Jeffrey Zhang1, Emily Zhou2, Brooke Ellison3
1University of Pennsylvania, Philadelphia, PA 19104, 2The Harker School, San Jose, CA 95129, 3Center for Compassionate Care, Medical Humanities, and Bioethics, Health Science Center, Stony Brook University, Stony Brook, NY 11794
*Editors: Hugo Onghai, Ethan Pereira, Jessica Guo
Remote learning has quickly established itself as a valid educational medium. In 2018, more than 6.9 million students were enrolled in online education courses at degree-granting postsecondary institutions.[1] However, the imbalance in resources between low-income and middle-class households is a prominent issue concerning this mode of learning; students without access to devices with an internet connection are disadvantaged. Recent surveys conducted among 1,500 families suggest that low-income parents are 10 times more likely to report the ineffectiveness or even absence of remote learning support for their children. Furthermore, 36% of surveyed households that earned less than $25,000 annually claim that online education is “going poorly,” a complaint that is only seen in 18% of the families surveyed who make more than $100,000 per year.[2] Therefore, we analyzed open-source data associated with remote learning to quantify the disparity in education quality experienced by low-income students. Furthermore, we also propose various recommendations to optimize remote learning for low-income students.
Using data from the 2020 Week 1 Household Pulse Survey collected by the US Census Bureau from April 23 to May 5, we compared the percentage of devices available for educational purposes across household income ranges. As shown in Figure 1, 96% of households with an income range at or above $150,000 had devices readily available for educational purposes, while only 81% of households earning less than $50,000 had devices readily available for educational purposes. However, Figure 1 also depicts similar access to technology between low-income families and families earning between $100,000 and $149,999.
Next, we analyzed the distribution of time spent on virtual education activities between different ranges of household income. As shown in Figure 2, there is a positive correlation between time spent on virtual education activities and household income range. For instance, students whose household income was less than $50,000 averaged around 16 hours of virtual educational activities. On the other hand, students whose household incomes were between $100,000 and $149,999 averaged 18.2 hours.[3]
In the future, we hope to draw more definite conclusions about remote learning for low-income
students by gathering more data and calculating additional quantitative measurements. Nevertheless, we recommend the following actions to counteract issues low-income students may face with remote learning: 1) Employment of public resources to lessen technological inequality between families and facilitate equal education opportunities for all. 2) Enact individualized instruction, which is predicted to produce greater improvements in academic achievement than a mostly instructor-led class.[4] 3) Use additional channels of communication. This includes live video sessions for collaborative work, and breakout rooms to promote needed social interactions.
As the COVID-19 pandemic continues to keep students at home, the need to support those who are less fortunate and reduce inequality in education has become a more pressing issue that must be addressed.
References
[1] “The NCES Fast Facts Tool Provides Quick Answers to Many Education Questions (National Center for Education Statistics).” National Center for Education Statistics (NCES) Home Page, a Part of the U.S. Department of Education, nces.ed.gov/fastfacts/display.asp?id=80.
[2] Rosa, Shawna De La. “Survey: Lower-Income Students Struggling with Remote Learning.” Education Dive, 29 May 2020, www.educationdive.com/news/survey-lower-income-students-struggling-with-remote-learning/578828/.
[3] Bureau, US Census. “Week 1 Household Pulse Survey: April 23 – May 5.” Census.gov, 20 May 2020, www.census.gov/data/tables/2020/demo/hhp/hhp1.html.
[4] Zhang, D. 2005. Interactive multimedia-based e-learning: A study of effectiveness. American Journal of Distance Education 19 (3):149–62.