The urgent transition to distance education in March 2020 revitalized the debate on the influence of a new educational reality on the educational inequality. A study was performed to measure the relationship between the difficulties experienced by students during distance learning and their socioeconomic status. Data from a nationwide survey of students administered in late March — early April 2020 was used as empirical basis of this study. Results demonstrate significant differences in obstacles faced by students from families with different levels of income. Students from low-income families were the most likely to have technical and self-regulation problems and to lack skills required for effective distance learning. Findings indicate the importance of finding system-level solutions to ensure equal opportunities for students in distance learning, regardless of their socioeconomic status.

Data from the survey shows that the transition to remote learning raised a number of challenges for students. The most widespread obstacles were technological barriers and problems associated with specific characteristics of remote learning as such and a lack of relevant skills. In particular, over one third of students said that they lacked interactions with peers and professors and experienced technical and network connectivity problems. However, the scale of problems was different for groups of students with different social and economic status. 

12% of low-income students have no access to computer, laptop, or tablet and use their mobile phones for learning. They are also less likely to use fixed broadband and more likely to use mobile Internet: 43% compared to 33% in the highest-income group (χ2= 19.942, p < 0.000). Furthermore, student from low-income backgrounds are more likely to experience technical and connectivity problems (40.5% compared to 27.6% in the highest-income group, χ2 = 43.636, p < 0.000).

The survey reveals essential variance in this parameter across the income groups. For example, the lowest-income group features the highest percentage of those who find it difficult to study from home (χ2 = 31.409, p < 0.000) and those struggling to find an appropriate study space (χ2 = 53.159, p < 0.000). Students from low-income families are much more likely to face difficulties because of their lack of skills for effective remotelearning. In particular, they find it difficult to answer questions or ask instructors for clarification online (26.4% compared to 20.4% in the highest-income group, χ2 = 11.430, p < 0.05) as well as to focus dur‑ ing self-study (30.2% compared to 22.7% in the highest-income group, χ2 = 23.068, p < 0.000), and they are more likely to experience diffi‑ culties understanding the interface of online courses and programs (17.9% compared to 12.2% in the highest-income group, χ2 = 23.383, p < 0.000) and to delay doing assignments in the remote learning format (44.7% compared to 38.0% in the highest-income group, χ2 = 15.594, p < 0.01).

Results of the present study allow assuming that disparities in remote learning environments and experiences between students from different socio-economic backgrounds may lead to inequality of educa‑ tional outcomes. Although the study did not imply collecting data on students’ performance or other objective indicators of their academic success, the questionnaire contained an item asking how students perceived the effectiveness of remote learning. Perceptions were found to vary significantly depending on family SES: 53% of students from the lowest-income group agreed that remote learning was less effective than in-person instruction, compared to 45% in the high‑ est-income group (χ2 = 11.883, p < 0.01).

The present article uses data from a cross-sectional survey of Russian university students conducted on behalf of the Ministry of Science and Higher Education between March 25 and April 3, 2020. Data was collected online using two recruitment techniques: (a) contextual targeting in the social network VKontakte displaying ads with the link to the questionnaire to users aged 17–23 and (b) distribution of the link to the questionnaire among students by university administrators as a piece of news at the official website or via email. The final sample consisted of 10,018 questionnaires completed by students from 647 Russian universities. The questionnaire was dedicated entirely to the use of remote learning technology by universities and the measures that they took to prevent COVID‑19 from spreading. The following indicators were used to assess the technological infrastructure and learning environment of students in remote learning: • Access to devices (“Please select all types of devices that you have access to”; “Apart from you, who else has access to this equipment?”) • Quality of devices (“Do the devices accessible to you meet all the functional requirements for learning?”) • Characteristics and quality of Internet connection (“Do you have access to the Internet at your current place of residence?”; “Do you experience technical or network connectivity problems?”) • Overall perception of technical challenges (“What technical constraints did you encounter after the transition to remote learning?”) Challenges encountered by students in remote learning were assessed by asking them the question, “Does remote learning present any challenges to you?” Respondents were asked to select all that applied from the following: 1) I struggle to find an appropriate study space for remote learning. 2) I am uncomfortable with the instructor asking me to turn my camera on. 3) I have no suitable devices (e.g. a computer with Internet access) for remote learning. 4) I find it difficult to understand the interface of online courses and remote learning programs. 5) I find it difficult to remain focused when watching video lectures. 6) I find it difficult to focus during self-study. 7) I find it difficult to ask the instructor questions in the absence of in-person classes. 8) I find it difficult to answer questions or ask instructors for clarification online.

The present study demonstrates empirically that remote learning may exacerbate the inequality of educational opportunity between students from different socio-econom‑ ic backgrounds. Our findings are largely consistent with the results of some studies, which found that students from low-income families faced more challenges transitioning to remote learning than their more economically advantaged peers. In particular, essential variance is observed in access to digital equipment among students from different income groups as well as in the severity of problems that they experienced due to the lack of an appropriate study space and specific skills required for effective learning in this format. Limited access to devices suitable for remote learning and inappropriateness of learning environments among students from lower-income groups are easy to understand and explain. The gap between students of low- and high-income backgrounds may affect educational outcomes. Universities can mitigate the effects of such differences by monitoring students’ access to digital equipment to identify vulnerable groups, providing necessary equipment to students in residence halls, or developing individualized learning plans with regard to access to remote learning technology. Instructors should also take the existing limitations into account. When delivering classes and designing homework and test assignments, they should keep in mind that some learners may connect via mobile devices and avoid mandatory camera policies so as to prevent exposure of low economic status or difficult living situations, which may have long-term negative psychological effects. SES disparities in students’ technical competence may y be related to differences in their patterns of online activities: difficulties understanding the interface of platforms and embracing the remote learning format may indicate that learning is not a regular online behavior for low-SES students.

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