid, SPAIN2 University of Zambia, ZAMBIA* Corresponding Author
International Journal of Pedagogical Development and Lifelong Learning, 2(1), 2021, ep2101, https://doi.org/10.30935/ijpdll/9290
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ABSTRACT

Schools in Zambia partner with students’ families and communities through the Parents Teachers Association to promote community participation in the academic and social welfare of students. However, this partnership is sometimes weakened by unexpected external factors to the detriment of the students’ educational development. This study used the theoretical lens of Epstein’s School-Family-Community partnership model to examine the role of parents in the home environment in creating a supportive technological environment for school children in response to the effects of COVID-19 on education. To accomplish the purpose of the study, twenty parents responded to open-ended written interviews. Data were qualitatively analyzed using content analysis. Results revealed inadequate preparation of parents to respond to the academic needs of their school children. Households were facing numerous challenges in procuring curriculum materials like prescribed textbooks. Further, it was revealed that electricity load shading negatively affected students’ access to the educational content on television and e-learning facilities aimed at supplementing school classroom instruction. The study concluded that there were limited or no technology resources in Zambian homes to support students’ access to education during the COVID-19 period.
Keywords: educational preparedness, home environment, partnership, COVID-19

CITATION (APA)

Sintema, E. J., & Singogo, D. (2021). Educational Preparedness of the Home Environment: A Technological Perspective Amidst Coronavirus (COVID-19) Outbreak. International Journal of Pedagogical Development and Lifelong Learning, 2(1), ep2101. https://doi.org/10.30935/ijpdll/9290

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