case study on impact of covid 19 on education

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 23 September 2023

Unraveling the controversial effect of Covid-19 on college students’ performance

• Luca Bonacini 1 ,
• Giovanni Gallo 2 &
• Fabrizio Patriarca 2  

Scientific Reports volume  13 , Article number:  15912 ( 2023 ) Cite this article

4359 Accesses

4 Citations

3 Altmetric

Metrics details

• Epidemiology
• Health care economics
• Social evolution

We disentangle the channels through which Covid-19 has affected the performance of university students by setting up an econometric strategy to identify separately changes in both teaching and evaluation modes, and the short and long term effects of mobility restrictions. We exploit full and detailed information from the administrative archives of one among the first universities to be shut down since the virus spread from Wuhan. The results help solving the inconsistencies in the literature by providing evidence of a composite picture where negative effects such as those caused by the sudden shift to remote learning and by the exposure to mobility restrictions, overlap to opposite effects due to a change in evaluation methods and home confinement during the exam’s preparation. Such overlap of conflicting effects, weakening the signaling role of tertiary education, would add to the learning loss by further exacerbating future consequences on the “Covid” generation.

Similar content being viewed by others

Schooling and Covid-19: lessons from recent research on EdTech

Heterogeneous peer effects of college roommates on academic performance

Grade inflation effects of capacity expansion in higher education: a longitudinal study in undergraduate teacher education programs from 2003 to 2022

Introduction.

There is a wide and varied literature stressing how the pandemic crisis has harmed the accumulation of human capital. In this article we focus on tertiary education and in particular on students’ performance. While in the case of primary and secondary education the literature converges on the emergence of a consistent learning deficit 1 , in the case of tertiary education the picture is much more controversial. To untangle the knot it is important to consider the variety of channels through which the pandemic might have affected students’ outcomes. Indeed, together with channels that affected all the population, both directly on health and indirectly through containment and lockdown measures, in the case of college education there are specific channels related to the shutdown of in presence activities as the sudden shift to remote learning, the temporary return of students to their places of origin and the change in student assessment methods that also shifted to online mode. Each channel has had impacts on different aspects of students’ careers, with different intensities and even in opposite directions. In our opinion, this composite picture helps explaining the lack of uniqueness of the evidence provided by the related literature developed so far.

Separating the overall effects between different channels requires very detailed data as to implement satisfactory econometric strategies to go beyond the identification of the overall effect based on simple comparison of pre- and post-pandemic values. For this purpose, we use the administrative data of one among the first Universities directly involved in the spread of the virus outside China: the University of Modena and Reggio Emilia. We can track between 2018 and 2021 a total of about 38,000 students, who have taken about 400,000 exams, with high-level details on the characteristics of examinations, study paths, background of students and teachers fixed effects.

By exploiting this rich dataset, we build an econometric strategy based on difference-in-differences estimations 2 to analyze the exams marks by distinguishing between the contrasting effects of the change in teaching and in assessment modes, and then consider separately the effects of exposure to lockdown measures.

On the one hand, while the transition to distance learning may have had a negative impact on learning, as confirmed by the literature on lower levels of education 3 , 4 , the need to change the assessment method may have had an opposite effect on measured performance. Indeed, since the shift to online exams made more difficult to avoid plagiarism or other misconduct 5 , this might have incentivized students to cheat. Furthermore, the exams mode itself (e.g. alone or in the classroom, with interviews or quizzes) may have affected students’ performance during the exams, and finally also teachers evaluation attitudes could have become less stringent. To solve the possible overlap of contrasting effects and correct for the possible divergent dynamics of actual and measured students’ performance, we exploit the pre-existence of courses where classes were already given, though partially, in remote mode even before the pandemic, although exams mode were the same as for the other courses. In this way we can build a difference-in-differences identification strategy exploiting the heterogeneity related to the fact that the extent shift of teaching mode has been different though the change in assessment mode has been the same.

On the other one hand, we use the information on the exam date as to take into account the effect of lockdown measures. This information allows us to build a proxy of exposure to restriction which is both time and space varying, by matching the data on the pattern of restrictions in Italian regions. Indeed, the prolonged closure of a university with a supra-regional students pool, located in an area with a relatively high cost of living, has led the majority of students to return to their homes. This led to (exogenous) different exposures to containment measures, since they had a predominantly regional character.

The results also give us a composite evidence that help us explain the puzzled results found in the literature about the effects of lockdown measures: while the overall exposure to containment measures appears to have a significant negative impact on students, being confined at home during the preparation of the exams turns out instead to have had a positive effect.

In the next sections, after a review of the related literature, we lay out a description of the case study and of the data used. Next, we present the econometric strategy and then discuss the results. Before concluding, in the final session we also perform some robustness checks.

Tertiary education and the pandemic

While the socio-economic consequences of the Covid-19 have been already studied in deep from many points of views, papers focused on the impact of the pandemic on higher education are still few and provide contradictory results. We can split this branch of literature into two groups of studies: those using pupils’ surveys 6 , 7 , 8 and those considering data on students’ actual outcomes 9 , 10 , 11 , 12 . Overall, the first ones find negative effects of the pandemic, while the second ones mostly agree on the contrary.

A pioneering contribution is provided by 6 , which surveyed approximately 1500 students at one of the largest public institutions in the United States. To our knowledge, their analysis is the first trying to get the impact of the pandemic on students’ outcomes. Results show large negative effects. Due to Covid-19, 13% of students have delayed graduation, 40% have lost a job, internship, or job offer, and 29% expect to earn less at age 35. Moreover, these effects have been highly heterogeneous: one quarter of students increased their study time by more than 4 weekly hours due to Covid-19, while another quarter decreased their study time by more than 5 h per week. This heterogeneity often followed existing socioeconomic divides. Lower-income students are 55% more likely than their higher-income peers to have delayed graduation due to Covid-19.

In the same spirit 8 , conducted an online survey on 3163 Queens College students during the summer 2020. She analyses the effect of the Covid-19 outbreak on current and expected outcomes through an estimation of individual-level subjective treatment effects. She finds that due to the pandemic, between 14 and 34% of students considered to drop-out, as they think to lose their financial assistance, or to postpone their graduation. The pandemic also deprived 39% of students of their jobs and reduced their earnings by 35%. Finally, her analysis also reveals that the effect of the pandemic on social insecurity has been different on the basis of the students’ well-being as it has been deeper for students with a federal Pell grant than their peers.

Hu et al. 7 make a contribution to the analysis on students’ self-perception as they differentiate their analysis to the previous ones asking about students’ conditions two years later since the outbreak of the pandemic, in the period between January 17 to February 25, 2022. They surveyed 151 college students in Northern Michigan asking how much their learning quality is influenced by the Covid-19 and they conclude that respondents’ education was severely affected by the pandemic, averaging a score of 7.58 on a scale of 10. These results suggest that the negative impact of Covid-19 on students’ self-perception is not limited to the short run.

Contrasting results are provided instead by the second stream of literature as in 10 , 11 , 12 . Gonzalez et al. 10 analyze the effects of Covid-19 confinement on the autonomous learning performance of students in higher education through a sort of randomized control experiment. Their study relies on a field experiment with 458 students at Universidad Autonoma de Madrid. The control group corresponds to academic years 2017/2018 and 2018/2019. The experimental group comprehends students from 2019/2020. The results show a significant positive effect of the Covid-19 confinement on students’ performance as they changed their learning strategies to a more continuous habit. Similar results hold in 11 . They estimate the effects of online education during the Covid-19 lockdown on student performance through a difference-in-differences approach using administrative data from Chinese Middle Schools. They consider three schools in the same county in Baise City before and after the Covid-19 onset. School A is the control group, as it did not provide any online educational support to its students. School B and C (treatment group) used an online platform. They point out a positive effect of online education by 0.22 of a standard deviation on student academic results. They also found that the results are homogeneous between rural and urban students.

Other contributions mainly focus on the heterogeneity of the effect across groups, but even in none of these we can find an evidence of a decrease in overall performance. Rodríguez-Planas 8 uses an event study approach to compare the gap between low-income students and their peers in the same University. She concludes that lower-income students with a lower performance during the pre-pandemic period outperformed their higher-income peers thanks to the different use of the flexible grading policy based on their financial and academic needs. In contrast, in the absence of the flexible grading policy, lower-income top-performing students would have underperformed relative to their higher-income counterparts. Engelhardt et al. 13 compare university students’ performance in the first semester affected by Covid-19 to that of the previous three ones. They do not find significant differences in performance across periods. These results are confirmed also for low-income, first-generation, and minority students. Castellanos-Serrano et al. 14 focus on the academic consequences of the Covid-19 in gender inequalities by several education performances. They consider 7477 students enrolled in just one faculty from the 2016/2017 to 2020/2021 academic years. Using a basic pre-post identification strategy, they find heterogeneous effects of the pandemic by sex since women’s results worsened in comparison to those of the pre-covid-19 period to a greater extent than for men. Besides, all sex slightly improved their results over the pandemic period. Maldonado and De Witte 15 consider the last year of primary schools in the Dutch-speaking Flemish region of Belgium. Using a 6-year panel, they perform a linear regression model with a pre-post Covid variable and find that, on average, students of the 2020 cohort experienced significant learning losses. Moreover, inequality within and across schools increased as a result of the Covid-19 crisis. Altindag et al. 16 leverage data from 15,000 students enrolled in a U.S. public university to investigate the performance of students in in-person compared to online courses during the pandemic. Using a student fixed effects model, the authors find that students in in-person courses fared better than online students with respect to their grades, the propensity to withdraw from the course, and the likelihood of receiving a passing grade. Agostinelli et al. 17 decompose the potential channels operating through the online learning, peers interactions, and the time spent with the parents. They conclude that each of these channels contribute to higher educational inequality during the pandemic.

All these studies target at the overall impact of Covid-19. Differently, Bird et al. 9 focus on the specific impact of the pandemic-triggered shift to online education. To do that they use data on students attending Virginia’s community colleges and set up an econometric strategy partially similar to that of part of our analysis: they use a difference-in-differences strategy in which the treatment groups is composed by the students enrolled in an in-person course and the control group is composed by the students which the course was provided online also before the Covid-19 widespread. Differently to the present contribution, their primary outcome of interest is the course completion, namely a binary variable equal to one whether the student received any grade sufficient (A, B, C, D, P + , or P), zero otherwise. The authors find that the shift to the online modality led to a modest decrease in course completion between 3 and 6%. This reduction in course completion is primarily driven by a large increase in course withdrawals (37% or + 2.7 percentage points in absolute terms) and, more narrowly, by an increase in course failure (10.8% or + 1.3 percentage points).

It is thus worth to notice that by focusing on a specific channel of the impact of the Covid-19 period, results shows a different picture than the one offered by the aggregate evidence. Delving deeper in this direction, in this paper we will try to solve the apparent puzzle. Our basic hypothesis is that the coexistence of negative effects reported subjectively or detected in the analysis of specific channels, together with positive effects resulting from the analysis of the overall outcomes is mainly due to the coexistence of positive effects on reported performance due to a change in evaluation standards, and negative effects on actual performance.

The case study

The case study is the University of Modena and Reggio Emilia. Unimore is a medium-sized Italian university, with a wide range of fields organized in 12 departments, ranked in the middle among Italian high education institutions, with a predominantly regional and national enrollment pool. As we will see in the econometric strategy session, this last characteristic together with the peculiarities of the relationship with pandemic events will be valuable for the purpose of the identification strategy we will use in this study. A final feature of the case study, that we will exploit in “ Econometric strategy ” section, is that a significant share of Unimore’s departments, before the pandemic, already offered degree programs where each single course provides mixed in-presence and remote classes.

At the same time, the university has recently undertaken a process of integrating all micro-data from administrative sources or interviews into a single database, Unimoredata, which enable us to analyze with a very high level of detail the performance trends of its students along the period of interest.

The pandemic at Unimore

On 21 February 2020 the Coronavirus had just begun to spread outside China and the first outbreaks of the virus were detected in the North-East of Italy. Two days later, on February 23, due to the dynamics of the virus in the neighborhood, the Emilia-Romagna Region imposed a four days closure of the activities to all the universities in its territory, thus including Unimore. This has been the first restrictive measure involving educational institutions, which will anticipate all other restrictive measures, including the first large-scale red zone, the one that the following week was imposed to the territory of the Modena province (i.e. the Italian name for the NUTS-3 region level). Indeed, since the virus spread over, the next week lessons did not turn back to in presence and the closing measures were instead extended to all Italian Universities as early as March 4, according to restrictive measures that will last until the summer.

In the Italian university system, the yearly activity is divided into two semesters, with lessons taking place from late September to December for the first semester and from late February up to the end of May in the second one. Consequently, the closure of the in-presence activities at Unimore, coincides exactly with the beginning of the second semester of the academic year 2019/2020. As a result, the shift toward remote learning at Unimore, unlike in the case of the other universities, has completely covered the semester affected by the first stage of the pandemic.

After the first wave of the virus, most Italian universities opted for solutions allowing at least a partial resumption of in-presence activities for the following semester. Unimore, instead, adopted a very restrictive policy announcing already in May 2020 that the activities would have remained in remote for all the first semester of the following academic year (i.e. 2020/2021) and that it would have been possible to attend the lessons remotely in the second semester of the following academic year independently from the evolution of the pandemic. The lessons turned back in presence only at the end of the second semester of the academic year 2020/2021and only for the first-year students. The latter decision, taken in December 2020, was driven by the fact that a second wave of Covid-19 contagions was in place during that period and a third wave was largely expected for the successive months. In Italy, to be noted, the first wave of Covid-19 contagions took roughly place from February to May 2020, the second wave from October to December 2020, and the third wave from February to April 2021.

Following the timing of the main waves of coronavirus contagions, the pandemic period can be split in three different sub-periods in the Unimore context. The first one arrives up to September 2020 and corresponds to the first wave of contagions, the complete shift of the University activities to remote mode, and to the national restrictive measures. The second period, from October 2020 to March 2021, was characterized by the fact that Unimore was still closed and lockdown measures took a regional level dimension using a four colors classification. According to this new mechanism, the tightening of restrictive measures was based on a set of indicators at the regional level—mostly related to pressures of Covid-19 contagions on the healthcare system—which distinguished white, yellow, orange and red zones.. The third period, from April 2021 onwards, was instead characterized by a partial return to in-presence activities at Unimore thanks to a progressive loosening of social distancing measures and the massive vaccination campaign.

As for the scheduling of exams, whose grades are the outcome variable we are going to consider, in line with the other Italian universities, Unimore provides three regular sessions of exams: the winter session, from the beginning of January up to the end of February; the summer session, spanning from the half of May to the end of July; and the fall session, from the end of August to the end of September. According to the specific course, there are also a number of cases where exams are held in extra-ordinary sessions (April to May and October to December). The first exams in the Covid-19 period are thus the ones in April 2020, the last exams of the first sub-period ends with the exams of the fall 2020 regular session, the second sub-period starts with the extra-ordinary sessions of October and December 2020, includes the 2021 winter session end finishes with the exams of the extraordinary session in spring 2021, the last period covers the regular sessions of summer and fall 2022.

The Unimore dataset

This study relies on Unimoredata, a database created with a specific Unimore project integrating all students’ individual information from administrative records and many large scale surveys (e.g. the Almalaurea post-degree surveys on early access to the labour market) since 2001.

Specifically, for the purpose of the presented analysis, we refer to a dataset merging together detailed information from the following administrative archives: (1) the register containing demographic characteristics of each student; (2) the archive reporting yearly information on each Unimore course attended by each student; and (3) the archive collecting all exams passed by each student attending Unimore. The latter dataset is particularly important for our analysis, as it contains full information about students’ passed exams, like the obtained mark, the date of notification, the subject, the teaching period, and teachers’ characteristics. According to the administrative data collection policies in Italian public Universities, failed exams are instead not recorded. Further investigation, however, have shown that during the pandemic the dynamics of passed exams had very a similar path to those of average exams marks which, as we will see below, have slightly increased. At same time, drop out rates increased by 2.1 percentage points, showing thus very similar patterns as those record elsewhere as in 9 .

The analysis focuses on the grades of passed exams held in the period ranging from January 2018 to September 2021, thus our reference period starts from more than two years before the pandemic and then covers all the period characterized by the first three and major waves of Covid-19. We decide to restrict the sample of analysis considering only students aged 18–36 years old. Despite students being 37 years old or more represent a clear minority group (about 2% of the sample), we choose to exclude them from the analysis because their peculiar characteristics makes overall unclear their condition during the pandemic (e.g. they may be employed in remote working or in layoff/furlough period). Due to similar reasons, we also drop from the sample those students who still haven’t held any exam one year after the standard end of the course (about 5% of the sample). We also drop the exams for which we miss information about the teacher since they correspond to courses taught by teachers who are recruited on annual contracts and thus normally change from year to year (about 9.5% of the sample). In conclusion, our analysis relies on a sample of 371.000 exams held and passed by about 38,000 students. A detailed description of all variables used in the analyses and main descriptive statistics on the sample of students are presented in the Supplementary Material (Supplementary Table S1 and Table S2 respectively).

In the second part of the analysis, we build a difference-in-differences (DID henceforth) identification strategy exploiting also the information about the courses held with mix modality of teaching. However, as the provision of such kind of courses is not common to all departments, we exclude from the sample of analysis all observations referring to departments where these course are not supplied. With this last sample restriction the second part of the analysis relies on about 230 thousand exams. Also the main descriptive statistics on this reduced sample of students are presented in the Supplementary Material (Supplementary Table S3 ).

Econometric strategy

The performance of students exams is analyzed by looking at the mark of each single exam as resulting from the administrative archives.

The benchmark model uses the following linear specification:

where \({y}_{j,i,t}\) is the mark obtained at the j exam of the student i at time t (if the student attends and passes the exam); \({X}_{i,t}\) and \({Z}_{j,t}\) are two vectors respectively of student level and exam level controls (some of them are time varying); \({m}_{t}\) is the month of the exam; \({C}_{t}\) is the dummy variable for the Covid-19 period, that is set alternatively as a single dummy or a set of dummies distinct by the 3 sub-periods outlined above, and \({\varepsilon }_{j,i,t}\) is the error term. The equation is estimated with linear OLS and errors are clustered at student level. The set of controls at student level \({X}_{i,t}\) includes: students’ demographic characteristics as gender, age, NUTS-3 region level region of birth and region of residence; the kind of upper secondary school attended before university (11 different categories); a dummy variable for being a sophomore or junior student and the number of exams already passed by the student at each exam date (i.e. proxies of students’ tenure and quality). The set of controls at the exam level \({Z}_{j,t}\) includes: the specific department of the degree program; a dummy for master degree courses (vs bachelor ones); the number of university credits (CFU) related to the exam; the exam month. To be clear, in the Italian system each exam correspond to an amount of credits varying from 3 to 12, and usually equal to 6 and 9; the greater is the number of credits the higher is the complexity and somewhat the difficulty of the exam. Formally, a CFU represents about 25 studying hours (in general assuming 7/8 h of lessons attendance and 17/18 h of ‘study at home’). A bachelor degree is generally reached after the completion of 180 CFU, while master degree courses count 120 CFU. Furthermore, we include in \({Z}_{j,t}\) also teachers individual fixed effects to account for this important source of heterogeneity, corresponding to 1160 dummy variables in the benchmark case.

In this benchmark model we thus focus on the coefficient \(\theta\) representing the overall impact of the pandemic, similarly to what most of the literature outlined above does. As we discussed above, this approach would catch the effect on measured performance rather than to actual one. Thus, once set up this base model, we move to assess an identification strategy aimed to disentangle the effect of the changing teaching (and thus learning) methodologies first, and then the effects of the exposure to restrictive measures.

Identifying the impact of (suddenly) changing teaching models

In this section we set up an econometric strategy to identify the impact of the shift from in-presence to remote learning brought about by social distancing measures. Thus, we are not going to evaluate the effectiveness of different teaching methodologies in normal times, we are instead analyzing the impact of a forced sudden shift that has also often caught unprepared teachers and technical staff.

As we have anticipated in previous sessions, possible negative effects on students’ actual performance could be overshadowed by opposing changes in measured performance related to changing examination modes. To avoid student misconduct, and in compliance with the general directives of the Italian Ministry of Education, Unimore adopted a set of arrangements to the remote examination modes that included student room control systems, software to control the activities of the personal computers used for examination tests (Safe Exam Browser), and limits to the ratio of examining students to teachers assigned to video surveillance. Such arrangements have reduced possible misbehavior however surely not eliminated it. At same time, the same shifts of exams modes with this related arrangements might have impacted on students’ performance during the exam. An analysis of the impacts on actual student performance, therefore, cannot disregard all this performance measurement problems. To this end, we will set up a DID identification strategy relying on the fact that while the shift in exams mode, with the related performance measuring biases, has equally concerned all courses, the change in teaching modalities has not been equal for all. Indeed, many Departments at Unimore, before the pandemic, already included in their supply degree programs with an hybrid online and in presence learning. In these programs all courses have only a share of teaching using traditional face to face methodology, and this share corresponds on average to the half of the course teaching activities with very little variation among courses. At same time, this courses have all same in-presence evaluation modes independently from the teaching modality. Thus, the shut off of in-presence activities had different consequences in terms of intensity in changing teaching modes among hybrid and standard courses but same consequences in terms of changes in evaluation modes and then also in performance measuring standards. In particular, we can argue without loss of generality that the impact in terms of changing teaching methodologies was double in the case of standard in-presence programs respect to hybrid ones.

We exploit this option in a DID approach by adding to the base specification in Eq. ( 1 ) the course modality variable and its interaction term with the Covid-19 variable:

where the variable \({D}_{j}\) is a dummy representing the course modality in normal times: in-presence ( \({D}_{j}=1\) ) or hybrid one. A negative sign of the interaction coefficient \(\pi\) would evidence a relatively worst performance for exams in traditional programs respect to those in hybrid ones and thus, according to the DID strategy interpretation of causal inference, supporting for a negative impact of the shift to distance teaching. Moreover, since the teaching modality shift is double for courses in standard programs respect to those in hybrid programs, in terms of magnitude we can state that the impact estimated is a lower bound estimation that should correspond to half of the actual impact.

In the sample of analysis, these hybrid courses represent around 19% of the students and the online teaching usually represent half of the classes for each exam. As students and exams could have different features in the two kind of programs, we correct for possible composition biases by using an Inverse Probability Weighting (IPW) strategy with the hybrid mode variable D as treatment variable. The IPW estimate relies on the following set of covariates: students’ demographic characteristics (i.e. gender, age, and NUTS-3 level region of birth and residence); the kind of upper secondary school; the year of enrollment; the specific department; and a dummy for master degree courses (vs bachelor ones). Finally, to properly isolate the effect of changing in teaching modes, we restrict the sample to the exams corresponding to classes taught in the immediately preceding teaching period (i.e. about 161 out of 223 thousand of exams). In fact, exams can be attended either in the months immediately following the end of classes but also in next semesters, several months after. We limit our analysis to the former case of ‘on-schedule’ exams. With this sample restriction we narrow the analysis on exams prepared by students attending courses taught according the modalities corresponding to the same specific period (before and after Covid-19 and also, in case, to the specific sub-periods). Moreover, by doing so, we can focus on exams whose preparation is more strictly related to the classes attendance rather than to the use of supplementary materials, such as handbooks or slides.

Identifying the impact of the exposure to restrictions

To identify the effect of exposure to restrictive and lockdown measures, we exploit the consequences of the very prudential policy implemented by Unimore about the recovery of normal activity described above.

As elsewhere, the closure of universities led to the return to their origin places of a large part of students being not resident in the neighborhood of the University. Suggestive is the case of Milan, where the news of the regional lockdown for the following day, circulated in advance because of a communication mistake, caused an exodus of students from North to South Italy so massive as to strongly impact on the spread of the virus in the southern regions of the Country while it was still concentrated only in the Northern regions.

During summer 2020, while Covid-19 related restrictions had been loosened by the national government, the universities were allowed to decide autonomously whether to re-start in presence activities for the next year. The decision in most campus or university cities contexts to reactivate in-presence activities, with the need to bear the cost of new infrastructure needed to respect legal prescriptions for social distancing, have also been driven by the economic interests of the neighborhood, for which the closure of the university leads to significant losses, like in the case of the owners of rental properties, commercial activities, and so on.

This was not actually the case for Unimore. In this area, the university has indeed a significant impact on its territory, but the economic vocation is another, ranging from automotive (Ferrari, Maserati, etc.) to food processing (e.g. Parmigiano Reggiano, Modena’s Balsamico), via robotics and ceramics. Moreover, the Modena city hospital, which was among those most put under pressure since the first waves of the pandemic, is part of the same university and has significant political weight even in the managerial offices (the same chancellor was a professor of the department of Medicine). As a consequence, the subjective experiences of professors and other civil servants grounded in departments operating within the Modena hospital understandably had a weight on their attitudes on the level of precautions to take.

As a result, Unimore adopted different decision respect to most universities, as the neighboring University of Bologna, which guaranteed a reopening of activities also through ad hoc investments for mixed teaching and the intervention of public institutions providing housing supports for students. Just before the end of the second semester of 2020, Unimore finally announced that the activities of the first semester of the following academic year—starting in September 2020—would have kept the distance mode. This exacerbates the emptying of the cities of Modena and Reggio Emilia, as evidenced by the attention given by the local press. Indeed, since then, also for the contribution of the very high living costs characterizing the cities of Modena and Reggio Emilia, most students returned to their homes and freshmen did not come in Modena and Reggio Emilia to find a new accommodation. This depletion is also confirmed by the fact that at the end of the second semester of the 2021/2022 academic year, when in-person attendance was reopened for a number of courses, despite the announcement made well in advance, only a minority share of students actually returned physically to the classroom while the rest continued to attend remotely. This decision did not turn out to be so wrong if one considers that the arrival of the second and third waves of the virus also induced the other universities to close down again.

At the same time, restrictive measures took a regional articulation from October 2020, following the four-color classification mentioned above. This induced a strong heterogeneity in students exposure to restrictions. The restrictions adopted in the case of red classification are similar to the lockdown implemented nationwide from March to May 2020, thus an overall home confinement. Accordingly, the time-varying restrictions in place at the residence of each student are a reliable proxy of the restrictions to which she has been subjected having a relevant time and space varying dimension. Figure  1 gives evidence of the regional heterogeneity of the cumulated restrictions from the beginning of the pandemic to September 2021, but the time-varying dimension of restrictions is relevant as well. To be noted, for the sake of the analysis, the national level lockdown imposed during the first wave of the virus, which lasted 70 days, is considered as a red zone and included on each regions’ records.

Cumulated number of days in red classified regional conditions.

We exploit this peculiarity to analyze two different aspects of the exposure to the restrictive measures. First, we consider the impact of cumulated exposition to restrictive measures since the start of pandemic. Second, we consider the effect of exposure to restrictive measures during the exam preparation period. To do all this, we add to the benchmark model in Eq. ( 1 ) one variable in two different cases. For each date of exam, in the first case we compute the cumulated number of days that the region of residence has passed under red zone restrictions while in the second one we compute the share of days in red zone over the 14 days before the exam. As we count among the days spent in a red zone also those related to the national level lockdown, when these variables still have a time-varying dimension and then allow for some heterogeneity, we can use all the data period from May 2020 onwards.

In detailing our findings, we start by providing an overall picture of students’ performance after and before the Covid-19 pandemic in Table 1 . In the first column we report estimates of the model specification presented in Eq. ( 1 ) and, in particular, the coefficient of the Covid-19 dummy variable being 1 for the whole period ranging from April 2020 to September 2021. The coefficient is positive and significant at 1% level.

In terms of magnitude, considering that exam marks at the Italian universities are expressed over 30 points with 18 being the minimum of passed exams and the standard deviation in the sample is 3.7 points, the value of 0.186 reported in the first column of Table 1 is not negligible although low. When we look at the three sub-periods of pandemic discussed in “ The pandemic at Unimore ” section separately, the coefficient is still positive and significant for each sub-period (second column of Table 1 ). The positive effect is concentrated in the first two periods of the pandemic, where the coefficient is a bit greater than 0.2. In the third period (i.e. April-September 2021), the coefficient becomes much lower but it remains still significant. The lower magnitude of the coefficient in the last period is consistent with the partial reopening of in-presence activities, which could blur the pandemic influence on the students’ performances. To account for this possible confounding factor, in the third column of Table 1 , we report the estimate of the overall impact limited to the first year of the pandemic only, thus limiting the reference period to April 2021 rather than September 2021. In this case the coefficient of the Covid-19 dummy variable has a value close to those reported in the first two sub-periods of pandemic.

In conclusion, the evidence provided in Table 1 would suggest that in relation to the sample of passed exams, students’ performance has slightly benefited from the pandemic, consistently with other studies of the literature surveyed above which use the same Covid-19 period dummy variable approach or else rely on some descriptive evidence. Our explanatory hypothesis, that we attempt to confirm in what follows, is that this unexpected outcome is mainly driven by a misalignment between the reported performance and the actual one. Indeed, because of the shut off of all in-presence activities, not only classes but also the exam evaluation shift to remote, becoming more slack. (To be clear, we are not able to assess whether these changes in the evaluation standards are due to a change in the kind of exams made—which also shifted from in-presence to remote—or to the adoption of magnanimous criteria by teachers.) This hypothesis could also fit with the partial different behavior of the last period, when time elapsed and experience cumulated could have impact on the effectiveness of assessment modes.

In what follows, we go beyond the analysis of the overall effect on reported performance to explore the two main different channels through which the pandemic may have negatively impacted actual performances: the sudden shift to remote teaching and the home confinement.

The impact of (suddenly) changing teaching models

Table 2 shows the estimation results of the model specification presented in Eq. ( 2 ) and corresponding to the identification strategy outlined in “ Identifying the impact of (suddenly) changing teaching models ” section. This strategy is aimed at disentangling the effect of the sudden shift to remote teaching on students’ performances. To do that, as anticipated in “ Identifying the impact of (suddenly) changing teaching models ” section, we first restrict the sample to the departments having both in-presence and hybrid courses (see “ The Unimore dataset ” section), then consider only the exams corresponding to classes taught in the immediately preceding teaching period, and then estimate the IPW weights using the course modality as treatment variable.

Column 1, 3 and 5 of Table 2 presents the same base model shown in the previous section restricted to departments providing at least one hybrid course and with the addition of a control variable for the course modality (1 if in-person and 0 otherwise) and using the IPW correction (see Supplementary Table S4 for the first stage estimations). To be noted, Supplementary Table S5 , which is the equivalent of Table 1 in the subsample used in this IPW case, highlights that the pandemic-related coefficient does not change much with respect to the one presented in Table 1 . This evidence confirms that the sample restrictions here adopted, as well as the bias on the coefficient of variables not related to the IPW treatment variable due the application of the IPW correction, does not affect significantly our results. In column 2, 4 and 6 of the same table we use the DID specification presented in “ Identifying the impact of (suddenly) changing teaching models ” section.

In the baseline case, exam marks of students attending in-presence courses are lower if compared to those reported by students attending hybrid courses. When we consider the DID model which adds the interaction term, however, the effect of attending in-presence courses is not significant anymore while the coefficient of the interaction term is negative and strongly significant. Columns 3 and 4 of Table 2 present the same analysis shown in Columns 1 and 2 limiting the reference period to the first year of pandemic (i.e. up to May 2021), thus focusing on the period during which all classes were attended remotely. Clearly, in this case, the magnitude of the interaction term is much larger than before (1.0 vs 0.6 points), as well as the one of the Covid-19 dummy (1.2 vs 0.3 points).

Summing up, the hypothesis according to which the sudden shift to remote teaching had negatively affected students’ performance finds evidence in our results. As hybrid courses generally have half of lessons in presence, we can estimate the total impact of the change in teaching modality by doubling the coefficient of the interaction term, and thus obtaining a value of about 2 points out of 30. To better understand the extent of the estimated effect related to the pandemic, it should be considered that this value represents more than half of the standard deviation of exam marks and 6.6% of the overall marks range. Our estimated value of the losses related to the shift to remote teaching is close to the upper threshold of the results provided by 9 although obtained with different econometric set-up, unit level analysis, performance outcome and in a case study of another country (US vs Italy).

At same time we also confirm the hypothesis that changes in assessment modes are prominent drivers of the increase in student reported outcomes evidenced in the literature. This effect has offset the negative impact of the pandemic period misaligning the effective performance of students from the measured one. Indeed, when we shift from the base to the DID specifications the coefficient of the Covid-19 variable increases substantially and to an extent close to the absolute value of the coefficients of the interactions included.

The impact of the exposure to restrictions

We move now to the analysis of the impact of the exposure to mobility restrictions on students’ performances described in “ Identifying the impact of the exposure to restrictions ” section. To do this purpose, we slightly restricts the sample of the benchmark case (see Table 1 ) as we exclude the exams held by students who are not resident in Italy (they represent less than 2% of the full sample of exams). The second column of Table 3 adds to the base model—whose results are reported in column 1—the overall number of days each student spent under red zone restrictions, while the third column adds the variable reporting the share of days spent under red zone restrictions over the 14 days before the exam. As explained above, in the former case we focus on the cumulated impact of restrictions, while in the latter we assess the impact of being confined at home in the days just before the exam’s session, corresponding to the period of exams’ preparation.

Table 3 highlights that the number of days spent under red zone restrictions decreases the exam marks. One day more spent under lockdown restrictions corresponds to a reduction of 0.003 points. Considering that at the end of the reference period the average value of this variable is 105 days, we can estimate the average effect on students’ exam marks at the end of the pandemic to be about one third of point. At same time, as also in the previous section, when we take into account this negatively impacting channel, the estimated coefficient of the Covid-19 dummy increases.

While the results shows a negative long run effect of home confinement, that can be ascribed to mental stress issues, when we look at the effect in the short run, things substantially change. Our results show that a greater number of days spent under red zone restrictions during the two weeks preceding the exam (i.e. probably those on which the preparation to the exam is mainly concentrated) engenders an increase of students’ exam marks. In this case, the Covid-19 dummy coefficient does not report any relevant variation, confirming the change of examination modality to represent the main explanation of the positive impact on measured performance.

In conclusion, the results of our analysis suggest a composite effect of mobility restrictions. On the one hand, consistently with the results provided by 6 , they might have increased the amount of time allocated to study for exams thus improving performances in the short run. On the one other hand, however, in the long run the protracted exposure to the restrictions clearly reduced the students’ outcomes.

Robustness checks

In this session we present two different robustness checks, one for each of the two channels we considered in main analysis: the change in teaching modalities and the exposure to mobility restrictions.

As for the change in teaching methods, we perform a placebo test analysis. Instead of restricting the sample to on-schedule exams only, we consider the other exams: those made during the pandemic but related to courses attended in the pre-Covid-19 semesters. This test should therefore be considered as valid if two conditions hold. The first one is that the coefficients of the baseline model of the Covid variables are still positive and significant. This would confirm the increase in exams grade is due to the change in exams modality and not to change in teaching modes. The second condition requires that in the DID specification the coefficients of the interaction term between the Covid-19 dummy and the in-presence course one are found to be insignificant or to have very small magnitude. Results of the placebo test, presented in Table 4 , confirm the robustness of our results. In fact, while in the baseline model the coefficients of the covid variables confirm the baseline specification results, the coefficients of the interaction terms in the DID specification are always insignificant and their magnitude is strongly reduced if not even with opposite sign if compared to those reported in Table 2 .

As for the effect of restrictions on students’ performances, one possible weakness of our strategy is the fact that some students may not have returned back to their households and thus the restrictions in place in the region of origin may not correspond to the actual restrictions to which these students where subject to. This would affect our estimates but only partially since in the first stage of pandemic, the variables of interest have only a time variation, not spatial, because restrictions had national dimension. As to the following period, the option of not coming back home does not apply to freshmen students since the decision to keep university activities in remote mode for all the first semester, and to allow in any case to attend classes in remote for all the rest of the year, was communicated well before the opening of course registration. Besides, for the same reasons the case of not coming back home even after the first Covid-19 wave is less likely to have occurred for non-freshmen students because of the rent costs that could be saved. To be noted, house rent costs in Modena and Reggio Emilia are indeed particularly high if compared to other university cities as recorded by the yearly official statistics on living costs performed by the Italian Institute of Statistics, which places the two cities among the highest in Italy for living costs. Finally, it is likely that the climate of fear and concern that had spread in the early stages of the pandemic pushed most of people returning to their household of origin just before the end of the first national lockdown in May 2020 independently from the high economic incentives.

Anyway, to account for this possible source of bias we perform a sensitivity analysis by restricting the sample to students resident out of the Modena and Reggio Emilia provinces. We consider only students who faced the same decisions about where to spend the periods of suspension of university in-person activities, thus the bias would affect randomly all kind of students. Table 5 highlights that the coefficients of variables regarding the effect of restrictive measures do not change substantially with respect to those reported in Table 3 , overall confirming the robustness of our main results.

In the Supplementary Material we also report a heterogeneity analysis of our main results (i.e. those in Table 1 and Table 2 ) to assess whether they present any relevant change when distinguishing departments by ERC sector or teachers by age group (aged 59 or younger vs aged 60 or older). Specifically, Supplementary Table S6 and Table S8 show the heterogeneity of the Covid-19 impact on students’ exam marks by ERC sectors, while Supplementary Table S7 and Table S9 do the same by teachers’ age group.

Supplementary Table S6 points out that coefficients in the first column and the last column always have the same statistically significance and direction. As for the magnitude, departments in the Life Sciences sector (e.g. Medicine and Nursing) seem the most affected by Covid-19, while coefficients of Social Sciences and Humanities and STEM sectors are very similar each other. These results are overall confirmed in the analysis by pandemic period with the exception of Social Sciences and Humanities departments, where the coefficient for the Covid III period is positive but insignificant (in line with results in Table 2 though). Moreover, while the in-presence students appear to have different performances by ERC sector, Supplementary Table S8 highlights that the Covid-19 effect related to the change of teaching modality is negative and significant in all departments except for those in Life Sciences. As for the heterogeneous effects by teachers’ age group, Supplementary Table S7 shows that coefficients are very similar, then suggesting that older teachers have not behaved differently from others. Nonetheless, the DID analysis in Supplementary Table S9 points out a heterogeneous causal effect of Covid-19, which is significant only for the subgroup of older teachers when considering the full period (column 2). This evidence seems to suggest that teachers’ reaction to pandemic-related changes was similar during the first year of pandemic, but the effect has lasted longer among older teachers. All in all, they were more vulnerable to the COVID disease and probably have had a harder time to adapt to the online modality.

Finally, Supplementary Table S10 presents a robustness check on the overall effect of the time spent in a regional red zone during the two weeks preceding the exam (see Table 3 ). First, we provide an estimation where Covid-19 period dummies are included. Second, we provide an estimation focusing on the first year of pandemic only, to assess whether the effect estimated for the full period is stable or not over time. Supplementary Table S10 clearly shows that the effect of the variable of interest here is slightly lower than the one presented in Table 3 (differences are not significant at 10 percent level though), but still strongly positive and significant. This evidence confirms the effect of being forced at home during the two weeks before the exam is actually quite stable over the analyzed period.

Conclusions

In this study, we have focused on the effect of the pandemic on the performance of university students. By exploiting the opportunities provided by an administrative dataset containing very detailed information on the University of Modena and Reggio Emilia (Unimore), one of the forerunners of the restrictions imposed worldwide to universities during Covid-19 early stages, we have tried to solve some inconsistencies in the literature and to unbundle the two main channels through which the pandemic changed university students’ pathways: the shift to remote lessons and the exposure to lockdown measures.

On the one hand, the results of the DID estimations based on the distinction between full in-presence programs and hybrid ones suggests a mismatch between actual performance and measured performance related to the change in assessment methods and/or parameters. In the standard design that uses the Covid-19 period as treatment, the evidence is that of an overall albeit slight improvement in average marks: in the context of a grading system with marks expressed in thirtieths, with 18 as the minimum grade of passed exams and a variance of 3.6, during the pandemic the score of passed the exams increased by one sixth of point, a result substantially in line with that of the literature which also shows slightly positive overall effects in a number of different outcomes of students’ performance. Besides, to a more detailed insight, the pandemic still appears to have had negative effects on student performance. The evidence gathered allows us to estimate the impact of the sudden change in lecture modes in nearly two thirtieths. This result is in line with the literature focusing on specific aspects of the Covid-19 impact and also coherent with the studies on students’ subjective evaluations. Despite this channel seems to have been more relevant, also the psychological effects due to exposure to lockdown measures result as significant: at the end of the period considered, the cumulative impact of exposure to home confinement amounting to about one third of point. At the same time, being confined at home in the two weeks prior to the examination date appears to have had a positive impact: being forced to stay at home during all the two weeks before the exams increase the average grade by nearly one sixth of point. Nonetheless, the driver of the overall positive effect on students’ grades seems to be the change in evaluation standard, that result in having increased student grades by a value in the range of 2–2.5 thirtieths.

As a result, if we look at the effect on student’s actual performance, and thus on their process of human capital accumulation, we can support the evidence of an appreciable negative impact that has been, however, offset on the surface by an average more slack assessment systems. This gives rise to two different kind of problems. The first concerns the most well-known and direct aspect: the loss in terms of human capital accumulation, a significant loss that might have long-term effects. There is however also a further aspect. This generation of students will turn out to be less prepared compared to the others, regardless their similar average marks. This, over time, could produce a stigma effect by fostering a widespread perception that those who studied in the pandemic years are less capable if compared with other ones with same degree or marks. While this may be true for some, in particular for those who have benefited most from the different assessment modes, it is not true for all. Anyway, the signaling role of their degree on job applicants would be weakened. This could result in a process of statistical discrimination: an efficient practice for those who implement it, the employers, but as unfair for an already hard-hit generation of students.

Data availability

The datasets generated and analyzed during the current study uses the information coming from the administrative archives of the University of Modena and Reggio Emilia. They are not publicly available due restrictions related to data ownership but they are available together with all do files from the corresponding author on reasonable request by remote connection to a dedicated server. The research did not rely on any kind of experiments on humans and/or the use of human tissue samples. The whole research was performed in accordance with relevant guidelines/regulations, in particular with all requirements imposed by the Italian Data Protection Authority (GDPR) in its November 27, 2008 Requirements (Gazzetta Ufficiale No. 300, December 24, 2008) and subsequent and possible adjustments and amendments. In compliance with Regulation (EU) 2016/679 of the European Parliament and of the Council of April 27, 2016, Legislative Decrees August 10, 2018 No. 101 and May 18, 2018 No. 51 of the Italian Government, the study did not required ethics approval and/or individual consent of the involved persons (the students of Unimore), who, in any case, at the time of matriculation at Unimore were informed about the processing of personal data also for purposes that respond to and are aimed at implementing the exercise of institutional powers vested in the university, including research.

Betthäuser, B. A., Bach-Mortensen, A. M. & Engzell, P. A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nat. Hum. Behav. 7 , 375–385 (2023).

Article   PubMed   Google Scholar  

Angrist, J. D. & Krueger, A. B. Empirical Strategies in Labor Economics. Handbook of Labor Economics. 3(A) 1277–1366. Springer (1999).

Grewenig, E., Lergetporer, P., Werner, K., Woessmann, L. & Zierow, L. COVID-19 and educational inequality: How school closures affect low-and high-achieving students. Eur. Econ. Rev. 140 , 103920 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Fuchs-Schündeln, N. Covid-induced school closures in the United States and Germany: Long-term distributional effects. Econ. Policy 37 (112), 609–639 (2022).

Article   Google Scholar  

Ives, B. & Cazan, A. M. Did the COVID-19 pandemic lead to an increase in academic misconduct in higher education?. Higher Education, on-line first (2023).

Aucejo, E. M., French, J., Araya, M. P. U. & Zafar, B. The impact of COVID-19 on student experiences and expectations: Evidence from a survey. J. Public Econ. 191 , 104271 (2020).

Hu, K. et al. The impact of the COVID-19 pandemic on college students in USA: Two years later. Psychiatry Res. 315 , 114685 (2022).

Rodríguez-Planas, N. Hitting where it hurts most: COVID-19 and low-income urban college students. Econ. Educ. Rev. 87 , 102233 (2022).

Bird, K. A., Castleman, B. L. & Lohner, G. Negative impacts from the shift to online learning during the COVID-19 crisis: Evidence from a statewide community college system. AERA Open 8 , 23328584221081220 (2022).

Gonzalez, T. et al. Influence of COVID-19 confinement on students’ performance in higher education. PLoS ONE 15 (10), e0239490 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Clark, A. E., Nong, H., Zhu, H. & Zhu, R. Compensating for academic loss: Online learning and student performance during the COVID-19 pandemic. China Econ. Rev. 68 , 101629 (2021).

Rodríguez-Planas, N. COVID-19, college academic performance, and the flexible grading policy: A longitudinal analysis. J. Public Econ. 207 , 104606 (2022).

Engelhardt, B., Johnson, M. & Meder, M. E. Learning in the time of Covid-19: Some preliminary findings. Int. Rev. Econ. Educ. 37 , 100215 (2021).

Castellanos-Serrano, C., Escribano, G., Paredes-Gázquez, J. & San-Martín González, E. What is behind the gender gap in economics distance education: Age, work-life balance and COVID-19. PLoS ONE 17 (8), e0272341 (2022).

Maldonado, J. E. & De Witte, K. The effect of school closures on standardised student test outcomes. Br. Edu. Res. J. 48 (1), 49–94 (2022).

Altindag, D. T., Filiz, E. S. & Tekin, E. Is online education working? National Bureau of Economic Research, No. w29113 (2021).

Agostinelli, F., Doepke, M., Sorrenti, G. & Zilibotti, F. When the great equalizer shuts down: Schools, peers, and parents in pandemic times. J. Public Econ. 206 , 104574 (2022).

Download references

Author information

Authors and affiliations.

Department of Economics, University of Bologna, 40126, Bologna, Italy

Luca Bonacini

Department of Economics Marco Biagi, University of Modena and Reggio Emilia, 41121, Modena, Italy

Giovanni Gallo & Fabrizio Patriarca

You can also search for this author in PubMed   Google Scholar

Contributions

All the authors contributed equally to the paper.

Corresponding author

Correspondence to Fabrizio Patriarca .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary tables., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Bonacini, L., Gallo, G. & Patriarca, F. Unraveling the controversial effect of Covid-19 on college students’ performance. Sci Rep 13 , 15912 (2023). https://doi.org/10.1038/s41598-023-42814-7

Download citation

Received : 22 February 2023

Accepted : 14 September 2023

Published : 23 September 2023

DOI : https://doi.org/10.1038/s41598-023-42814-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

When perceived similarity overrides demographic similarity: examining influences on stem students’ developmental mentor networks.

• Rachelle Pedersen
• Anna Woodcock
• Paul R. Hernandez

International Journal of STEM Education (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Impact of COVID-19 on Higher Education: Critical Reflections

• Original Article
• Published: 11 August 2022
• Volume 35 , pages 563–567, ( 2022 )

Cite this article

• Ka Ho Mok   ORCID: orcid.org/0000-0003-0846-1867 1  

3935 Accesses

20 Citations

Explore all metrics

This Special Issue has chosen the major focus to examine how the COVID-19 pandemic has affected higher education development and governance. The collection of articles in this Special Issue is organized with three key sub-themes, namely, student mobility, teaching and student learning, and university governance. Papers selected in this Issue were presented at different international conferences examining how the outbreak of the COVID-19 pandemic in late 2019 has affected higher education development from international and comparative perspectives. During the international research events, authors contributing their papers to this Special Issue indeed benefitted from the exchanges and dialogues from international peers. Drawing insights from the papers collected in this Special Issue, this introductory article concludes by drawing the implications for future development of international education.

Similar content being viewed by others

Rethinking Higher Education Post COVID-19

Seizing the COVID-19 Conjuncture: Re-positioning Higher Education Beyond the Pandemic

The COVID-19 Teaching Experience: The University of Petra as a Case Study

Avoid common mistakes on your manuscript.

Teaching and Student Learning

The outbreak of the COVID-19 pandemic has pushed higher education systems across different parts of the globe to adopt online platforms for conducting teaching and learning activities. Angela Hou and colleagues , in her article, ask a very important and reflective question: How would COVID-19 drive digitalization, innovations, and crisis management of higher education? More importantly, they also raise the issue of quality assurance when most higher education teaching and learning had been operating through online platforms. Based upon a case study of the INQAAHE Virtual Review, they critically examine issues related to quality assurance when higher education teaching and learning of had been digitalized. Their article does not only offer a case study of Taiwan, showing how one of the East Asian economies responded to the outbreak of the COVID-19 crisis through digitalizing higher education. This case studies also shows relevance to other parts of the world, especially when those countries/regions encounter difficulties in realizing the digitalization of teaching and student learning. International research reports educational inequality and disparity being intensified after the widespread the COVID-19 pandemic (UNESCO, 2020 , 2021 ). International and comparative research report higher education systems from relatively low-income countries/regions have suffered tremendously simply because of the lack of resources/infrastructural support for online teaching/learning, let alone diverse differences in educational cultures/management and practices across different parts of the globe (Vegas, 2020 ; Mok, et al., 2021 ).

The second article contributed by Mok , Xiong, and Ke critically examines how Chinese students evaluate overseas studies during and in the post-COVID-19 crisis, showing the growing interest of Chinese students in making Asia their future destination for studying abroad, especially when becoming more concerned about public health conditions in traditional destinations based in Europe, the UK and the USA (QS, 2020 ). The motivations and desires of Chinese students choosing overseas learning would have been affected by the new geopolitics and different kinds of “cultural shocks”, particularly when Asian students were reportedly being discriminated/stigmatized after the outbreak of the COVID-19 pandemic when studying abroad (Mbous, et al2022; Mok and Zhang, 2021 ).

Institutional Response and University Governance

Moving beyond management of teaching and student learning, Susan Robertson, critically reflecting upon the future of higher education governance set against the COVID-19 context, presented a paper at the Conference for Higher Education Research (CHER) 2020. Based upon recent works on temporality and higher education, Robertson considers such works have made important contributions to work on time, though time-future continues to be under-developed. In her presentation, she attempted to explore anticipatory governance in the contemporary university. Exploring a range of anticipatory practices and their logic in the contemporary academy, from goals to planning, predictions, forecasts, indicators, specialised knowledge, and agreements, Robertson believed we should think beyond our own box of how the future presents potential opportunities for academic development. Adopting the time-future lens in conceiving future university governance, Robertson’s paper shows the anticipatory practices mobilise different kinds of socio-temporal and political sensibilities and expectations, practices, and institutional arrangements, that constitute timescapes in the contemporary academy (Robertson, 2020 ).

Whereas Robertson discusses temporality in general, Tilak critically examines the impact of the pandemic on Indian higher education. In his article, he presents the major challenges confronting higher education development in India against the COVID-19 crisis, discussing major strategies/policy measures adopted by the Indian government in managing challenges for higher education. As India is committed to further increasing its higher education enrolments in order to produce sufficient young talents for the changing economic needs of the country, the current COVID-19 crisis would considerably disrupt its plans for higher education development. To which extent the Indian government and university leaders make use of innovative measures through the technology-enabled platforms to achieve its development goals depends not only on resources but also on careful policy coordination.

Moving away from Asia, the article contributed by O’Shea, Mou, Xu, and Aikins critically examine how higher education institutions (HEIs) in three countries, namely, Canada, China, and the USA, responded to the challenges of COVID-19 over a six-month period at the outbreak of the global pandemic. Employing document analysis, they analyze 732 publicly available communications from 27 HEIs in Canada, China, and the United States. Through the theoretical framework of Situational Critical Communications Theory (SCCT), O’Shea et al ., explore how HEIs respond to the crisis and communicate their response to the crisis to campus stakeholders. While there are important country-level distinctions among HEIs in how they communicate and respond to crisis, this research finds there are common themes across the three countries, including (1) emphasizing social responsibilities of serving the community, (2) referencing public health guidelines, and (3) offering different kinds of financial support to students. The findings shed light on strengths and weaknesses of the SCCT model in analyzing HEI responses to COVID-19 and may be helpful for HEIs to prepare for the next crisis.

Future of International Education

After the outbreak of the COVID-19 pandemic, international students are considered to be more adversely affected by COVID-19 restrictions than other student and population groups (e.g., local students) in the world (Dodd, et al., 2021 ). According to research conducted by Amoah and Mok in 2020, international students find themselves living in foreign countries/regions with limited social and economic support and in a context of rising discrimination (Amoah and Mok, 2020 ). With special attention to international student well-being, the article contributed by Amoah and Esther Mok examines the effects that COVID-19 restrictions have had and are having on the lives of international students. Such effects include direct consequences of the disease itself and its disruptive effect on this group of students and the effectiveness of the support offered by universities for the well-being of international students. The study analyzed data from a global survey conducted among international students in April 2020. They found that the well-being of international students is negatively associated with being worried about COVID-19 itself ( B = − 0.218, p = .027); with perceived COVID-19 disruption of academic activities ( B = − 0.162, p = .016); and with feelings of loneliness ( B = − 0.317, p = .000). Notably, COVID-19 information support provided by universities was positively associated with the students’ well-being ( B = 0.224, p = 0.003). These findings are discussed in the context of education policy and practical changes introduced by the COVID-19 pandemic. The discussion also considers the influence of the changing geopolitical and social environment (e.g., racism) on higher education internationalisation, critically reflecting upon management and governance issues faced by universities worldwide when promoting the well-being of international students (Mok, 2022 ).

A critical reflection of how the COVID-19 pandemic has disrupted the Australian university system, Anthony Welch shows the impact of COVID as a stark reminder that international students are so much more than cash cows for universities. Not merely do they add immeasurably to the vibrant cultural diversity of universities, they “are vital parts of communities. Indeed, many international students are future Australian citizens. It is estimated that between 20,000 and 30,000 international students move from student visas to permanent residency visas every year” a figure that is likely to be an underestimate, since students often gain another form of temporary visa, before attaining permanent residence. During the COVID-19 crisis, we have witnessed how academic cooperation and research collaboration have become highly politicized, especially when the new geopolitics has emerged as an influential force shaping international education and research.

In view of the worsening diplomatic relationship between China and Australia, Welch highlights the potential for COVID to curtail staff and student mobility, restricting research collaboration between colleagues in Australia and China. The growing anti-Chinese and anti-Asian sentiments commonly found not only in Australia but also in other major university systems in Europe and North America would create disincentives for inter-university and cross-border collaboration, which would be detrimental to future development of international education and research. According to Welch, what is urgently needed is a dialogue of civilizations, rather than a clash of civilizations, with the associated rancorous and rivalrous international relations that threaten international academic mobility and collaboration.

This Special Issue brings together thought-provocative pieces, critically reflecting upon the impact of the COVID-19 pandemic on higher education development. The challenges confronting contemporary universities are partly caused by the pandemic, disrupting the “normal operation” of universities. Nonetheless, the present global health crisis has also opened new opportunities for university teachers and leaders for exploring innovative modes of teaching and student learning, moving beyond the conventional models in developing new forms of inter-university collaborations. However, part of the problems facing universities globally is the unfavorable influences of new geopolitics creating mistrust across countries/regions. Perhaps world leaders as well as university leaders should be humbled to learn from the global health crisis resulting from the outbreak of COVID-19, seeking appropriate ways for closer and deeper collaboration for the betterment of the humanity.

Amoah, P.A. and Mok, K.H. (2020) ‘The Covid-19 pandemic and internationalisation of higher education: International students knowledge, experiences and well-being’, Higher Education Policy Institute's blog , 13 June. Available on https://www.hepi.ac.uk/2020/06/13/weekend-reading-the-covid-19-pandemic-and-internationalisation-of-higher-education-international-students-knowledge-experiences-and-wellbeing/ , accessed 18 June 2020.

Dodd, R.H., Dadaczynski, K., Okan, O., McCaffery, K.J. and Pickles, K. (2021) Psychological Wellbeing and Academic Experience of University Students in Australia during COVID-19. International Journal of Environmental Research and Public Health 18 (3): 866

Article   Google Scholar  

Mbous, Y.P.V., Mohamed, R., Rudisill, T.M. (2022) ‘International Students Challenges during the COVID-19 Pandemic in a university in the USA: A focus group study’, Current Psychology . Published online 4 February. doi: https://doi.org/10.1007/s12144-022-02776-x .

Mok, K.H. (2022) 'COVID-19 Pandemic and International Higher Education Major Challenges and Implications for East Asia', in S. Marginson and X. Xu (eds.) Higher Education in East Asia Internationalization Strategy and National Agendas London: Bloomsbury, pp. 225–246.

Google Scholar  

Mok, K.H., Xiong, W.Y. and Bin Aedy Rahman, H.N. (2021) COVID-‘19 pandemic’s disruption on university teaching and learning and competence cultivation: Student evaluation of online learning experiences in Hong Kong.’ International Journal of Chinese Education 10 (1): 1–20

Mok, K.H. and Zhang, Y.L. (2021) ‘Remaking International Higher Education for an Unequal World’, Higher Education Quarterly . Published online 13 November. doi.org/ https://doi.org/10.1111/hequ.12366 .

QS (2020) International Student Survey: Global Opportunities in the New Higher Education Paradigm. QS Report, London: QS. Available on https://unibuddy.com/wp-content/uploads/2020/10/QS_ISS2020_GLOBAL_FINAL.pdf , accessed 21 February 2022.

Robertson, S. (2020) Radical Uncertainty and Anticipatory Practices in the Pandemic University. Keynote speech presented at the Conference for Higher Education Research (CHER) 2020, Lingnan University, Hong Kong, 13-14 November 2020.

UNESCO (2020) Education: From Disruption to Recovery . https://www.unesco.org/en/covid-19/education-response , accessed 6 March 2022.

UNESCO (2021) About Virtual Student Mobility in Higher Education . https://www.iesalc.unesco.org/en/2021/01/20/about-virtual-student-mobility-in-higher-education/#_ftn1 .

Vegas, E. (2020) ‘School closures, government responses, and learning inequality around the world during COVID-19’ , Brookings, 14 April. Available on https://www.brookings.edu/research/school-closures-government-responses-and-learning-inequality-around-the-world-during-covid-19/ .

Download references

Author information

Authors and affiliations.

Lingnan University, Tuen Mun, Hong Kong, China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ka Ho Mok .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Mok, K.H. Impact of COVID-19 on Higher Education: Critical Reflections. High Educ Policy 35 , 563–567 (2022). https://doi.org/10.1057/s41307-022-00285-x

Download citation

Published : 11 August 2022

Issue Date : September 2022

DOI : https://doi.org/10.1057/s41307-022-00285-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• COVID-19 pandemic
• Quality education
• Higher education
• Student learning
• Geo-politics
• Student mobility
• Reflections from Asia
• Find a journal
• Publish with us
• Track your research

Analyzing the impact of COVID-19 on the grades of university education: A case study with economics students

Affiliations.

• 1 Department of Agricultural Economics, Statistics and Business Management, ETSIAAB, Universidad Politécnica de Madrid, Campus Ciudad Universitaria, Av. Puerta de Hierro 2-4, 28040, Madrid, Spain.
• 2 CEIGRAM, Universidad Politécnica de Madrid, Senda del Rey 13, 28040, Madrid, Spain.
• PMID: 36741347
• PMCID: PMC9886649
• DOI: 10.1016/j.ssaho.2023.100428

COVID-19 has been one of the major incidents in the global university education system in recent years. Its influence and effects on education are still difficult to determine today. Both students and teachers have had to change their study and work routines and disciplines, in many cases lacking the necessary infrastructure to adapt to online learning. Students had to start a new academic year with a complete return to face-to-face teaching without having overcome, in many cases, the incidence of online learning. This study, through 167 responses to a survey addressed to economics students at the Universidad Politécnica de Madrid, aims to analyse the causes of an improvement or a worsening of the academic performance of university students in the return to normality after having gone through COVID-19's restrictions. The results obtained show that students, students who attend tutorials and those who have evaluated online teaching positively, are the ones who have most improved their performance in the return to face-to-face teaching. And those who have suffered the physical and psychological consequences of COVID and those with less infrastructure and income have worsened their results.

Keywords: Academic performance; COVID-19; Higher education; Post-confinement; Survey.

© 2023 The Authors.

An official website of the United States government

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Publications
• Account settings
• Advanced Search
• Journal List

Online education and its effect on teachers during COVID-19—A case study from India

Surbhi dayal.

• Author information
• Article notes
• Copyright and License information

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Received 2021 Nov 13; Accepted 2023 Jan 27; Collection date 2023.

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

COVID pandemic resulted in an initially temporary and then long term closure of educational institutions, creating a need for adapting to online and remote learning. The transition to online education platforms presented unprecedented challenges for the teachers. The aim of this research was to investigate the effects of the transition to online education on teachers’ wellbeing in India.

The research was conducted on 1812 teachers working in schools, colleges, and coaching institutions from six different Indian states. Quantitative and qualitative data was collected via online survey and telephone interviews.

The results show that COVID pandemic exacerbated the existing widespread inequality in access to internet connectivity, smart devices, and teacher training required for an effective transition to an online mode of education. Teachers nonetheless adapted quickly to online teaching with the help of institutional training as well as self-learning tools. However, respondents expressed dissatisfaction with the effectiveness of online teaching and assessment methods, and exhibited a strong desire to return to traditional modes of learning. 82% respondents reported physical issues like neck pain, back pain, headache, and eyestrain. Additionally, 92% respondents faced mental issues like stress, anxiety, and loneliness due to online teaching.

As the effectiveness of online learning perforce taps on the existing infrastructure, not only has it widened the learning gap between the rich and the poor, it has also compromised the quality of education being imparted in general. Teachers faced increased physical and mental health issues due to long working hours and uncertainty associated with COVID lockdowns. There is a need to develop a sound strategy to address the gaps in access to digital learning and teachers’ training to improve both the quality of education and the mental health of teachers.

Introduction

As of November 4, 2021, the spread of novel coronavirus had reached 219 countries and territories of the world, infecting a total of 248 million people and resulting in five million deaths [ 1 ]. In March 2020, several countries including India declared a mandatory lockdown, resulting in the temporary closure of many institutions, not least educational ones. Since then, various restrictions and strategies have been implemented to counter the spread of the virus. These include wearing masks, washing hands frequently, maintaining social and physical distance, and avoiding public gatherings. The pandemic has greatly disrupted all aspects of human life and forced new ways of functioning, notably in work and education, much of which has been restricted to the household environment. The closure for over a year of many schools and colleges across the world has shaken the foundations of the traditional structures of education. Due to widespread restrictions, employees have been forced to carve out working spaces in the family home; likewise, students and teachers have been compelled to bring classes into homes [ 2 ]. Nearly 1.6 billion learners in more than 190 countries have been physically out of school due to the pandemic. In total, 94 percent of the world’s student population has been affected by school closures, and up to 99 percent of this student population come from low-to middle-income countries [ 3 ].

According to the World Economic Forum, the pandemic has changed how people receive and impart education [ 4 ]. Physical interaction between students and teachers in traditional classrooms has been replaced by exchanges on digital learning platforms, such as online teaching and virtual education systems, characterized by an absence of face-to-face connection [ 5 ]. Online education has thus emerged as a viable option for education from preschool to university level, and governments have used tools such as radio, television, and social media to support online teaching and training [ 6 ]. Various stakeholders, including government and private institutions, have collaborated to provide teachers with resources and training to teach effectively on digital platforms. New digital learning platforms like Zoom, Google Classroom, Canvas, and Blackboard have been used extensively to create learning material and deliver online classes; they have also allowed teachers to devise training and skill development programs [ 7 ]. Many teachers and students were initially hesitant to adopt online education. However indefinite closure of institutions required educational facilities to find new methods to impart education and forced teachers to learn new digital skills. Individuals have experienced different levels of difficulty in doing this; for some, “it has resulted in tears, and for some, it is a cup of tea” [ 8 ].

Teachers have reported finding it difficult to use online teaching as a daily mode of communication, and enabling students’ cognitive activation has presented a significant challenge in the use of distance modes of teaching and learning. Teachers have also expressed concerns about administering tests with minimal student interaction [ 9 ]. Lack of availability of smart devices, combined with unreliable internet access, has led to dissatisfaction with teacher-student interaction. Under pressure to select the appropriate tools and media to reach their students, some teachers have relied on pre-recorded videos, which further discouraged interaction. In locations where most teaching is done online, teachers in tier 2 and tier 3 cities (i.e., semi-urban areas) have had to pay extra to secure access to high-speed internet, digital devices, and reliable power sources [ 10 ]. Teachers in India, in particular, have a huge gap in digital literacy caused by a lack of training and access to reliable electricity supply, and internet services. In rural or remote areas, access to smart devices, the internet, and technology is limited and inconsistent [ 6 ]. In cities, including the Indian capital Delhi, even teachers who are familiar with the required technology do not necessarily have the pedagogical skills to meet the demands of online education. The absence of training, along with local factors (for example, stakeholders’ infrastructure and socio-economic standing), contributes to difficulties in imparting digital education successfully [ 10 ]. The gap in digital education across Indian schools is striking. For example, only 32.5% of school children are in a position to pursue online classes. Only 11% of children can take online classes in private and public schools, and more than half can only view videos or other recorded content. Only 8.1% of children in government schools have access to online classes in the event of a pandemic-related restrictions [ 11 ].

The adverse effects of COVID-19 on education must therefore be investigated and understood, particularly the struggles of students and teachers to adapt to new technologies. Significant societal effects of the pandemic include not only serious disruption of education but also isolation caused by social distancing. Various studies [ 7 , 12 , 13 ] have suggested that online education has caused significant stress and health problems for students and teachers alike; health issues have also been exacerbated by the extensive use of digital devices. Several studies [ 6 , 11 , 14 ] have been conducted to understand the effects of the COVID lockdown on digital access to education, students’ physical and emotional well-being, and the effectiveness of online education. However, only a few studies [ 13 , 15 – 17 ] have touched the issues that teachers faced due to COVID lockdown.

In this context, this study is trying to fill existing gaps and focuses on the upheavals that teachers went through to accommodate COVID restrictions and still impart education. It also provides an in-depth analysis of consequences for the quality of education imparted from the teachers’ perspective. It discusses geographical inequalities in access to the infrastructure required for successful implementation of online education. In particular, it addresses the following important questions: (1) how effectively have teachers adapted to the new virtual system? (2) How has online education affected the quality of teaching? (3) How has online education affected teachers’ overall health?

Because of lockdown restrictions, data collection for this study involved a combination of qualitative and quantitative methods in the form of online surveys and telephonic interviews. A questionnaire for teachers was developed consisting of 41 items covering a variety of subjects: teaching styles, life-work balance, and how working online influences the mental and physical well-being of teachers. In the interviews, participants were asked about their experiences of online teaching during the pandemic, particularly in relation to physical and mental health issues. A pilot study was conducted with thirty respondents, and necessary changes to the items were made before the data collection. The survey tool was created using google forms and disseminated via email, Facebook, and WhatsApp. A total of 145 telephonic interviews were also conducted to obtain in-depth information from the respondents.

The data were collected between December 2020 and June 2021. The Research Advisory Committee on Codes of Ethics for Research of Aggrawal College, Ballabhgarh, Haryana, reviewed and approved this study. A statement included in the google survey form as a means of acquiring written consent from the participants. Information was gathered from 1,812 Indian teachers in six Indian states (Assam, Haryana, Karnataka, Madhya Pradesh, New Delhi, and Rajasthan) working in universities, schools, and coaching institutions. Nearly three-quarters of the total sample population was women. All participants were between the ages of 18 and 60, with an average age of 34 and a clear majority being 35 or younger. Nearly three-quarters of participants work in private institutions (25% in semi-government entities and the remainder in government entities). In terms of education, 52% of participants have a graduate degree, 34% a postgraduate degree, and 14% a doctorate. Table 1 summarizes the demographic characteristics of the participants.

Table 1. Demographic details of participants.

Results & discussion.

Upon analyzing the survey responses, three crucial areas were identified for a better understanding of the effect of COVID-19 on the Indian education system and its teachers: how effectively teachers have adapted, how effective teaching has been, and how teachers’ health has been affected.

1. How effectively have teachers adapted to the new virtual system?

The first research question concerns how willing teachers were to embrace the changes brought about by the online teaching system and how quickly they were able to adapt to online modes of instruction. This information was gathered from December 2020 to June 2021, at which point teachers had been dealing with school lockdowns for months and therefore had some time to become conversant with online teaching.

While 93.82% of respondents were involved in online teaching during the pandemic, only 16% had previously taught online. These results were typically different from the results of a similar study conducted in Jordon where most of the faculty (60%) had previous experience with online teaching and 68% of faculty had also received formal training [ 16 ]. Since the spread of COVID-19 was rapid and the implementation of the lockdown was sudden, government and educational institutions were not prepared for alternative modes of learning, and teachers needed some time for adjustment. Several other factors also affected the effectiveness of the transition to online education, namely access to different types of resources and training [ 18 ].

a. Access to smart devices

Online teaching requires access to smart devices. A surprising number of teachers stated that they had internet access at home via laptops, smartphones, or tablets. A more pertinent question, however, was whether they had sole access to the smart device, or it was shared with family members. Only 37.25% of those surveyed had a device for their exclusive use while others shared a device with family members, due to lack of access to additional devices and affordability of new devices. During the lockdown, an increase in demand led to a scarcity of smart devices, so that even people who could afford to buy a device could not necessarily find one available for purchase. With children attending online classes, and family members working from home, households found it difficult to manage with only a few devices, and access to a personal digital device became an urgent matter for many. Respondents admitted to relying on their smartphones to teach courses since they lacked access to other devices. Teachers on independent-school rosters were significantly better equipped to access smart devices than those employed at other types of schools. The data also indicates that teachers in higher education and at coaching centers had relatively better access to laptops and desktop computers through their institutions, whereas teachers in elementary and secondary schools had to scramble for securing devices for their own use.

b. Internet access

Internet access is crucial for effective delivery of online education. However, our survey shows that teachers often struggled to stay connected because of substantial differences between states in the availability of internet. Of the respondents, 52% reported that their internet was stable and reliable, 32% reported it to be satisfactory and the rest reported it to be poor. Internet connectivity was better in the states of Karnataka, New Delhi, and Rajasthan than in Assam, Haryana, and Madhya Pradesh. Internet connectivity in Assam was particularly poor. Consequently, many teachers with access to advanced devices were unable to use them due to inadequate internet connection.

The following comments from a teacher in Assam capture relevant situational challenges: “I do not have an internet modem at home, and teaching over the phone is difficult. My internet connection is exhausted, and I am unable to see or hear the students.” Another teacher from Haryana reported similar difficulties: “During the lockdown, I moved to my hometown, and I do not have internet access here, so I go to a nearby village and send videos to students every three days.” Another teacher from Madhya Pradesh working at a premier institution reported experiencing somewhat different concerns: “I am teaching in one of the institute’s semi-smart classrooms, and while I have access to the internet, my students do not, making it difficult to hear what they are saying.”

These responses indicates clearly that it is not only teachers living in states where connectivity was poor who experienced difficulties in imparting education to students; even those who had good internet connectivity experiences problems caused by the poor internet connections of their students.

c. Tools for remote learning

Teachers made use of a variety of remote learning tools, but access to these tools varied depending on the educator’s affiliation. Teachers at premier institutions and coaching centers routinely used the Zoom and Google Meet apps to conduct synchronous lessons. Teachers at state colleges used pre-recorded videos that were freely available on YouTube. Teachers in government schools used various platforms, including WhatsApp for prepared material and YouTube for pre-recorded videos. To deliver the content, private school teachers used pre-recorded lectures and Google Meet. In addition to curriculum classes, school teachers offered life skill classes (for example, cooking, gardening, and organizing) to help students become more independent and responsible in these difficult circumstances. In addition to online instruction, 16% of teachers visited their students’ homes to distribute books and other materials. Furthermore, of this 36% visited students’ homes once a week, 29% visited twice a week, 18% once every two weeks, and the rest once a month. Additionally, a survey done on 6435 respondents across six states in India reported that 21% teachers in schools conducted home visits for teaching children [ 19 ].

d. Knowledge and training for the use of information and communication technologies

With the onset of the pandemic, information and communication technology (ICT) became a pivotal point for the viability of online education. The use of ICT can facilitate curriculum coverage, application of pedagogical practices and assessment, teacher’s professional development, and streamlining school organization [ 20 ]. However, the effective adoption and implementation of ICT necessitated delivery of appropriate training and prolonged practice. Also the manner in which teachers use ICT is crucial to successful implementation of online education [ 21 ]. While countries such as Germany, Japan, Turkey, the United Kingdom, and the United States recognized the importance of ICT by integrating it into their respective teacher training programmes [ 22 ], this has not been case in India. However, there are some training programmes available to teachers once they commence working. In accordance with our survey results, the vast majority of respondents (94%) lacked any ICT training or experience. In the absence of appropriate tools and support, these teachers self-experimented with online platforms, with equal chances of success and failure.

The transition from offline to online or remote learning was abrupt, and teachers had to adapt quickly to the new systems. Our data indicate that teachers in professional colleges and coaching centers received some training to help them adapt to the new online system, whereas teachers in urban areas primarily learned on their own from YouTube videos, and school teachers in rural areas received no support at all. Overall, teachers had insufficient training and support to adjust to this completely new situation. Policy research conducted on online and remote learning systems following COVID-19 has found similar results, namely that teachers implemented distance learning modalities from the start of the pandemic, often without adequate guidance, training, or resources [ 23 ]. Similar trends have been found in the Caribbean, where the unavailability of smart learning devices, lack of or poor internet access, and lack of prior training for teachers and students hampered online learning greatly. Furthermore, in many cases the curriculum was not designed for online teaching, which was a key concern for teachers [ 24 ]. Preparing online lectures as well as monitoring, supervising and providing remote support to students also led to stress and anxiety. Self-imposed perfectionism further exacerbated these issues while delivering online education [ 15 ]. A study conducted on 288 teachers from private and government schools in Delhi and National Capital Region area, also found that transition to online education has further widened the gap between pupils from government and private schools. It was more difficult to reach students from economically weaker sections of the society due to the digital divide in terms of access, usage, and skills gap. The study also found that even when teachers were digitally savvy, it did not mean that they know how to prepare for and take online classes [ 10 ].

2. How has online education affected the quality of teaching?

Once teachers had acquired some familiarity with the online system, new questions arose concerning how online education affected the quality of teaching in terms of learning and assessment, and how satisfied teachers were with this new mode of imparting education. To address these questions, specific questionnaire items about assessment and effectiveness of teaching has been included.

a. Effectiveness of online education

Respondents agreed unanimously that online education impeded student-teacher bonding. They reported several concerns, including the inattentiveness of the majority of the students in the class, the physical absence of students (who at times logged in but then went elsewhere), the inability to engage students online, and the difficulty of carrying out any productive discussion given that only a few students were participating. Another significant concern was the difficulty in administrating online tests in light of widespread cheating. In the words of one teacher: “I was teaching a new class of students with whom I had never interacted in person. It was not easy because I could not remember the names of the students or relate to them. Students were irritated when I called out their names. It had a significant impact on my feedback. I would like us to return to class so I do not have to manage four screens and can focus on my students and on solving their problems.”

For these reasons, 85.65% of respondents stated that the quality of education had been significantly compromised in the online mode. As a result, only 33% reported being interested in continuing with online teaching after COVID-19. The results show slightly higher dissatisfaction in comparison to another study conducted in India that reported 67% of teachers feeling dissatisfied with online teaching [ 25 ]. Findings of this study were similar to the findings of a survey of lecturers in Ukraine assessing the effectiveness of online education. Lower quality student work was cited as the third most mentioned problem among the problems cited by instructors in their experience with online teaching, right behind unreliable internet connectivity and the issues related with software and hardware. Primary reasons for lower quality student work were drop in the number of assignments and work quality as well as cheating. Almost half (48.7%) of the participants expressed their disapproval of online work and would not like to teach online [ 26 ].

Due to the nature of the online mode, teachers were also unable to use creative methods to teach students. Some were accustomed to using physical objects and role-playing to engage students in the classroom, but they found it extremely difficult to make learning exciting and to engage their students in virtual space. Similar trends have been reported in Australia, where schoolteachers in outback areas did not find online education helpful or practical for children, a majority of whom came from low-income families. The teachers were used to employing innovative methods to keep the students engaged in the classroom. However, in online teaching, they could not connect with their students using those methods, which significantly hampered their students’ progress. Some teachers mentioned difficulties with online teaching caused by not being able to use physical and concrete objects to improve their instructions [ 27 ].

b. Online evaluation

Of our respondents, 81% said that they had conducted online assessments of their students. Teachers used various online assessment methods, including proctored closed/open book exams and quizzes, assignment submissions, class exercises, and presentations. Teachers who chose not to administer online assessments graded their students’ performance based on participation in class and previous results.

Almost two-thirds of teachers who had administered online assessments were dissatisfied with the effectiveness and transparency of those assessments, given the high rates of cheating and internet connectivity issues. They also reported that family members had been helping students to cheat in exams because they wanted their children to get higher grades by any means necessary. In response, the teachers had tried to devise methods to discourage students and their families from cheating, but they still felt powerless to prevent widespread cheating.

As one respondent stated: “We are taking many precautions to stop cheating, such as asking to install a mirror behind the student and doing online proctoring, but students have their ways out for every matter. They disconnect the internet cable or turn it off and reconnect it later. When we question them, they have a connectivity reason ready”.

Teachers are also concerned about the effects of the digital skills gap on their creation of worksheets, assessments, and other teaching materials. As a result, some private companies have been putting together teacher training programs. The main challenge pertains to be implementation of a type of specialized education that many teachers are unfamiliar with and unwilling to adopt [ 28 ]. Because of the lack of effective and transparent online assessments, school teachers have reported that students were promoted to the next level regardless of their performance. Thus, only time will tell how successful online education has been in terms of its effects on the lives of learners.

3. How has online education affected teacher’s overall health?

The onset of the COVID-19 pandemic brought about a situation that few people had experienced or even imagined living through. Governments and individuals tried their best to adjust to the new circumstances, but sudden lockdown, confinement to the household periphery, and working from home had adverse effects on the mental and physical health of many people, including educators and students. To clarify the effects of online education on teachers’ overall health, a number of questionnaire items were focused on respondents’ feelings during the lockdown, the physical and mental health issues they experienced, and their concerns about the future given the uncertainty of the present situation.

a. Physical health issues

COVID-19 brought a multitude of changes to the lives of educators. Confinement to the household, working from home, and an increased burden of household and caregiving tasks due to the absence of paid domestic assistants increased physical workload and had corresponding adverse effects on the physical health of educators.

Of the study participants, 82% reported an increase in physical health issues since the lockdown ( Fig 1 ). Notably, 47% of those who were involved in digital mode of learning for less than 3 hours per day reported experiencing some physical discomfort daily, rising to 51% of teachers who worked online for 4–6 hours per day and 55% of teachers who worked more than 6 hours per day. Respondents reported a variety of physical health issues, including headaches, eye strain, back pain, and neck pain.

Fig 1. Number of working hours and frequency of physical issues.

The number of hours worked showed a positive correlation with the physical discomfort or health issues experienced. A chi-square test was applied to determine the relationship between the number of online working hours and the frequency of physical issues experienced by the participants and found it to be significant at the 0.05 level ( Table 2 ).

Table 2. Frequency of physical health issues experienced and number of online working hours.

As Fig 2 shows, 28% respondents’ complaint about experiencing giddiness, headaches; 59% complain of having neck and back pain. The majority of the participants had eye-strain problems most of the time; 32% faced eye problems sometimes, and 18% reported never having any eye issue. In addition, 49% had experienced two issues at the same time and 20% reported experiencing more than 2 physical issues at the same time.

Fig 2. Number of working hours and types of physical health issues.

The data in this study indicates a link between bodily distresses and hours worked. As working hours increased, so did reports of back and neck pain. 47% respondents reported back and neck pain after working for 3 hours or less, 60% after working for 3–6 hours, and nearly 70% after working for 6 hours or more.

The analysis also indicates link between physical issues experienced and the educator’s gender. Women experienced more physical discomfort than men, with 51% reporting frequent discomfort, compared to only 46% of men. Only 14% of female educators reported never experiencing physical discomfort, against 30% of male educators.

In terms of types of discomfort, 76% of female teachers and 51% of male teachers reported eye strain; 62% of female teacher and 43% of male teachers reported back and neck pain; 30% of female teachers and 18% of male teachers said they had experienced dizziness and headaches. The gender differences may be caused by the increase in household and childcare responsibilities falling disproportionately on female educators compared to their male counterparts. Several studies [ 17 , 29 – 31 ] have reported similar results, indicating that the gender gap widened during the pandemic period. The social expectations of women to take care of children increased the gender gap during the pandemic by putting greater responsibilities on women in comparison to men [ 29 ]. Women in academics were affected more in comparison to the men. Working from home burdened female educators with additional household duties and childcare responsibilities. A study done [ 32 ] in France, Germany, Italy, Norway, Sweden, the United States and the United Kingdom discovered that women were immensely affected by lockdown in comparison to men. On top of this, women with children are affected more than women without children.

No effect of age on physical discomfort was observed in this study but increasing use of online tools (such as class websites) for content creation and delivery and extended working periods were major contributors to health problems.

b. Mental health issues

The psychological effects of the COVID-19 pandemics have also proved difficult to manage. Being at home all day with limited social interaction, not to mention other pandemic-related sources of stress, affected the mental health of many people. The majority of the participants in this study admitted experiencing mental health issues including anxious feelings, low mood, restlessness, hopelessness, and loneliness. According to UNESCO [ 33 ], due to the sudden closure of schools and adaptability to new systems, teachers across the world are suffering from stress. Studies conducted in various parts of the world confirmed similar trends [ 34 , 35 ]. In Israel, teachers reported psychological stress due to online teaching. 30.4% teachers reported being stressed in comparison to 6.1% teachers in traditional classroom settings [ 34 ]. In Spain, teachers experienced various kinds of mental health issues like anxiety, stress, and depression [ 36 ]. An Arabian study found an increased number of cases related to anxiety, depression, and violence during the pandemic [ 37 ]. In New Zealand teachers in Higher education reported being overwhelmed due to the online teaching [ 15 ].

Online teaching appears to have negatively affected the mental health of all the study participants. Women (94%) reported more mental health issues than men (91%), as shown in Fig 3 . Nearly two-thirds of participants said they had been dealing with mental health issues regularly and a third occasionally; only 7% said they never dealt with them. Findings of this study are in line with other studies which found that female teachers had higher levels of stress and anxiety in comparison to men [ 36 ]. Studies conducted in China reported that teachers developed mental health issues due to online classes [ 37 , 38 ].

Fig 3. Number of online working hours and frequency of facing mental issues.

Our analysis indicated a positive relationship between the number of working hours and the frequency of mental health issues. Of the respondents who worked online for less than 3 hours, 55% experienced some kind of mental health issue; this rose to 60% of participants who worked online for 3–6 hours, and 66% of those who worked more than 6 hours every day. A chi-square test was applied to determine the relationship between the number of online working hours and the frequency of mental issues experienced by the participants and found it to be significant at the 0.05 level ( Table 3 ).

Table 3. Frequency of mental health issues experienced and number of online working hours.

In terms of types of mental health issues, respondents reported restlessness, anxious feelings, and a sense of powerlessness, along with feelings of hopelessness, low mood, and loneliness as shown in Fig 4 . The stress of adapting to a new online working environment, the extended hours of work required to prepare content in new formats, the trial-and-error nature of learning and adopting new practices, uncertainty caused by lockdown, and an overall feeling of having no control were some of the contributing factors.

Fig 4. Number of working hours and types of mental health issues.

Mental health issues were more common among those under the age of 35, with 64% reporting a problem most of the time compared to 53% of those over 35. It has been found that job uncertainty is one of the primary causes of a higher prevalence of mental health concerns among younger respondents than among older respondents. These findings are in line with other studies which found higher levels of stress among the young people in comparison to older one [ 36 , 39 ]. Feelings of loneliness and a sense of no control were reported by 30% of respondents under the age of 35, with these feelings occurring constantly or most of the time; only 12% of respondent over the age of 35 reported experiencing these feelings always or most of the time. Of respondents under 35 years of age 61% felt lonely at some point during the COVID-19 pandemic, compared to only 40% of those age 35 or older.

This study also found gender-based differences in the frequency of mental health issues experienced, with 62% of male respondents and 52% of female respondents reporting that they had always experienced mental health issues. The types of issues also differed by gender, with men more likely to report restlessness and loneliness and women more likely to report feeling anxious or helpless. More female respondents reported feelings of hopelessness than male respondents (76% compared to 69%), and they were also more anxious (66%).

The uncertainty of the pandemic seems to have caused helplessness and anxious feelings for female teachers in particular, perhaps because a lack of paid domestic help increased the burden of household and caregiving tasks disproportionately for women at a time when the pressure to adapt to new online platforms was particularly acute. In some cases, respondents left their jobs to accommodate new family dynamics, since private employers offered no assistance or flexibility. Deterioration of mental health also led to the increased number of suicides in Japan during COVID-19 [ 39 ].

However, female teachers fared better than their male counterparts on some measures of mental health. Although half of the respondents (men and women equally) reported low mood during the pandemic, the men reported more restlessness (53%) and loneliness (59%) than the women (50% and 49%, respectively). Restrictions on eating and drinking outside the household may have had a disproportionate effect on male respondents, making them more likely to feel restless or lonely than their female counterparts, who may have handled COVID-related isolation better by being more involved in household work and caregiving.

Number of hours worked online was also a factor contributing to mental health issues. Just as respondents had more physical complaints (including eye strain, back and neck pain, and headaches) the more hours they worked online, respondents who worked longer hours online reported more mental health issues.

One of the major drawbacks of online education is the widespread occurrence of physical and mental health issues, and the results of this study corroborate concerns on this point. This study found that online teaching causes more mental and physical problems for teachers than another study, which only found that 52.7% of respondents had these problems [ 12 ].

A report by the University of Melbourne has also indicated that online teaching and learning have a negative effect on the physical and mental well-being of individuals. Teachers working from home, in particular, have reported isolation, excessive screen time, inability to cope with additional stress, and exhaustion due to increased workload; despite being wary of the risks of exposure to COVID-19, they were eager to return to the campus [ 27 ].

c. Support mechanisms

In general, teachers experienced good support from family and colleagues during the pandemic, with 45.64% of teachers reported receiving strong support, 29.64 percent moderate support (although the remainder claimed to have received no or only occasional support from family and colleagues). 9.39% of male respondents reported that they have never received any support in comparison to 4.36% females. Female respondents reported receiving more support than male respondents perhaps because they have access to a more extensive network of family members and coworkers. Children, parents, and siblings were cited as the provider of a robust support system by most female respondents. For example, maternal relatives called or texted children to keep them engaged and helped them with homework, and female participants said their peers helped them to prepare lectures and materials. A link was also found between age and support; the older the respondent, the stronger the support system. A possible explanation for this difference is that older people have had time to develop stronger and longer-lasting professional and personal ties than younger people.

This study explored the effects of the COVID-19 pandemic on the Indian education system and teachers working across six Indian states. The effectiveness of online education methods varied significantly by geographical location and demographics based on internet connectivity, access to smart devices, and teachers’ training. While premier higher education institutions and some private institutions had provided teachers with the necessary infrastructure and training to implement effective successful online learning with relatively few challenges, teachers at schools and community colleges have more often been left to adopt a trial-and-error approach to the transition to an online system. Further, it indicates that online education has had a significant effect on the quality of education imparted and the lives and wellbeing of teachers. While online learning has enabled teachers to reach out to students and maintain some normalcy during a time of uncertainty, it has also had negative consequences. Owing to the lack of in-person interaction with and among students in digital classes, the absence of creative learning tools in the online environment, glitches and interruptions in internet services, widespread cheating in exams, and lack of access to digital devices, online learning adversely affected the quality of education. Teachers experienced mounting physical and mental health issues due to stress of adjusting to online platforms without any or minimal ICT training and longer working hours to meet the demands of shifting responsibilities. A positive correlation was found between working hours and mental and physical health problems.

The long-term impact of COVID-19 pandemic on both the education system and the teachers would become clear only with time. Meanwhile, this study sheds light on some of the issues that teachers are facing and needs to be addressed without further ado. These findings will provide direction to the policy makers to develop sound strategies to address existing gaps for the successful implementation of digital learning. However, researchers should continue to investigate the longer-term effects of COVID pandemic on online education.

Supporting information

Data availability.

Data apart from manuscript has been submitted as supporting information .

Funding Statement

The authors received no specific funding for this work.

• 1. WHO Coronavirus (COVID-19) Dashboard. [cited 14 Jan 2022]. Available: https://covid19.who.int
• 2. Cohen A, Nørgård RT, Mor Y. Hybrid learning spaces––Design, data, didactics. British Journal of Educational Technology. 2020;51: 1039–1044. doi: 10.1111/bjet.12964 [ DOI ] [ Google Scholar ]
• 3. unesco_covid-19_response_in_cambodia.pdf. Available: https://en.unesco.org/sites/default/files/unesco_covid-19_response_in_cambodia.pdf
• 4. Li C, Lalani F. The COVID-19 pandemic has changed education forever. This is how. In: World Economic Forum [Internet]. 29 Apr 2020. [cited 21 Jan 2022]. Available: https://www.weforum.org/agenda/2020/04/coronavirus-education-global-covid19-online-digital-learning/ [ Google Scholar ]
• 5. Daniel SJ. Education and the COVID-19 pandemic. Prospects. 2020;49: 91–96. doi: 10.1007/s11125-020-09464-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 6. Sothy C. The Impact of the COVID-19 Pandemic on Education in Cambodia. 2020. [cited 14 Jan 2022]. Available: https://www.eajournals.org/journals/british-journal-of-education-bje/vol-9-issue-1-2021/the-impact-of-the-covid-19-pandemic-on-education-in-cambodia/ [ Google Scholar ]
• 7. Pokhrel S, Chhetri R. A Literature Review on Impact of COVID-19 Pandemic on Teaching and Learning. Higher Education for the Future. 2021;8: 133–141. doi: 10.1177/2347631120983481 [ DOI ] [ Google Scholar ]
• 8. Teng MF, Wu JG. Tea or tears: online teaching during the COVID-19 pandemic. Journal of Education for Teaching. 2021;47: 290–292. doi: 10.1080/02607476.2021.1886834 [ DOI ] [ Google Scholar ]
• 9. König J, Jäger-Biela DJ, Glutsch N. Adapting to online teaching during COVID-19 school closure: teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education. 2020;43: 608–622. doi: 10.1080/02619768.2020.1809650 [ DOI ] [ Google Scholar ]
• 10. Jain S, Lall M, Singh A. Teachers’ Voices on the Impact of COVID-19 on School Education: Are Ed-Tech Companies Really the Panacea? Contemporary Education Dialogue. 2021;18: 58–89. doi: 10.1177/0973184920976433 [ DOI ] [ Google Scholar ]
• 11. aser2020wave1report_feb1.pdf. Available: https://img.asercentre.org/docs/ASER%202021/ASER%202020%20wave%201%20-%20v2/aser2020wave1report_feb1.pdf
• 12. Selvaraj A, Radhin V, Ka N, Benson N, Mathew AJ. Effect of pandemic based online education on teaching and learning system. International Journal of Educational Development. 2021; 85: 102444. doi: 10.1016/j.ijedudev.2021.102444 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 13. Joshi A, Vinay M, Bhaskar P. Impact of coronavirus pandemic on the Indian education sector: perspectives of teachers on online teaching and assessments. Interactive Technology and Smart Education. 2020. [cited 28 Jun 2022]. Available: doi: 10.1108/ITSE-06-2020-0087 [ DOI ] [ Google Scholar ]
• 14. Chinna K, Sundarasen S, Khoshaim HB, Kamaludin K, Nurunnabi M, Baloch GM, et al. Psychological impact of COVID-19 and lock down measures: An online cross-sectional multicounty study on Asian university students. PLOS ONE. 2021;16: e0253059. doi: 10.1371/journal.pone.0253059 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 15. Godber KA, Atkins DR. COVID-19 Impacts on Teaching and Learning: A Collaborative Autoethnography by Two Higher Education Lecturers. Frontiers in Education. 2021;6. Available: https://www.frontiersin.org/article/10.3389/feduc.2021.647524 [ Google Scholar ]
• 16. Almahasees Z, Mohsen K, Amin MO. Faculty’s and Students’ Perceptions of Online Learning During COVID-19. Frontiers in Education. 2021;6. Available: https://www.frontiersin.org/article/10.3389/feduc.2021.638470 [ Google Scholar ]
• 17. Augustus J. The Impact of the COVID-19 Pandemic on Women Working in Higher Education. Frontiers in Education. 2021; 6. Available: https://www.frontiersin.org/article/10.3389/feduc.2021.648365 [ Google Scholar ]
• 18. Ferri F, Grifoni P, Guzzo T. Online Learning and Emergency Remote Teaching: Opportunities and Challenges in Emergency Situations. Societies. 2020;10: 86. doi: 10.3390/soc10040086 https://www.frontiersin.org/article/10.3389/feduc.2021.647524 [ DOI ] [ Google Scholar ]
• 19. India Case Study.pdf. Available: https://www.unicef.org/rosa/media/16511/file/India%20Case%20Study.pdf
• 20. Kozma RB. ICT, Education Transformation, and Economic Development: An Analysis of the US National Educational Technology Plan. E-Learning and Digital Media. 2011;8: 106–120. doi: 10.2304/elea.2011.8.2.106 [ DOI ] [ Google Scholar ]
• 21. Comi SL, Argentin G, Gui M, Origo F, Pagani L. Is it the way they use it? Teachers, ICT and student achievement. Economics of Education Review. 2017;56: 24–39. doi: 10.1016/j.econedurev.2016.11.007 [ DOI ] [ Google Scholar ]
• 22. Usun S. Information and communications technologies (ICT) in teacher education (ITE) programs in the world and Turkey: (a comparative review). Procedia—Social and Behavioral Sciences. 2009;1: 331–334. doi: 10.1016/j.sbspro.2009.01.062 [ DOI ] [ Google Scholar ]
• 23. UNSDG | Policy Brief: Education during COVID-19 and beyond. [cited 21 Jan 2022]. Available: https://unsdg.un.org/resources/policy-brief-education-during-covid-19-and-beyond , https://unsdg.un.org/resources/policy-brief-education-during-covid-19-and-beyond
• 24. Leacock CJ, Warrican SJ. Helping teachers to respond to COVID-19 in the Eastern Caribbean: issues of readiness, equity and care. Journal of Education for Teaching. 2020; 46: 576–585. doi: 10.1080/02607476.2020.1803733 [ DOI ] [ Google Scholar ]
• 25. Report on rapid assessment of learning during school closures in context of COVID-19.pdf. Available: https://www.unicef.org/india/media/6121/file/Report%20on%20rapid%20assessment%20of%20learning%20during%20school%20closures%20in%20context%20of%20COVID-19.pdf
• 26. Bakhmat L, Babakina O, Belmaz Y. Assessing online education during the COVID-19 pandemic: a survey of lecturers in Ukraine. J Phys: Conf Ser. 2021;1840: 012050. doi: 10.1088/1742-6596/1840/1/012050 [ DOI ] [ Google Scholar ]
• 27. Ziebell N. Australian Education Survey.: 13. [ Google Scholar ]
• 28. Mudi A. Teaching in the Times of Coronavirus. In: Live Wire [Internet]. 19 Mar 2020. [cited 21 Jan 2022]. Available: https://livewire.thewire.in/personal/teaching-in-the-times-of-coronavirus/ [ Google Scholar ]
• 29. Güney‐Frahm I. Neoliberal motherhood during the pandemic: Some reflections. Gender, Work and Organization. 2020; 27: 847–856. [ Google Scholar ]
• 30. Nash M, Churchill B. Caring during COVID-19: A gendered analysis of Australian university responses to managing remote working and caring responsibilities. Gender, Work & Organization. 2020; 27: 833–846. doi: 10.1111/gwao.12484 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 31. Reflections on motherhood and the impact of COVID 19 pandemic on women’s scientific careers—Guatimosim—2020—Journal of Neurochemistry—Wiley Online Library. [cited 30 Jun 2022]. Available: https://onlinelibrary.wiley.com/doi/10.1111/jnc.15158 [ DOI ] [ PMC free article ] [ PubMed ]
• 32. Yildirim TM, Eslen-Ziya H. The differential impact of COVID-19 on the work conditions of women and men academics during the lockdown. Gender, Work & Organization. 2021;28: 243–249. doi: 10.1111/gwao.12529 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 33. https://plus.google.com/+UNESCO . Adverse consequences of school closures. In: UNESCO [Internet]. 10 Mar 2020 [cited 30 Jun 2022]. Available: https://en.unesco.org/covid19/educationresponse/consequences
• 34. Besser A, Lotem S, Zeigler-Hill V. Psychological Stress and Vocal Symptoms among University Professors in Israel: Implications of the Shift to Online Synchronous Teaching during the COVID-19 Pandemic. J Voice. 2022; 36: 291.e9–291.e16. doi: 10.1016/j.jvoice.2020.05.028 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 35. Jakubowski TD, Sitko-Dominik MM. Teachers’ mental health during the first two waves of the COVID-19 pandemic in Poland. PLOS ONE. 2021;16: e0257252. doi: 10.1371/journal.pone.0257252 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 36. Ozamiz-Etxebarria N, Berasategi Santxo N, Idoiaga Mondragon N, Dosil Santamaría M. The Psychological State of Teachers during the COVID-19 Crisis: The Challenge of Returning to Face-to-Face Teaching. Frontiers in Psychology. 2021;11. Available: https://www.frontiersin.org/article/10.3389/fpsyg.2020.620718 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 37. Al Lily AE, Ismail AF, Abunasser FM, Alhajhoj Alqahtani RH. Distance education as a response to pandemics: Coronavirus and Arab culture. Technol Soc. 2020;63: 101317. doi: 10.1016/j.techsoc.2020.101317 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 38. Zhou X, Yao B. Social support and acute stress symptoms (ASSs) during the COVID-19 outbreak: deciphering the roles of psychological needs and sense of control. European Journal of Psychotraumatology. 2020;11. doi: 10.1080/20008198.2020.1779494 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 39. Izawa S, Nakamura-Taira N, Yoshikawa T, Akamatsu R, Ikeda H, Kubo T. Conversation time and mental health during the COVID-19 pandemic: A web-based cross-sectional survey of Japanese employees. Journal of Occupational Health. 2022;64: e12334. doi: 10.1002/1348-9585.12334 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data availability statement.

• View on publisher site
• PDF (739.9 KB)
• Collections

Similar articles

Cited by other articles, links to ncbi databases.

• Download .nbib .nbib
• Format: AMA APA MLA NLM

Add to Collections

UNESCO Helps Future Generations Safeguard Intangible Heritage

#sharinghumanity is advancing technology to serve everyone.

#SharingHumanity calls for open science and the free flow of ideas. How do we promote ethical and equitable access to technology and science? 

The answer is by #SharingHumanity . 

Thank you Felipe Neto for joining our mission to share the solutions that can lead to a brighter future for all. 

Delivering in Times of Crisis

Impact stories.

UNESCO's Impact in Brief

Find out more about UNESCO's recent achievements in all fields, in every region of the world.

Our Mission

Ensure quality equitable and inclusive education and promote lifelong learning

Protect the environment through knowledge sharing, good behavior and respect for biodiversity

Encourage Open Science and the free flow of ideas, while developing ethical standards of AI and Neurosciences

Build inclusive societies by promoting respect & tolerance, fight against racism, hate speech & mis/disinformation

Our Vision : #SharingHumanity

Since wars begin in the minds of men and women, it is in the minds of men and women that peace must be built. UNESCO uses education, science, culture, communication and information to foster mutual understanding and respect for our planet. We work to strengthen the intellectual and moral solidarity of humankind and bring out the best in our shared humanity.

Building a Stronger Multilateralism

UNESCO stands on the frontline of international cooperation, in close coordination with sister agencies of the UN System, and as a privileged partner of the Group of 20 (G20). UNESCO shares knowledge, data and expertise to strengthen global coordination in the field of education, the sciences, culture, communication and information, supporting all Member States in the face of current global challenges. 

Our Shared Heritage

of exceptional cultural and natural value.

in 145 countries.

in 136 countries.

in 48 countries.

The UNESCO Courier

UNESCO’s magazine – bringing you local perspectives on global issues since 1948. Available in 6+ languages.