Background of the Study
The COVID-19 pandemic brought the world to a standstill in many aspects, including the economy, education, and other critical sectors. In March 2020, the World Health Organization (WHO) declared the disease a worldwide disaster, which led to the suspension of physical learning, which prompted schools to initiate online schooling activities. While the global infection has caused several challenges all almost all sectors, it has also revealed some critical information, such as disparity in computer and the Internet access among different groups of people. Auxier and Anderson (2020) indicate that some k-12 level children have no access to the Internet from their homes. This is even worse among learners coming from areas or households with low incomes around the United States of America. Since they have no access or poor connectivity to the Internet, they face challenges when following their classwork and submitting their assignments (Dhawan, 2020). Therefore, there is a significant gap between the various social classes in terms of remote learning accessibility. This report assesses the impact the COVID-19 pandemic has had on digital online learning among the Black and Latino communities.
Various scholars have researched the issues regarding differences in online learning accessibility among various communities. Some of such studies have been sparked by the idea that schools could have remote instead of in-person teaching. However, few scholars have conducted studies on the gaps between the majorities and minorities in the US and Canada (Bates, 2018). The current study delves deeper into understanding specific issues regarding digital remote learning, based on the access to computers and the Internet in the context of WI-FI usage, completion of school work, students’ mental health, and nutrition about access and use of digital learning technologies. It is hypothesized that the Black and Latino communities face several challenges regarding access to digital online learning resources during the COVID-19 pandemic.
Significance of the Study
Conducting a study that addresses the gaps in minorities’ access to a computer and the Internet is critical in policymaking, especially when many people still face the rage of the COVID-19 pandemic. The findings from the current study will help expose the gaps in accessing the critical resources used in remote learning, hence helping to find the best interventions to address the inequalities. Furthermore, the current study also examines and analyzes what other scholars have gathered regarding online learning, which is critical to understanding future directions.
Online learning is a concept that has been in use for several years, especially since the proliferation of personal computers and the Internet. However, the closure of institutions for more extended periods, as witnessed during the global pandemic, helped to intensify the need for online learning. According to Kusel et al. (2020), remote education is one, which comprises innovation in terms of blended, social and collaborative, and flipped learning. Moreover, special software is used in specific labs to help students simulate their practical lessons and use game theories. Online learning enables synchronization of video lectures and collaboration between peers, students, and teachers (Martin, & Bolliger, 2018). Therefore, online learning has numerous practical applications, which may surpass in-person teaching, hence critical for all students. According to Littenberg-Tobias and Reich (2020), one of the best online learning frameworks is the massive open online courses (MOOC). Li and Lalani (2020) further indicate that the teaching model can be achieved by using “language apps, virtual tutoring, video conferencing tools, or online learning software” (para. 3). Thus, technology has made remote learning inevitable in teaching in institutions, which have embraced online education.
Various students prefer remote learning for personal reasons, which differ from each one. According to Xhaferi and Xhaferi (2020), online learning enables students to control how to schedule their lessons. It also enables the students to have an in-depth experience resulting from interacting with their fellows, hence having a better chance to gain the knowledge they seek from the lessons taught. Moreover, some students do not attend their lessons as required due to low Internet speeds in their homes (Garbe et al., 2020). For instance, a study by Xhaferi and Xhaferi (2020) revealed that more than a quarter of learnings from low-income households do not have personal or family computers at their homes, which indicates they have to go to cyber cafes to attend their online classes. Dorn et al. (2020) reiterate the same idea by pointing out that Blacks and Latinos are primarily in the middle to low-income households, hence having lower access to online learning technology than whites. However, these gaps exist even though the US federal government has been making efforts to reduce such differences.
Virtual classrooms provide different learning environments compared to regular in-personal learning. The design of online courses and mode of delivery has a significant impact on the students’ motivation, their satisfaction, and retention of the concepts taught (Anthony et al., 2020). According to Hadder et al. (2020), three types of interactions occur in e-learning, including peer interaction, student-content interaction, and student-instruction interaction. Mustafa et al. (2021) indicate that the pandemic revealed that the speed of the Internet used in learning and its stability are the major factors, which have huge impacts on the students’ online learning experience. Other factors include comfortability, the nature of the learning environment, how noisy or quiet it is, the support provided by the teachers, and how easy one uses the platform.
The technology acceptance model (TAM) has been considered one of the frameworks giving an in-depth understanding of how easily people can accept online learning. TAM reveals that learners can accept virtual learning due to their perception of how valuable the framework is and how easily they can easily use the technology employed in the process. TAM further dictates that students’ intention to utilize the model rely chiefly on their acceptance of the tools employed in the learning process, hence have various effects of how they perceive how useful the framework is or how easily they can use it (Esteban-Millat t al., 2018). According to Huang and Liaw (2018), TAM also reveals that students’ perception of enjoyment is critical to their acceptance of virtual training. It defines the extent to which the learners enjoy using the technology despite the harmful outcomes. Therefore, the framework reveals that students expect to benefit from using the e-learning platforms, but they cannot employ extra efforts to utilize them.
Virtual learning has been effective and has had several positive impacts on education, though it also has some adverse effects on trainees. According to Stinger (2020), virtual learning affects students’ mental well-being as they cannot physically contact their peers and instructors. Ali et al. (2019) agree with Stinger (2020) by pointing out that 35% of young scholars received mental health services between 2012 and 2015. According to Ali et al. (2020), learners from low-income households and minority groups within the US are prone to rely on institutional settings’ mental health programs because they lack the financial abilities to access private mental illness services. Consequently, the cessation of physical learning led to a lack of socialization among learning, which affected their mental stability. On the other hand, attending physical learning was crucial in most students’ access to a balanced diet. Virtual learning teaches learners what is available to them according to their families’ income and social status. This, too, affects the learners’ mental and physical health, leading to their poor performance of lack of interest in the model. The Organization for Economic Co-operation and Development (2020) reveals that stopping physical learning led to the failure of students to access dietary needs. Therefore, online learning further affects the less fortunate as their inadequacy gaps continue to expand beyond access to computers and the Internet.
Reliability and Validity
The current study understands the need to use valid and reliable data to understand the impact of the pandemic on online learning among Blacks and Latinos. Validity refers to the exactness and precision of the test conducted, leading to an acceptable conclusion. According to Streiner (2020), the validity of any dataset depends on whether it is internally or externally valid. The dataset’s internal validity describes the degree to which the experimental parameters are certain in any specific research and are the actual results of the survey. According to Pound and Ritskes-Hoitinga (2018), this validity removes possibilities of incorporating possible interventions, affecting the survey’s outcome. The present study used the assertion of Hopkins (2017) that the analysis of correlation helps to measure the validity of any dataset. This method revealed that the various variables used in the study were correlated, hence the dataset was valid for the present research.
Reliability is achieved by conducting several surveys, and the data should produce a consistent outcome (Mohajan, 2017). Reliability analysis is conducted by determining a consistent variation in proportion in a given scale, enabling establishing an association between different outcomes of the tests conducted. (Barrows et al., 2019). The current study used Viladrich et al.’s (2017) assertion that Cronbach’s alpha, as proposed by McNeish (2018), is critical in measuring the internal consistency and level of data validity. The analysis produced a value of 0.464092965, which deviated from the expected reliability result, but this was associated with some of the findings that a significant number of participants did not respond to the questions asked.
The present study used both dependent and independent variables to collect appropriate data regarding the effect of the pandemic on online learning among Black and Latino communities. Specifically, a dependent variable is one, which is being studied in the research, while an independent variable is the one controlled in the survey to have a notable effect on the dependent variable (Bloomfield & Fisher, 2019). The current research used race as the independent variable, while access to a computer and the Internet were independent variables. By changing the racial backgrounds of the participants, the study was able to find the corresponding effects on the accessibility of computer and the Internet among the different races used in the study.
An outliner refers to the observations that are at abnormal distances from other data. A large dataset usually has extreme values, which tend to be out of range from other values. The current research analyzed the dataset and determined factors such as median, lower and upper quartiles, interquartile range, lower and upper inner fences, and lower and upper outer fences to determine the outliers of the dataset. The analysis revealed that 373 and 39,611,510 are the two primary values, which were distant from the rest.
The current study hypothesized that Latinos and Blacks are disadvantaged in accessing digital online learning resources during the COVID-19 pandemic. Based on the United States Census Bureau (2021) dataset, the current research found out that there are differences among various races regarding access to a computer and the Internet, affecting online learning among students from different communities. The descriptive analysis conducted on the racial backgrounds of the participants indicated that about 1872913 Latinos and 1110072 Blacks have access to computers, compared to 4669443. On the other hand, about 1872913 Latinos and 1110072 Blacks have access to the Internet, compared to 4669443 whites. This data reveals a wide disparity in the access of these two crucial resources, which assist online learners.
Data visualization is one of the critical attributes of professional research. It enables the audience to have an idea of the findings by observing various tools used to present the data. The current research utilized radar, area, and contour plots to visualize the dataset used in the study (Kirk, 2016). The study also used descriptive analysis on various variables to help reveal the nature of the dataset utilized in the analysis of the survey’s findings. The significant attributes that help understand the data collected include means, distributions, sums, and skewness. The descriptive tables and the charts used for visualization are presented in the appendix section of this report. In conclusion, the visualization tools have made it possible to observe the study’s findings and fully understand the various elements of the dataset.
Ali, M. M., West, K., Teich, J. L., Lynch, S., Mutter, R., & Dubenitz, J. (2019). Utilization of mental health services in an educational setting by adolescents in the United States. Journal of School Health, 89(5), 393-401.
Anthony, B., Kamaludin, A., Romli, A., Raffei, A.F.M., Abdullah, A., Ming, G. L., Shukor, N. A., Nordin, M.S. and Baba, S. (2019). Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: An empirical investigation. Education and Information Technologies, 24(6), 3433-3466.
Auxier, B. & Anderson, M. (2020). As schools close due to the coronavirus, some U.S. students face a digital ‘homework gap.’ Pew Research Center. Web.
Barrows, C., Bloom, A., Ehlen, A., Ikäheimo, J., Jorgenson, J., Krishnamurthy, D., Lau, J., McBennett, B., O’Connell, M., Preston, E. & Staid, A. (2019). The IEEE reliability test system: A proposed 2019 update. IEEE Transactions on Power Systems, 35(1), 119-127.
Bates, T. (2018). The 2017 national survey of online learning in Canadian post-secondary education: methodology and results. International Journal of Educational Technology in Higher Education, 15(1), 1-17.
Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association, 22(2), 27-30. Web.
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22.
Dorn, E., Hancock, B., Sarakatsannis, J., & Viruleg, E. (2020). COVID-19 and learning loss—disparities grow and students need help. McKinsey & Company. Web.
Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895-910.
Garbe, A., Ogurlu, U., Logan, N., & Cook, P. (2020). COVID-19 and remote learning: Experiences of parents with children during the pandemic. American Journal of Qualitative Research, 4(3), 45-65.
Hadder, K., Pack, A., & Williams, L. (2020). Online Learning in a Time of Crisis: A Look at Student and Faculty Perceptions of University Responses to Covid-19 and How It Has Impacted Student and Faculty Satisfaction (Doctoral dissertation, Lipscomb University).
Hopkins, W. G. (2017). Spreadsheets for analysis of validity and reliability. Sportscience, 21(9), 36-44.
Huang, H. M., & Liaw, S. S. (2018). An analysis of learners’ intentions toward virtual reality learning based on constructivist and technology acceptance approaches. International Review of Research in Open and Distributed Learning, 19(1).
Kirk, A. (2016). Data visualization: A handbook for data driven design. Sage.
Kusel, J., Martin, F., & Markic, S. (2020). University students’ readiness for using digital media and online learning—comparison between Germany and the USA. Education Sciences, 10(11), 313.
Li, C., & Lalani, F. (2020). The COVID-19 pandemic has changed education forever. This is how. World Economic Forum. Web.
Littenberg-Tobias, J., & Reich, J. (2020). Evaluating access, quality, and equity in online learning: A case study of a MOOC-based blended professional degree program. The Internet and Higher Education, 47, 100759.
Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205-222.
McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412.
Mohajan, H. K. (2017). Two criteria for good measurements in research: Validity and reliability. Annals of Spiru Haret University. Economic Series, 17(4), 59-82.
Mustafa, F., Khursheed, A., Rizvi, S. M. U., Zahid, A., & Akhtar, A. (2021). Factors influencing online learning of university students under the covid-19 pandemic. IJERI: International Journal of Educational Research and Innovation, (15), 342-359.
Organization for Economic Co-operation and Development. (2020). The impact of COVID-19 on student equity and inclusion: Supporting vulnerable students during school closures and school re-openings. Web.
Streiner, D. L. (2020). Statistics commentary series. Commentary no. 44: Internal and external validity. Journal of Clinical Psychopharmacology, 40(6), 531-533.
United States Census Bureau. (2021). Week 23 household pulse survey: January 20 – February 1. Web.
Viladrich, C., Angulo-Brunet, A., & Doval, E. (2017). A journey around alpha and omega to estimate internal consistency reliability. Annals of Psychology, 33(3), 755-782.
Xhaferi, G., & Xhaferi, B. (2020). Online learning benefits and challenges during the covid 19-pandemic-students’ perspective from SEEU. SEEU Review, 15(1), 86-103.