effect of covid 19 on student academic performance essay

BRIEF RESEARCH REPORT article

The impact of the covid-19 pandemic on academic performance: a comparative analysis of face-to face and online assessment.

• 1 Department of Cognitive Sciences, Psychology, Education and Cultural Studies, University of Messina, Messina, Italy
• 2 Department of Philosophy and Communication, University of Bologna, Bologna, Italy
• 3 Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund, Dortmund, Germany
• 4 Bielefeld University, University Hospital OWL, Protestant Hospital of Bethel Foundation, University Clinic of Psychiatry and Psychotherapy, University Clinic of Child and Adolescent Psychiatry and Psychotherapy, Bielefeld, Germany
• 5 Dipartimento di Psicologia “Renzo Canestrari”, Alma Mater Studiorum Università di Bologna, Cesena, Italy
• 6 Neuropsychology and Cognitive Neuroscience Research Center (CINPSI Neurocog), Universidad Católica del Maule, Talca, Chile

Introduction: Survey studies yield mixed results on the influence of the COVID-19 pandemic on academic performance, with limited direct evidence available.

Methodology: Using the academic platform from the Italian university system, a large-scale archival study involving 30,731 students and 829 examiners encompassing a total of 246,416 exams (oral tests only) to scrutinize the influence of the COVID-19 pandemic on the likelihood of passing exams was conducted. Examination data were collected both in face-to-face and online formats during the pandemic. In the pre-pandemic period, only face-to-face data were accessible.

Results: In face-to-face examination, we observed a lower probability of passing exams during the pandemic as opposed to pre-pandemic periods. Notably, during the pandemic we found an increased chance of passing exams conducted through online platforms compared to face-to-face assessments.

Discussion and conclusions: These findings provide the first direct evidence of an adverse impact of the COVID-19 pandemic on academic performance. Furthermore, the results align with prior survey studies underscoring that using telematics platforms to evaluate students' performance increases the probability of exam success. This research significantly contributes to ongoing efforts aimed to comprehend how lockdowns and the widespread use of online platforms impact academic assessment processes.

Introduction

The COVID-19 pandemic has forced nations to undergo significant restructuring across economic, health and educational systems. Recent psychological research, spanning the past 3 years, has started to illuminate the impact of prolonged exposure to a pandemic along with associated lockdowns and home confinement on cognitive and affective processing (e.g., Diotaiuti et al., 2021 , 2023 ; Fiorenzato et al., 2021 ; Wilke et al., 2021 ; Gewalt et al., 2022 ; Rania et al., 2022 ). For example, Fiorenzato et al. (2021) , documented an increase in the severity and prevalence of conditions such as depression, anxiety disorders, abnormal sleep, appetite changes, decreased libido, and health-related anxiety in the pandemic. On the cognitive level, the authors reported a paradoxical improvement in memory, compared to pre-lockdown. However, the authors of this study reported subjective complaints of the participants with respect to daily activities involving attention, temporal orientation, and executive functions. This highlights that the effects of the pandemic on mental processes extend to both affective and cognitive dimensions.

The spread of the COVID-19 pandemic has exposed education systems to unprecedented challenges, with a sudden shift of classroom-based pedagogics to distant learning approaches ( Aldossari and Chaudhry, 2021 ). This transition from face-to-face to virtual classes has resulted in a diverse spectrum of educational models: on the one hand, some professors replicated their in-person classes through videoconferencing, while, on the other hand, others undertook a comprehensive overhaul of their teaching plans to align methodological and evaluative strategies with the demands of the new context ( Fardoun et al., 2020 ; Ramos-Pla et al., 2021 ). For instance, there's an observable trend of increasing collaborative work ( Ramos-Pla et al., 2022 ), which enhances professor-student interactions—a critical predictor of students' perceived quality of teaching ( del Arco et al., 2021 ). In response to this paradigm shift, training centers across various universities adapted their programs to facilitate the continuous learning of professors. However, these educators faced challenges, expressing concerns about the time constraints in assimilating new knowledge into their teaching practices and the complexities of online evaluations ( Ramos-Pla et al., 2021 ). Moreover, other studies underscored students' difficulties in following online courses, particularly those without personal devices or sharing them with other family members ( Ramos-Pla et al., 2023 ).

In the present study, we focused on academic assessment, a pivotal sector significantly impacted by the pandemic ( Onyema et al., 2020 ; Rashid and Yadav, 2020 ; Estrada Guillén et al., 2022 ; Gewalt et al., 2022 ). This sector witnessed an extensive adoption of telematic technologies and was a dynamic response to ensure the continuity of educational services, including university services.

To the best of our knowledge, the existing literature (e.g., Mahdy, 2020 ; Radu et al., 2020 ; Son et al., 2020 ; Akin-Odanye et al., 2021 ; Andersen et al., 2022 ; Appleby et al., 2022 ; Hadwin et al., 2022 ) exploring the impact of the COVID-19 pandemic on academic performance is based on conventional survey research methodology. For instance, Mahdy (2020) examined the academic performance of veterinary medical students during the pandemic by collecting their opinions via an online Google form questionnaire. The author pointed out that while online education offers an opportunity for self-study, the main pandemic-related challenge in veterinary medical science is how to give practical lessons. Moreover, the study by Estrada Guillén et al. (2022) identified a connection between emotional intelligence and resilience to pandemics, which was associated with better academic performance. This can help to explain the mixed results provided by the literature in the field (e.g., Gonzalez et al., 2020 ; Giusti et al., 2021 ; Keržič et al., 2021 ).

Traditional–internet-based survey panels are characterized by several limitations such as response or sampling biases, desirability biases, and memory recall biases ( Andrade, 2020 ). Moreover, the sampled data might not be representative of the actual population ( Hays et al., 2015 ), potentially yielding biased results.

In the current study, we aimed to overcome such limitations by examining actual data recorded and archived within our university multifunction academic (online) platform to answer a series of outstanding questions not addressable via survey studies. This platform serves as a comprehensive teaching management computer system, providing students and professors with a dedicated space to oversee exam registration, grade management, and participation in university initiatives. The wealth of information available through this platform includes details about the scheduling of all exams, and the outcomes of each student evaluated within our university. This dataset thus provides a more reliable and accurate picture of the impact of the COVID-19 pandemic on academic achievement compared to survey studies. Furthermore, these actual data serve as a robust alternative to subjective survey measures, which are susceptible to biases, including those stemming from social expectations. Finally, this dataset allowed us to explore whether and how the mode of examination (face-to-face vs. online platform) during the pandemic influences its impact.

Our focus was directed to data spanning the period between January 2019 and October 2021. This specific time frame facilitated a comparative analysis, allowing us to discern any differences between “in-person” and “online” examinations, both in the period just before and during the pandemic. Additional details are offered in the Methods section.

The data were extracted from the multifunction academic platform of the University of Messina. These data consisted of 246,416 assessments (exams) provided by 829 examiners. The evaluation involved 1,846 teaching courses. The data were collected over three academic years, from 2019 to 2021, and involved a total of 32,123 students [originating from 135 bachelor's/master's degrees and post-graduate specializations offered by the University of Messina (see Table 1 )].

Table 1 . Number of assessments and respective percentages per type of degree course before and during the COVID-19 pandemic.

The pre-pandemic period refers to exams from January 2019 to February 2020. The pandemic period refers to exams from March 2020 to October 2021. We choose to include a relatively extended time window for the pandemic condition as two modalities of examination (i.e., in presence and online) were implemented in this period. In contrast, only one (in person) was available in the pre-pandemic condition. We excluded data referring to mixed mode (online/in person) assessments, as it was not possible to disentangle the two modalities. Refer to Tables 2 , 3 for more details.

Table 2 . Comparison between complete data and selection by exam type.

Table 3 . Comparison between complete data and selection by modality of the assessment.

The extracted data included the modality of the assessment session (online and in presence), the type of assessment (written and oral), and the respective outcome (passed or failed). Inclusion criteria for the final data analysis referred to only oral examinations. We excluded data referring to mixed mode (online/presence) assessments, as it was not possible to clearly disentangle the two modalities. We referred to rectoral decrees to determine when the exams were (or not) online or in the mixed mode. For privacy reasons, demographic data (e.g., age, sex, and country of origin) were not provided. The study was approved by the Local Ethics Committee (Protocol Number: COSPECS_08_2022). The ethics committee waived the requirement for consent as the study implied the analysis of already collected and anonymized data.

A typical oral exam session begins with verifying the student's identity. There is no standard way to conduct the exam. The assessor can start the session by asking the student to choose the topic from the general program of the course or by selecting the topic himself from those addressed in the course. The duration of the exam and the number of questions also vary depending on the assessor and the need to have a clear picture of the level of preparation of the student being examined. For data analysis we employed an approach to discern significant differences in pass rates between categories. Specifically, we utilized the prop.test() function in the R language, which conducts a hypothesis test to compare proportions. Internally, this function employs the chi-square test statistic for proportions. The version used for this analysis is R language ver. 4.2.

First, the overall number of assessments during the pandemic was higher ( N = 141.758) compared to the pre-pandemic period ( N = 104.658). However, when looking separately at each type of degree course ( Table 1 ), a reversed pattern of results (i.e., a lower number of assessments) is documented for the master's degree (5 years single cycle).

Table 4 provides the results of the statistical analysis when comparing the number of assessments before vs. during the pandemic for each type of degree course.

Table 4 . Statistical comparisons of the number of assessments as a function of assessment modalities (A = in presence before the pandemic; B = in presence during the pandemic; C = online during the pandemic) for each type of degree course.

Considering the whole sample, the observed absolute number of passed exams was 151.702 out of 246.416, resulting in an overall pass rate of 0.62 (61.6%). Furthermore, we noted an overall higher chance of a favorable assessment during the COVID-19 pandemic compared to the pre-pandemic period (0.63 vs. 0.60) ( Figure 1 and Table 5 ). However, a more mixed picture emerged when examining different assessment modalities. Specifically, during the COVID-19 pandemic, the chance for a favorable assessment was higher in the “online” modality (0.65) but notably lower in the “in presence” modality (0.58), compared to the pre-pandemic “in presence” assessments (0.60). This indicates that the pandemic negatively influenced academic performance, specifically when the assessment was conducted in the standard (i.e., in presence) setting. See Table 6 and Figure 2 .

Figure 1 . The figure shows the pass rates in the COVID-19 pandemic (Yes) and before (No).

Table 5 . Pass rate by the assessment modality: number of examinations, standard error (S.E.), and Z- test for equality of proportions.

Table 6 . Pass rate by modality of the assessment: number of examinations, the respective standard error (S.E.), and Z- test.

Figure 2 . The figure shows the pass rate by modality of the assessment.

To account for potential season-related effects, we performed a further control analysis comparing passing rates between three consecutive years (2019, 2020, 2021) considering the same seasons for each of the 3 years. We excluded the winter season from the analysis because it encompassed both pre-pandemic and pandemic data, in accordance with the university's rectoral decree, which established the examination modality (online, face-to-face) for the entire institution. We examined the influence of seasons, and assessment modality on the likelihood/chance of a favorable assessment.

The results (two-sided Z -tests) confirm the pattern observed in the primary analysis, indicating a reduced likelihood of a favorable assessment in face-to-face settings during the pandemic (58%, p = 0.007), and an increased likelihood of a favorable evaluation online (during the pandemic, 68%, p < 0.001) compared to the pre-pandemic—face-to-face—setting condition (59%). See Table 7 for details on the different seasons.

Table 7 . Pass rate by modality of the assessment after controlling for the season effect.

In this archival study we examined the impact of the COVID-19 pandemic on academic assessment outcomes, introducing several innovative elements compared to previous work in the field. Our approach combined direct empirical evidence about academic performance, a comprehensive archival analysis of large-scale data, and a comparison between face-to face and online assessments.

The first important finding is the significant difference of exam pass rates between face-to-face and online modalities. This has relevant practical implications for the landscape of academic assessment. In contrast to previous survey-based studies (e.g., Mahdy, 2020 ), our research, based on direct empirical evidence, demonstrates that the COVID-19 pandemic negatively affected academic assessments, specifically in a face-to-face setting. This is evident through a decreased pass rate in the “in person” assessments during the pandemic compared to the period before the outbreak. Crucially, this trend persists even when accounting for seasonal effects, and might be caused by an adverse impact of the COVID-19 pandemic on mental health ( Salehinejad et al., 2020 ; Craparo et al., 2022 ; La Rosa et al., 2022 ; Vicario et al., 2023 ) and cognitive skills ( Fiorenzato et al., 2021 ), which could have deleterious effects on academic performance.

In principle, the lower pass rate in “face-to-face” assessments during the COVID-19 pandemic may also be influenced negatively by attendance in online classes provided during the pandemic, which could have affected learning quality. However, we observed a higher pass rate for online assessments during the pandemic (but see discussion below), and evidence from other studies suggests that online platforms and other modalities for remote practices, such as clinical interventions ( D'Oliveira et al., 2022 ; Prato et al., 2022 ) and remote learning ( Al-Maroof et al., 2021 ) allow for effective outcomes. Therefore, although we do not dismiss the possibility that online lectures may have negatively affected learning in some students, our data and previous research ( Al-Maroof et al., 2021 ) argue against attributing a causal role to this factor. On the other hand, “face-to-face” exams might have triggered heightened social stress, originating from prolonged isolation, which restricts social interactions. This, in turn, could have impacted students' cognitive performance and assessors' decision-making processes in the assessment (e.g., Starcke and Brand, 2012 ).

Other potential stressors, such as using facial masks, may have further reduced pass rates by interfering with student performance. The discomfort associated with face masking (e.g., Lazzarino et al., 2020 ; Tornero-Aguilera and Clemente-Suárez, 2021 ) has been shown to compromise cognitive performance and interfere with the occupational duties of workers (e.g., Shenal et al., 2012 ), and prolonged mask use can cause bilateral headaches ( Ong et al., 2020 ). Face masks may compromise the positive effects of relational continuity ( Wong et al., 2013 ). Additionally, facial masking reduces the recognition of emotions, potentially impacting social functioning ( Grundmann et al., 2021 ).

The second major finding in this study is the higher pass rate observed in the online assessment condition during the pandemic, suggesting a potential advantage for students tested in this modality. Also, this outcome remains consistent even after accounting for seasonal effects, indicating that the utilization of online platforms for assessment could increase the likelihood of passing exams during the pandemic. It is important to note, however, that no data on online assessments conducted before the COVID-19 pandemic are available, making it challenging to determine whether the increased pass rate is solely due to the use of the online platform or reflects an interaction between this assessment modality and the unique circumstances of the pandemic.

The more favorable outcome in the online session could potentially be attributed to a reduction in social distress experienced by students. This hypothesis is supported by the study of Stowell and Bennett (2010) , indicating that students who typically experience high levels of test anxiety in a classroom setting report reduced test anxiety when taking exams online. This might reflect an effective capacity to implement successful coping strategies crucial for an effective adaptation to unexpected circumstances associated with the ongoing pandemics (e.g., Zhao et al., 2022 ).

However, it is noteworthy that approximately one-third of students perceive e-exams as more stressful than in-person exams ( Elsalem et al., 2020 ).

Additionally, it is essential to acknowledge that previous research has emphasized an increased likelihood of cheating in (online) exams when lacking proctoring mechanisms (as in this case) (e.g., Harmor and Lambrinos, 2008 ; see also Chiang et al., 2022 , for a recent systematic review of academic dishonesty in online learning environments). This underscores the potential risk of undeserved promotion associated with the use of telematic tools. However, it is important to recognize the relevance of these technologies in supporting the continuity of teaching and academic assessment during the challenging circumstances posed by the COVID-19 pandemic.

Our study significantly contributes to understanding how the COVID-19 pandemic has influenced academic assessment, shedding light on both challenges and opportunities associated with online platforms. We provide direct evidence of the adverse effects of the COVID-19 pandemic on academic performance when exams are conducted in person. Conversely, the observed higher pass rate in the online condition, compared to the in-person conditions both before and during the pandemic, suggests a potential drawback of this assessment modality. This includes an increased likelihood for students to consult notes and teaching material in the absence of a supervision system, and/or a higher inclination of assessors toward positive evaluations. However, it is important to note that this statement, which represents the main limitation of our work, remains unverified, as our study did not encompass the condition of online assessment before the COVID-19 pandemic for a comparative analysis with that during the pandemic. Moreover, other limitations pertain to the absence of control for additional variables that could have influenced the results, such as variations in learning styles, assessment methodologies, and socio-cultural factors.

Conclusions

In conclusion, our study extends the existing body of research (e.g., del Arco et al., 2021 ; Diotaiuti et al., 2021 ; Ramos-Pla et al., 2021 , 2022 ), underscoring the profound impact of the COVID-19 pandemic on academic assessments and the use of virtual classes. For face-to-face examinations, it documents a lower probability of passing an exam during the pandemic compared to pre-pandemic times. It also emphasizes disparities in pass rates between in-person and online assessments, indicating a higher likelihood of passing exams online compared to in-person. Potential factors that contribute to explaining these differences include the impact of the pandemic on students' mental wellbeing and/or the potential for academic dishonesty in online assessments. The discovery of a lower probability of passing exams during the pandemic compared to pre-pandemic times suggests educational institutions need to formulate resilient contingency plans, crucial for mitigating disruptions in academic assessments resulting from unforeseen events such as pandemics.

The finding that the use of online platforms for assessment may increase the likelihood of passing exams holds practical implications for assessment strategies. It unveils, among other considerations, the potential risk of overestimating student‘s knowledge of the subject matter, which needs to be addressed.

Data availability statement

The raw data supporting the conclusion of this article will be available by sending a formal request to COSPECS Department at ZGlwLmNvc3BlY3MmI3gwMDA0MDtwZWMudW5pbWUuaXQ= .

Ethics statement

The studies involving humans were approved by Local Ethics Committee, Cospecs Department, University of Messina. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants' legal guardians/next of kin because we used archival data to conduct our study.

Author contributions

CV: Conceptualization, Investigation, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. MM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. PP: Conceptualization, Resources, Supervision, Visualization, Writing – review & editing. CL: Conceptualization, Writing – review & editing. MN: Methodology, Supervision, Validation, Writing – review & editing. AA: Conceptualization, Supervision, Validation, Writing – original draft, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The University of Messina covered the publication expenses for this article through the APC initiative. AA was supported by Universidad Católica Del Maule (CDPDS2022).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: academic assessment, COVID-19 pandemic, online assessment, face-to-face assessment, archival study, archive statistical analysis

Citation: Vicario CM, Mucciardi M, Perconti P, Lucifora C, Nitsche MA and Avenanti A (2024) The impact of the COVID-19 pandemic on academic performance: a comparative analysis of face-to face and online assessment. Front. Psychol. 14:1299136. doi: 10.3389/fpsyg.2023.1299136

Received: 22 September 2023; Accepted: 18 December 2023; Published: 09 January 2024.

Reviewed by:

Copyright © 2024 Vicario, Mucciardi, Perconti, Lucifora, Nitsche and Avenanti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Carmelo Mario Vicario, Y3ZpY2FyaW8mI3gwMDA0MDt1bmltZS5pdA==

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Lessons From Early COVID-19: Associations With Undergraduate Students’ Academic Performance, Social Life, and Mental Health in the United States

Joseph p nano, mina h ghaly.

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Edited by: Olaf von dem Knesebeck , University Medical Center Hamburg-Eppendorf, Germany

Reviewed by: Jesus Alejandro Aldana Lopez , Instituto Jalisciense de Salud Mental, Mexico

*Correspondence: Joseph P. Nano, [email protected] ; Mina H. Ghaly, [email protected]

These authors share first authorship

This Original Article is part of the IJPH Special Issue “The Impact of the COVID-19 Pandemic on Mental Health”

Received 2022 Jan 28; Accepted 2022 Nov 28; Collection date 2022.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Objectives: This study aims to explore the influence of COVID-19 on undergraduate students’ academic performance, social life, and mental health during the pandemic’s early stage, and evaluate potential correlates of stress, anxiety, and depression in relation to COVID-19.

Methods: Participant data was collected as part of a survey that consisted of demographic questions, a DASS-21 questionnaire, and an open-ended question. The final sample consisted of 1077 full-time students in the United States.

Results: 19%, 20%, and 28% of participants met the cutoff for “severe” and “extremely severe” levels of stress, anxiety, and depression according to DASS-21. During COVID-19, a significant increase in hours of sleep, and decrease in hours spent on extracurriculars and studying were observed. While talking to family was significantly associated with stress, anxiety, and depression, engaging in hobbies was only associated with depression.

Conclusion: With the continued spread of COVID-19, it is critical for universities to adapt to the mental health needs of their students. Future institutional advancements should create treatment programs to ensure better academic and social outcomes.

Keywords: anxiety, mental health, COVID-19, depression, stress, DASS-21, undergraduate students

Introduction

In March 2020, the World Health Organization officially declared the rapidly spreading coronavirus outbreak a pandemic [ 1 ]. The pandemic has since reshaped almost every facet of modern society. Many schools and universities across the United States closed from March 2020 through the end of the Spring 2020 semester. Consequently, students living in university dormitories were required to return to tumultuous living conditions that likely detracted from learning. Educators were asked to revise, and in some cases completely revamp course standards, expectations, and assessments, all within a matter of weeks.

Despite limited evidence regarding the implications of transitioning to online learning in the context of COVID-19, previous studies have shown that the transition to postsecondary education is itself a source of anxiety [ 2 ], stress [ 3 ], and depression [ 2 , 4 ]. This transition can bring about feelings of worthlessness, appetite disturbances, and issues with concentration, all of which adversely affect students’ capability to perform well in demanding environments [ 5 ]. Moreover, during the undergraduate years, students are not only immersed in higher education but are also transitioning into other critical social roles [ 6 ]. Young adults in this age range, therefore, are forced to deal with identity exploration and adjustment to university life.

Academics are exceptionally fundamental to the life and health of undergraduate students. The amount of time spent studying as well as concerns about examinations have been shown to lead to heightened immune and stress responses [ 7 ]. Therefore, coping mechanisms and social support to reduce stress are crucial, as effective coping strategies can potentially ameliorate stress reactivity [ 8 ]. In particular, understanding how the learning experience was for undergraduate students is important as online learning will likely be the primary method of instruction during future university closures. Some research has been done to assess early pandemic-related responses associated with undergraduate students’ academic work [ 9 , 10 ] and mental health [ 11 ] in the United States.

Coinciding with the ever-demanding academic burden, mental health among undergraduate students represents an important and growing public health concern [ 12 ]. It was found that 12%–18% of college students suffer from a diagnosable mental illness [ 13 ]. A mental health crisis may take its toll years after the course of the COVID-19 pandemic [ 14 ]. Thus, it is important to investigate the potential factors that have adversely affected students during the pandemic.

Literature to-date is limited on commentary with regards to the effects of online learning on studying quality among undergraduates during the pandemic. The abrupt transition to online learning exploited time better spent in clinical training and internet subscription costs impeded access to effective learning for students studying at home, as did tending to family [ 15 ]. A poor internet connection was found to be a leading barrier to online learning [ 16 ].

Undergraduates are one of the most sleep-deprived age groups in the United States [ 17 ]. Studies that have investigated sleep reported significantly worsened insomnia among students during the pandemic [ 18 , 19 ]. Specifically, a large cross-sectional study found a marked incidence of insomnia among college-aged students during the lockdown period [ 20 ]. Deteriorating sleep quality correlated with depressive symptoms [ 21 ].

While previous studies have commented on particular dimensions of the pandemic relating to issues of depression and anxiety [ 22 ], there remains a pressing concern to determine precisely which aspects of students’ daily life had been affected. Thus, the aim of the present study was to analyze specific correlates of stress, anxiety, and depression among undergraduate students as they relate to the most intimate issues of the college student’s demanding lifestyle. In particular, we aimed to analyze data from a large sample of students using the Depression, Anxiety, and Stress Scale (DASS-21) to approximate emotional valence. Further questions offered to students in a survey were meant to gauge whether changes in hours spent on extracurricular activities, studying, and sleep before and during the pandemic were related to observed DASS-21 results. This study also aimed to provide a better understanding about COVID-19’s influence in the context of full-time undergraduate students’ academic performance, social life, and mental health in the United States.

Participants

Participants were undergraduate students at a private university, public university, or community college in the United States. To be included, participants had to be at least 18 years old and enrolled as full-time students as part of class years 2020, 2021, 2022, or 2023. Between April and June 2020, participants were recruited through two channels. First, an email was sent to undergraduate students at Boston College. Second, participants were recruited online via Reddit, a social news platform and online forum.

An invitation to participate in the study was posted on over 20 university-related and survey recruitment subreddits (a subreddit is an online community with user-created threads dedicated to a specific topic). One such subreddit utilized for recruitment in this study was r/SampleSize, a community of over 40,000 users assembled for the express purpose of survey recruitment and participation [ 23 ]. Subreddits such as these have been shown to be a good source of diverse and viable participants [ 23 ] and are useful for inexpensive participant recruitment and reliable data collection [ 24 , 25 ]. For the purpose of the current study, face-to-face interviews during this time had to be avoided because of the US lockdown and ongoing public health crisis. The study received 1,734 completed responses. After excluding students younger than 18 ( n = 34), part-time students ( n = 135), and students with incomplete responses ( n = 488) from our analyses, our final sample size was 1,077.

DASS (Depression, Anxiety, and Stress Scale): The online survey included the 21-item DASS-21 scale on mood and stress [ 26 ]. Based on the scores, participants were classified into “normal” (a score of 0–9 for depression, 0–7 for anxiety, and 0–14 for stress), “mild” (10–13, 8–9, 15–18), “moderate” (14–20, 10–14, 19–25), “severe” (21–27, 15–19, 26–33), and “extremely severe” (28+, 20+, 34+) categories. The purpose of these questions was to assess the severity of depression, anxiety, and stress during the COVID-19 pandemic. Previous studies have verified the validity of the DASS-21 scale as a routinely-used clinical and non-clinical self-report scale [ 27 , 28 ]. One sample statement that participants were to score for depression was “I was unable to become enthusiastic about anything”; one sample statement for anxiety was “I worry about situations in which I might panic and make a fool of myself”; and one sample statement for stress was “I tended to over-react to situations”. Participants were given the option to choose between “once a week or less” (score = 0), “2–3 times a week” (= 1), “4–6 times a week” (= 2) and “7 times a week or more” (= 3). The corresponding sum of scores was used to assess the severity of depression, anxiety, and stress. Cronbach’s α for the items in this test was 0.934, indicating excellent internal consistency in the questionnaire.

COVID-19 Evaluation: The first portion of the survey consisted of a series of questions meant to measure how COVID-19 may have affected students’ social life and mental health. Students were asked about hours of studying per day (“1–2 h,” “3–5 h,” “5–8 h,” and “8+ h”), hours spent on extracurricular activities per week (participants typed in number of hours), and hours of sleep received per night (“4 h or less,” “5–7 h,” “7–8 h,” and “8+ h”) before the pandemic (e.g., “ How many hours of sleep did you get per night (BEFORE the COVID-19 pandemic)? ”) and during the pandemic (e.g., “ How many hours of sleep do you [currently] get per night? ”).

Coping Strategies: Participants were asked to select up to three ways they managed their stress. Seven response categories were given: “talk to friends,” “talk to family members,” “home workout/indoor sports,” “meditate,” “do favorite hobbies,” “walk outside,” and “other”. When participants chose “other”, they were asked to specify. Participants were provided with an open-ended question at the end of the survey to further elaborate on experiences that were not captured by previous questions.

Before taking the survey, all participants provided informed consent. Participants were made aware of all risks and benefits associated with the survey, confidentiality, and right to withdraw their voluntary participation at any time. The survey, which took 10 min to complete, consisted of four components: social demographics, school adjustments, DASS-21 questions, and an optional open-ended question. As an incentive for participation, participants were entered into a raffle for a chance to win a $10 Amazon gift card (15 participants were awarded a gift card). The survey was accessible online for 9 weeks (from April to June 2020). The procedure was approved by the university’s institutional review board in April 2020, ensuring the protection of human subjects in this research in compliance with US federal law.

Statistical Analysis

Statistical analyses were conducted using R-Studio statistical software (version 1.3.959, 2009–2020 R-Studio, PBC). In the first phase of analysis, descriptive statistics were used to describe the demographics of the sample and the distribution of the three mental health outcomes among students. Next, t-tests were used to test whether moving to remote learning had an effect on stress, anxiety, and depression. Paired t-tests were used to determine differences in the hours of sleep, study, and extracurricular activities before and during the pandemic. Bivariate regression analysis was used to determine whether participants’ responses to moving classes to remote learning were associated with stress, anxiety, and depression. Regression analysis tested whether changes in the hours of sleep and study during COVID-19 were associated with stress, anxiety, and depression. Lastly, a multivariate OLS system was used to determine whether gender, having a family member who tested positive for COVID-19, number of times participants left their homes, school performance after moving to remote learning, and changes in the hours of sleep and studying were associated with stress, anxiety, and depression.

Descriptive Statistics of the Sample

Most participants were students at public universities (70.5%), followed by those from private universities (28.5%) or community colleges (0.7%). 0.3% of participants preferred not to answer this question. Participants were categorized as students in the class of 2020 (16.6%), 2021 (26.1%), 2022 (29.1%), and 2023 (24.4%), respectively. 3.8% of participants preferred not to answer this question ( Table 1 ). Female participants accounted for 51.4% of the sample. Slightly more than half of the participants were Caucasian (52.8%), one fourth were Asian/Pacific Islander (24.7%), about one tenth were Hispanic/Latinx (8.9%), and about 4% were African American (3.7%). 0.4% of participants self-identified as Native American. While the majority of participants were US students (94.3%), some participants were international students (4.9%) and a few participants preferred not to answer (0.8%).

Demographic characteristics of participants (United States. 2020).

Using DASS-21 scores, 19%, 20%, and 28% of participants were categorized as having “severe” or “extremely severe” levels of stress, anxiety, and depression, respectively (% “severe” + % “extremely severe”) ( Figure 1 ) ( Table 2 ). These results indicate an increase of “severe” and “extremely severe” levels of stress, anxiety, and depression in comparison to a sample baseline, non-pandemic DASS-21 scores, with levels of stress, anxiety, and depression at 11%, 15%, and 11%, respectively [ 29 ].

Proportion of participants whose answers on the Depression, Anxiety, and Stress Scale-21 indicated a normal, mild, moderate, severe, or extremely severe level of stress, anxiety, and depression (United States. 2020).

Stress, anxiety, and depression characteristics of participants (United States. 2020).

Bivariate Relationship Between Academic Performance, Social Life, and Mental Health

COVID-19 disrupted traditional classroom instruction and led to remote learning, as 69.3% of participants claimed that moving to remote learning had a negative impact on their school performance, while 30.7% of participants noted a positive impact of remote learning on school performance. Moreover, in terms of the association between school performance and mental health, a t-test showed that participants who claimed that remote learning had a negative impact on their school performance had significantly higher scores in stress ( Figure 2 , p < 0.001), anxiety ( Figure 2 , p < 0.001), and depression ( Figure 2 , p < 0.001), compared to peers who reported the opposite. A bivariate regression analysis further confirmed that students’ opinions about remote learning were significantly associated with stress ( p < 0.001), anxiety ( p < 0.001), and depression ( p < 0.001). Participants were also asked about changes in hours spent on extracurricular activities, studying, and sleeping before and during COVID-19 comparatively. A paired two-sample t-test showed a significant increase in the hours of sleep (before COVID-19: 6.7 h; during COVID-19: 7.7 h, p < 0.001), a significant decrease in hours spent on extracurricular activities (before COVID-19: 9.4 h; during COVID-19: 6.2 h, p < 0.001), and a significant decrease in hours spent studying (before COVID-19: 4.2 h; during COVID-19: 3.6 h, p < 0.001).

Association between stress, anxiety, and depression scores (mean Depression, Anxiety, and Stress Scale-21 score) and self-reported impact of COVID-19 on school performance (United States. 2020). Note: Error bars represent standard errors. Significance levels of Depression, Anxiety, and Stress Scale-21 scores: * p < 0.05, ** p < 0.01, *** p < 0.001.

Regression analysis was implemented to find correlates of stress, anxiety, and depression. For survey questions addressed in regard to pre-pandemic conditions, regression analysis showed that hours of sleep was associated with stress ( p < 0.001), anxiety ( p < 0.001), and depression ( p < 0.001), but hours of studying was not associated with stress ( p = 0.33), anxiety ( p = 0.213), and depression ( p = 0.056). However, during COVID-19, regression analysis showed that neither hours of studying nor hours of sleep were associated with stress ( p = 0.429 and p = 0.678), anxiety ( p = 0.283 and p = 0.506), and depression ( p = 0.0517 and p = 0.665).

Multivariate OLS Regression Models

Turning to multivariate OLS regression models, several factors were found to have significant associations with stress and anxiety. Being a male participant ( p < 0.001), having a family member who tested positive ( p < 0.001), leaving home three times a week ( p < 0.05), believing that school performance was affected negatively by moving to remote learning ( Figure 2 , p < 0.001), and hours of sleep during COVID-19 (“5–6 h” p < 0.05, “7–8 h” p < 0.001, “8+ h” p < 0.001) were significant correlates of stress ( Table 3 ). As for depression, being a male participant ( p < 0.01), leaving home once a week ( p < 0.05), believing that school performance was affected negatively by moving to remote learning ( p < 0.001), hours spent studying (“3–5 h” p < 0.001, “5–8 h” p < 0.05), and hours of sleep during COVID-19 (“5–6 h” p < 0.01, “7–8 h” p < 0.001, “8+ h” p < 0.001) were significant correlates. For stress management, talking to a family member was significantly associated with stress ( p < 0.05), anxiety ( p < 0.05), and depression ( p < 0.05). Engaging in favorite hobbies was only correlated with depression ( p < 0.05).

Multivariate Ordinary Least Squares Models for variables indicative of stress, anxiety, and depression (United States. 2020).

Note : * p < 0.05, ** p < 0.01, *** p < 0.001.

Sex Differences

Female participants showed a larger percentage of severe and extremely severe levels of stress (severe; 16% and extremely severe; 7%), anxiety (8%; 15%), and depression (12%; 18%) in comparison to male participants’ stress (severe; 11% and extremely severe 3%), anxiety (5%; 10%), and depression (11%; 14%) levels ( Figure 3 ). Chi-squared analysis showed being female was associated with severe and extremely severe levels of stress (x-squared = 30.497, df = 4, p < 0.001), anxiety (x-squared = 19.501, df = 4, p < 0.001), and depression (x-squared = 17.59, df = 4, p < 0.01). Two-sample t-test showed that female participants have a higher score in stress ( p < 0.001), anxiety ( p < 0.001), and depression ( p < 0.001) in comparison to male participants. Regression analysis showed that gender is associated with stress ( p < 0.001), anxiety ( p < 0.001), and depression ( p < 0.001).

Mean Depression, Anxiety, and Stress Scale-21 scores of stress, anxiety, and depression among males and females (United States. 2020). Note: Error bars represent standard errors. Significance levels of Depression, Anxiety, and Stress Scale-21 scores: * p < 0.05, ** p < 0.01, *** p < 0.001.

Stress Management

Recall that the survey asked participants to choose up to three ways they managed their stress during the COVID-19 pandemic. Almost half (47.9%) of participants reported that they talked to family members, 76% of participants engaged in their favorite hobbies, and 32.2% of participants walked outside as one of their three choices. Although the vast majority of the open-ended responses included some sentiment of stress, many individuals did have positive experiences to share. Some participants noted that conversing with significant others, in addition to immediate family, was cathartic (“I’ve been [...]reach [ing] out to my boyfriend, at the very least. These things, along with the positive experience of being around a happy family and the safety of our own home, has positively contributed to my mental wellbeing.”). In a simpler sense, some have made the most of their time while at home, with one participant noting that they were “Just staying inside and trying to learn new things.”

Stress During COVID-19

In the open-ended question, one participant from the class of 2021 said the following of their experience during lockdown: “I have been feeling quite depressed. I feel like I have no control over my life ... I cannot plan for the future and my extracurriculars that were going to help me prepare for grad schools have been affected.” Another participant from the class of 2021 said, “Anxious and overwhelmed … I feel like I have less access to academic advising because … professors have not answered my emails. It’s been difficult to focus at home because my parents [are] working over the phone ….”

Many individuals expressed a lack of motivation to perform well on academic tasks noting severe procrastination, loss of direction, and overall dissatisfaction with the progression of the semester. One participant said, “I feel … grateful that I am healthy. However, I also have struggled to find any motivation to do my work or be active. Usually I can get things done because I look forward to having fun or relaxing on weekends, but now it is harder to get things done when it feels like that is all I am doing with nothing fun to look forward to.” Other participants found it challenging to engage with online classes. For example, one participant said, “Online learning is difficult because I feel zero engagement.”

One participant said that they felt suffocated. They said, “I want to leave the house, see new people, go to stores, but I only leave my house about 1–2 times a week.” In addition, leaving home a few times a week may have given students the opportunity to distance themselves from their families and home environment. One participant said, “My parents and I argue, and I feel like my mental health issues are having a negative effect on my family.” Another participant talked about the challenge of staying at home with family, saying, “Being at home with my family has taken a toll on my mental health. [We] do not have a great relationship, and being stuck at home has exacerbated our problems … ” One participant claimed that living temporarily away from family made them “feel great.”

This study looked for correlates of stress, anxiety, and depression during COVID-19. Those who reported that school performance was affected negatively by moving to remote learning exhibited significantly heightened levels of stress, as predicted by previous findings [ 30 , 31 ]. The classroom environment has several advantages over online learning. Teachers, for instance, are able to receive immediate feedback on students’ understanding of key concepts [ 32 ]. Furthermore, with remote learning comes some disadvantages; problematic internet use, longer screen time, isolation, and academic pressure are all associated with psychological distress among college students [ 33 – 36 ].

Our results specifically showed that the frequency with which participants left their homes was significantly associated with stress and depression. Participants who left their homes every day had lower DASS-21 scores of stress, anxiety, and depression. Social relationships have been shown to give individuals a sense of purpose and greater appreciation for life, leading to overall reduced stress and bolstered mental health [ 37 – 39 ].

We have identified unique correlates of stress as they relate to the COVID-19 pandemic. In particular, both sleeping hours and having a family member who tested positive for COVID-19 were correlates of stress. Changes in sleep and physical activity during the pandemic were associated with symptoms of high stress [ 40 , 41 ]. However, while previous studies have shown that patients with suspected COVID-19 (positive COVID-19 test result) demonstrated a significant reluctance to work [ 42 ], our study is among the first to identify the contribution of a family member’s positive test result.

This study shows a larger percentage of severe and extremely severe levels of stress, anxiety, and depression among female participants. In agreement with our results, previous studies established that the prevalence of anxiety was higher in women during the COVID-19 pandemic [ 43 – 46 ]. It has also been demonstrated that female students express greater concern for their future careers than do male students [ 47 ]. The dissimilarity in the levels of stress, anxiety, and depression between genders may be attributable to women seeking mental health consultation more often than men [ 48 ]. Being female was generally shown to be associated with a prominent increase in mental health problems during the pandemic [ 49 ].

This study found a significant discrepancy in hours devoted to sleep, extracurricular activities, and studying before and during the pandemic. The increase in hours of sleep could be due to heightened stress and anxiety about the pandemic during lockdown. The lockdown period had a negative impact on mental health by increasing post-traumatic stress symptoms and was associated with irregular sleep patterns [ 50 – 52 ]. Acute and chronic stress have been shown to perturb sleep differentially [ 53 ]. The observed decrease in hours spent on extracurricular activities during COVID-19 could be due to a fear of infection, as fear was a definite contributor to a reduction in pursuing activities and to an increase in anxiety during the pandemic [ 54 , 55 ]. As a whole, engagement in regular routines was also found to lower anxiety irrespective of the kind of stressor one was exposed to [ 56 ]. Therefore, one might be able to surmise that a lack of pursuing such activities may lead to greater anxiety. In discussing the motivation to pursue meaningful work, we found that students spent less hours studying when most instruction was conducted online. Although a decrease in hours of studying could be due to changing class syllabi and adjustments to the home environment, hours of studying did not correlate with stress, anxiety, or depression. It could be that students did not feel obligated to study in an environment with less structure or in the midst of a pandemic where students took on more responsibilities at home, such as caring for siblings or supporting their own children [ 57 , 58 ].

Future research should investigate current methods of mental health management for undergraduate students. The rise of telehealth and online counseling in the age of COVID-19 has provided greater opportunities for students to schedule appointments with a healthcare provider to manage mental health. Future studies might be able to explore the influence of telehealth on DASS-21 scores. Furthermore, it would be important to conduct follow up studies that investigate the impact of increased sleep on stress, anxiety, and depression during COVID-19. The adverse impact of the home environment should also be studied, specifically seeking to answer why students who left their homes less frequently experienced worsened mental health via higher DASS-21 scores (despite not dealing with the stresses of a daily commute, for instance).

Limitations

Because the survey was distributed during the lockdown period, an online convenience sampling method had to have been utilized. This sampling method limited the representativeness and generalizability of the findings reported, as we were necessarily constrained to only those responses from students with access to the Internet. Thus, it is not possible to draw causal inferences due to the nature of this study. The survey also had some duplicate questions and questions that did not provide the capability to select multiple options. For example, one question asked participants about COVID-19 safety precautions taken, where the selection of multiple options could have been appropriate. The term “extracurricular” was also left to the interpretation of participants. A better survey question could have offered participants the option to define extracurricular activities. Furthermore, in addition to DASS-21, the study could have benefited from the utilization of a resilience scale to examine how certain resilience factors could have protected individuals’ mental health from COVID-19 related stress. Allowing participants to evaluate resilience may have permitted greater insight into students’ mental health and wellbeing.

COVID-19 remains a credible threat to undergraduate students, beyond the acute and lingering physical effects of the virus. Students spent less time studying, complemented by the finding that the transition to remote learning hindered the majority of students’ academic experience. Students who left home more frequently may have had a greater opportunity to socialize, which lessened the stress and mental burden of lockdown. The majority of participants also stated that talking to family and engaging in favorite hobbies were beneficial for stress management. As such, these results showed that the pandemic led to significant changes in students’ academic performance, social life, and mental health.

Ethics Statement

The studies involving human participants were reviewed and approved by The Boston College Institutional Review Board (IRB) and Vice Provost for Research. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

JN, MG, and WF contributed to the design, execution, and conceptualization of the study. JN and MG contributed to data management and conducted the literature search. MG contributed to the sample size methodology and carried out the survey distribution and initial data analysis. JN contributed to key elements of statistical analyses and interpretation with additional tests and further interpretation suggested by MG and WF. MG contributed to the essential prose of the manuscript with input from JN and WF. MG and JN prepared the initial draft of the manuscript. MG and JN created the figures and tables with input and suggestions from WF on appropriate edits to be made. All authors approved the final version of the manuscript for submission.

Conflict of Interest

The authors declare that they do not have any conflicts of interest.

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COVID-19, college academic performance, and the flexible grading policy: A longitudinal analysis ☆

Núria rodríguez-planas.

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Queens College - CUNY, Economics Department, Powdermaker Hall, 65-30 Kissena Blvd., Queens, New York 11367, USA.

Received 2021 Feb 8; Revised 2022 Jan 7; Accepted 2022 Jan 19; Issue date 2022 Mar.

Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

I use an unbalanced panel of over 11,000 academic records spanning from Spring 2017 to Spring 2020 to identify the difference in effects of the COVID-19 pandemic across lower- and higher-income students’ academic performance. Using difference-in-differences models and event study analyses with individual fixed effects, I find a differential effect by students’ pre-COVID-19 academic performance. Lower-income students in the bottom quartile of the Fall 2019 cumulative GPA distribution outperformed their higher-income peers with a 9% higher Spring 2020 GPA. This differential is fully explained by students’ use of the flexible grading policy with lower-income ones being 35% more likely to exercise the pass/fail option than their counterparts. While no such GPA advantage is observed among top-performing lower-income students, in the absence of the flexible grading policy these students would have seen their GPA decrease by 5% relative to their counterfactual pre-pandemic mean. I find suggestive evidence that this lower performance may be driven by lower-income top-performing students experiencing greater challenges with online learning. These students also reported a higher use of incompletes than their higher-income peers and being more concerned about maintaining (merit-based) financial aid.

Keywords: COVID-19, Income and performance inequalities, Unbalanced panel of academic records, Transcript and survey data, Difference-in-differences models, And event analysis

1. Introduction

There is mounting evidence that the COVID-19 pandemic, with its subsequent closing of schools and campuses and move to online teaching, may be widening socio-economic educational gaps ( Andrew et al., 2020 , Aucejo et al., 2020 , Engzell et al., 2021 , Maldonado and De Witte, 2020 , Rodríguez-Planas, 2022 , Chetty Raj et al., 2020 , Sass and Goldring, 2021 ). The digital divide and uneven access to online learning resources is one mechanism underlying the greater learning losses among lower-income students ( Barnum and Bryan, 2020 , Bacher-Hicks et al., 2021 , Altindag et al., 2021 ). Other factors contributing to the learning delay include disadvantaged students’ lower access to physical learning space and conducive learning environment ( Andrew et al., 2020 ), and higher stress and anxiety due to greater uncertainty and disruptions ( Aucejo et al., 2020 , Barnum and Bryan, 2020 , Rodríguez-Planas, 2022 , Jaeger et al., 2021 ).

At the same time, because online learning requires more discipline and self-regulated learning than traditional in-person learning, it would be reasonable to expect the educational gap between low- and high-performing students to widen if basic skills are necessary to acquire additional skills ( Cunha and Heckman, 2007 ). Using pre-COVID-19 estimates of learning losses from extended absences from school (i.e., summer months and natural disasters), Kuhfeld et al. (2020) project substantial learning losses from the COVID-19 pandemic, especially for low-performing students. Using data from primary schools in Germany, Grenewig et al. (2020) find that, after COVID-19 school closures, higher-performing students spent more time on school-related activities daily than their lower-performing peers. Kofoed et al. (2021) find that West Point students randomized in an online version of a “Principles of Economics” course in Fall 2020 underperformed in assignments and exams relative to those in the in-person sessions. Using data from Virginia community college system, Bird et al. (2020) find a decrease in course completion in the courses students started in-person in Spring 2020 as compared to those they started online. Both studies find that the losses were greater among the low-performing students.

Despite this evidence, little is known on how the pandemic affected college students’ academic performance during the Spring 2020 and whether it had any differential effect on lower-income students’ academic performance relative to that of their higher-income peers. This is the main objective of this paper. This study provides novel evidence on the differential effects of the pandemic on Spring 2020 GPA and credits taken, earned, and failed by students’ pre-pandemic income among students enrolled in Queens College. To do so, it uses an unbalanced panel of over 11,000 academic records spanning from Spring 2017 to Spring 2020. The study further identifies the mechanisms driving its main findings by analyzing students’ Spring 2020 transcripts as well as their responses to a rich online survey.

Queens College (QC) is a four-year college in the City University of New York (CUNY) system. Considered one of the most affordable colleges in the country—with a median undergraduate tuition of $6,530—, QC is an urban college with a socially vulnerable and ethnically diverse student population located in the borough of Queens in New York City. The identification strategy relies on both difference-in-differences models and event study analyses. In addition, to control for time-invariant unobserved heterogeneity, I exploit within-student variation by controlling for student fixed effects.

During Spring 2020, higher-income students earned a 13.4% higher Spring 2020 GPA relative to their pre-pandemic mean. In contrast with students’ academic expectations for Spring 2020 ( Aucejo et al., 2020 , Rodríguez-Planas, 2022 ), lower-income students outperformed their wealthier peers as they earned a 5.1% higher GPA. However, average effects hide important differences by pre-COVID-19 academic performance.

Lower-income students in the bottom quartile of the 2019 Fall cumulative GPA distribution outperformed their higher-income counterparts with a 9% higher Spring 2020 GPA relative to the counterfactual’s pre-pandemic mean. This higher performance is strongly associated with the flexible grading policy as it vanishes when I use the GPA students would have earned in the absence of the flexible grading policy. Indeed, transcript data reveal that lower-income students from the bottom quartile were 35% more likely to exercise the pass/fail option than their higher-income counterparts. I further find suggestive evidence that greater concerns with maintaining financial aid among the bottom-performing lower-income students (relative to their peers) may have driven their differential prevalence of pass/fail grade.

While no such GPA advantage is observed among top-performing lower-income students, in the absence of the flexible grading policy these students would have underperformed relative to their higher-income counterparts as their GPA would have been 4% lower (relative to the pre-pandemic mean for the comparison group of 3.718). To put it differently, in the absence of flexible grading, lower-income top-performing student would have seen their GPA decrease by 5% relative to their counterfactual pre-pandemic mean. I find suggestive evidence that this lower performance may be driven by lower-income top-performing students experiencing greater challenges with online learning than their wealthier peers. These students also reported a higher use of incompletes and being more concerned about maintaining (merit-based) financial aid. Transcript data also reveal that they were 57% less likely to exercise the pass/fail option than bottom-performing lower-income students relative to the differential observed between their wealthier counterparts.

To the best of my knowledge, this paper is the first to use higher-education administrative records and transcript data to study the short-run effects of the pandemic on college students’ academic performance using a student fixed effects model. This work relates to at least the following two strands of literature. First, it is close to studies analyzing the effect of violent conflicts or natural disasters on students’ academic performance ( Brück et al., 2019 , Sacerdote, 2012 ) or of economic recessions on students’ earnings after graduation ( Oreopoulos et al., 2012 , Fernández-Kranz, 2018 ). While these studies focus on the effects of events long after they occurred, I study the immediate effects of the event.

Second, the current paper contributes to a nascent literature analyzing the consequences of the COVID-19 pandemic on college education. In contrast with Aucejo et al. (2020) and Rodríguez-Planas ( forthcoming ), which focus on students’ self-perceived challenges, I find that the immediate effects of the COVID-19 pandemic on academic performance are not only positive but seem to benefit most lower-performing lower-income students. While Kofoed et al. (2021) study the impact of switching from in-person to online learning on grades during the pandemic, their analysis is confined to an Economics course during Fall 2020 with no temporal analysis. Bird et al. (2020) and Altindag et al. (2021) also estimate the effect of the pandemic on students’ academic performance due to the unexpected switch to online learning during Spring 2020. However, both papers focus on universities with well-established online education programs, and their identification strategy relies on comparing academic performance of students who took courses in both types of instruction modes before and after the pandemic. Because online teaching was practically non-existent prior to the pandemic at QC, estimates in the current study pick up both the switch to online teaching and other disruptions caused by the pandemic. By merging administrative records with transcript data and students’ responses to a survey on COVID-19 challenges, I document differential effects of the pandemic given students’ pre-pandemic income and pre-pandemic academic performance more broadly. Importantly, my analysis reveals students’ differential use of the flexible grading policy based on their financial and academic needs.

The data in this paper come from three different sources. I merged individual administrative academic records from QC with survey data on students’ challenges collected during the early months of the pandemic. To better understand students’ use of the flexible grading policy, I also used transcripts level data.

Most of the analysis focuses on an unbalanced panel of 11,443 academic records from 2,817 students spanning from Spring 2017 or later (if the student enrolled in QC at a later date) to Spring 2020. 2 For each semester, I observe students’ semester GPA; credits taken, which do not include courses officially withdrawn as they do not affect the GPA; credits earned; and credits for which a failing grade was earned. In regular years, the latter include credits from: (1) courses unofficially withdrawn (where the student stopped attending and never withdrew); (2) courses with an F grade; and (3) courses with a grade of Failed Incomplete, which is the grade assigned when an incomplete is not resolved by the following semester. In Spring 2020, this category also includes credits for courses with a grade of No Credit (NC). Other information available in QC administrative records includes students’ sex, age, race, and ethnicity. In addition, I also observe the following information collected at the beginning of Spring 2020: students’ major, class level (indicating whether the student is a freshman, sophomore, junior, senior, or in graduate school), Fall 2019 cumulative GPA, part-time student status, and whether the student had ever received the federal Pell grant. I use information on ever Pell grant receipt to define lower-income students because most Pell grant money goes to students with a total family income below $20,000.

In addition, Spring 2020 transcript data provide information on whether the student exercised the pass/fail option (no-letter grade), took an incomplete, or simply removed the class record after the end of the semester (the NC grade). I use such information to estimate both the prevalence and intensity of such options during the Spring 2020. Transcript data also inform us on whether the student withdrew a course officially or unofficially. If the student officially withdrew the course, she did so within the first nine weeks of the semester, and her GPA was not affected. If the student never withdrew the course but stopped attending, it is considered as an unofficially withdraw, and the student received a failing grade. I use such information to estimate both the prevalence and intensity of courses withdrawn officially and unofficially. Importantly, since transcript data also include the initial grade received before changes were made, and the credits each course was worth, I use such information to calculate the students’ Spring 2020 GPA in the absence of the flexible grading policy.

To further identify potential mechanisms behind these findings, I use the students’ responses to an online survey on their experiences during Spring 2020. The survey was sent to all students enrolled in Spring 2020, and it was fielded between Friday, July 24th and Friday, September 18th 2020. The response rate of 23% is higher than the usual response rate on CUNY online surveys of 13%, and the response rate of 10% to 12% obtained around the same time in 28 universities ( Jaeger et al., 2021 ). The sample in the current paper is almost twice as large as that of other post-COVID-19 higher-education survey studies ( Aucejo et al., 2020 ).

As seen in Table 1 , I observe small differences between students in my sample (columns 1 and 2) and the overall QC student population (column 4) in the racial/ethnic distribution, the share of part-time students, the distribution of majors 3 , and the share of Pell recipients. For example, the share of students who ever received the Pell grant in my sample is 57% if only undergraduate students are considered, which is not far from the 55% observed at the college level.

Descriptive statistics.

Note : Lower-income students are those who ever received the Pell grant based on QC administrative records.

Standard errors are reported in parentheses in column 3. Column 3 presents the coefficient on the low-income dummy from a regression model with no other controls.

Significant at the: ***1 percent level, ** 5 percent level, *10 percent level.

Source: https://www.qc.cuny.edu/about/research/Pages/CP-Enrolled%20Student%20Profile.aspx .

Excludes graduate students.

There is a higher share of females (68% versus 57%) and older-than-25-years-old students (35% versus 29%) than the overall QC population, and a lower share of US born (44% versus 68%), English second-language learners (ESL) learners (22% versus 36%), and transfer students (22% versus 55%). The lower rate of transfer students reflects the lower engagement of these students to regular college life as they frequently combine college with either part-time or full-time employment.

CUNY is known to be an institution that educates some of the poorest students in the country. It is also known to have a very diverse student population. Hence, it is not surprising that QC students are more racially diverse than students from the largest public university in each state. For example, only 27% of QC students are non-minority students compared to an average of 61% of non-minority students in the largest public universities in each state ( Aucejo et al., 2020 ).

Comparing columns 1 and 2 reveals that lower-income students (defined as those who ever received a Pell grant) are more likely to be Asians or Hispanics than higher-income students. They are also more likely to be first-generation college students, transfer students, and ESL students, and less likely to be US born than higher-income students. They also have a lower Fall 2019 cumulative GPA. Given the Pell-grant requirements, lower-income students are younger, and less likely to be graduate students or study part-time than higher-income students.

3. Statistical methods

To estimate the differential effect of the COVID-19 pandemic on lower-income students’ academic performance, I estimate the following difference-in-differences model with individual fixed effects:

where Y ist is the outcome of interest (for example, semester GPA) for student i in semester s and year t . Spring2020 s is a dummy equal 1 if the academic record is for Spring 2020 and 0 prior to that . Low-Income i is equal to 1 if student i ever received the Pell grant and 0 if the student never received the Pell grant. φ i represents the individual fixed effects, Fall s is a dummy equal 1 if the academic record is for the Fall semester and 0 if it is for the Spring semester, and Year t represents the year fixed effects. Standard errors are clustered at the student level.

The coefficient of interest, β 2 , captures the differential post-pandemic effect on the outcome, Y ist , for lower-income students relative to their higher-income peers. Note that the individual fixed effects, φ i , absorb the lower-income indicator (as well as all the other time-invariant observable and unobservable characteristics). The coefficient β 1 captures how the academic performance of higher-income students changed in Spring 2020 when the COVID-19 pandemic hit. As identification comes from comparing outcomes from the same student before and after the pandemic, there is no need to control for time-invariant observable characteristics. The Fall semester dummy controls for semester-specific characteristics, and the year fixed effects control for year differences over time.

The critical identifying assumption is that there are parallel trends in the outcome variable across both groups (lower- versus higher-income students). To assess the validity of this assumption, I check for pre-existing diverging trends using the following event-study framework:

where S sj is a dummy which takes value 1 if the outcome is observed in j th semester before ( -j ) or after ( +j ) January 27th 2020, which is when Spring 2020 began, and 0 otherwise. Fall 2019 dummy is the omitted semester. In the absence of any pre-existing differential pre-trends between lower- and higher-income students, the estimated coefficients ξ j corresponding to the semesters prior to the Spring 2020 would not be statistically different from zero.

Because average effects may hide differences by students’ pre-COVID-19 academic performance, I also estimate the differential effect for students in the different quartiles of the 2019 cumulative GPA distribution using the following difference-in-differences model with individual fixed effects:

where Q iq F a l l 2019 is a dummy variable which takes the value 1 if the Fall 2019 cumulative GPA of student i is in the q th quartile—the reference category is the first quartile. All the other covariates have been previously defined. In this specification, the individual fixed effects, φ i , absorb the lower-income indicator, the quartile indicators, Q iq F a l l 2019 , and the interaction of the low-income indicator with the quartile indicators, ∑ q = 2 4 α q ( L o w I n c o m e i ∗ Q iq F a l l 2019 ) , as well as all the other time-invariant observable and unobservable characteristics.

The coefficient β 1 captures the change in outcome during Spring 2020 experienced by higher-income students in the bottom quartile. The sum of β 1 and γ q , ( β 1 + γ q ) , captures the change in outcome during Spring 2020 experienced by higher-income students in the q th quartile. And the coefficient γ q measures the size of the differential effect in the change in outcome before and after January 27th 2020 for higher-income students in q th quartile relative to similar students in the bottom quartile.

To identify changes by income level, the coefficient β 2 captures the differential effect in the change in outcome after the pandemic between lower- and higher-income students in the bottom quartile. The sum of β 2 and δ q , ( β 2 + δ q ) , captures the differential effect in the change in outcome after the pandemic between lower- and higher-income students in q th quartile. The coefficient δ q measures the differential effect in the change in outcome post-pandemic between lower- and higher-income students in q th quartile relative to that experienced between lower- and higher-income students in the bottom quartile. At the bottom of Panel B of Table 2 , I display ( β 1 + γ q ) and ( β 2 + δ q ) for the top three quartiles ( q  = 2, 3, and 4). Because Eq. (3) is an individual fixed effects model, time-invariant individual unobserved heterogeneity is held constant.

Academic performance during Spring 2020 by low-income status and pre-pandemic academic performance, individual fixed-effects estimates, outcomes from academic records.

Notes : Spring 2020 is a dummy equal 1 if the outcome is measured during Spring semester 2020, and 0 otherwise. Low Income is a dummy equal 1 if the student ever received the Pell grant, and 0 otherwise. Q1 is a bottom quartile dummy equal to 1 if the student’s cumulative GPA is below 2.64 and equal 0 otherwise. Q2 is a second quartile dummy equal to 1 if the student’s cumulative GPA ranges between 2.64 and 3.23, both included, and equal 0 otherwise. Q3 is a third quartile dummy equal to 1 if the student’s cumulative GPA ranges between 3.24 and 3.71, both included, and equal 0 otherwise. Q4 is a third quartile dummy equal to 1 if the student’s cumulative GPA is higher than 3.71, and equal 0 otherwise. Panel A reports individual fixed-effects estimates associated with post-pandemic and low- versus high-income students on the dependent variables indicated in column headings. I estimate Eq. (1) described in the main text. Panel B reports individual fixed-effects estimates associated with post-pandemic and low- versus high-income students from different cumulative Fall 2019 GPA quartiles on the dependent variables indicated in column headings. I estimate Eq. (3) described in the main text. Standard errors clustered at the individual level are reported in parentheses. All regressions include a Fall-semester indicator and year 2017 and year 2018 dummies.

*, *, ** , *** Estimate significantly different from zero at the 0.1 level, 0.05 level, or 0.01 level.

Finally, when analyzing pre-pandemic income and academic performance heterogeneity effects with transcript or survey data, which is only available for Spring 2020, I estimate the following equation using ordinary least squares (OLS):

where Y i S p r i n g 2020 is the student’s i outcome during Spring 2020. X i 0 is a vector of for all the baseline characteristics listed in Table 1 and students’ major. Importantly, I control for students’ pre-pandemic academic performance with the Fall 2019 semester GPA. The coefficient α 1 captures the association between being a lower-income student in the bottom quartile and the students’ outcome relative to their higher-income counterfactual. Similarly, (α 1  +   ρ q ) captures the association between being a lower-income student in the q th quartile and the students’ outcome relative to their higher-income peers. The coefficient ρ q measures the differential effect in the in Spring 2020 outcome between lower- and higher-income students in q th quartile relative to that experienced between lower- and higher-income students in the bottom quartile.

4. Average effects by pre-pandemic income status

Higher-income students’ academic performance improved in Spring 2020 as students took and earned more courses than before the pandemic. They also got higher grades. As seen in Panel A of Table 2 , the effect of the pandemic on higher-income students’ academic performance shows a 13.4% increase in semester GPA, a 4% increase in credits taken, and a 5% increase in credits earned during Spring 2020 relative to pre-pandemic means. All three effects are statistically significant at the 1% level.

It is plausible that the pandemic affected the number of credits taken because these do not include any courses officially withdrawn within the first nine weeks of the semester (that is, by March 30th 2020). While the flexible grading policy was effective on April 1st 2020, discussions on its content occurred during most of March and both faculty and students were aware of the policy’s consequences for students’ GPA. Hence, the Spring 2020 increase in credits taken is likely due to students not officially withdrawing courses by March 30th because they already knew that they would be able to exercise the NC grade without penalizing their GPA. In contrast, the flexible grading policy could not affect credits earned as the student is not awarded credit with the NC grade. Instead, the increase in credits earned can only be explained by an increase in passing versus failing grades during the Spring 2020.

The increase in GPA is also likely related to the fact that as many as 17.2% of higher-income students exercised the pass/fail option, 6.3% exercised the NC grade, and 5% exercised the incomplete option. Comparing the official GPA increase before and after the pandemic with the GPA increase students would have experienced in the absence of the flexible grading policy (columns 1 and 5, Panel A in Table 2 ), we observe that the GPA increase ended up being 5.9% higher relative to the average pre-pandemic GPA of 3.014 because of the policy. 4 To put it differently, the flexible grading policy is responsible for 43.7% of the Spring 2020 GPA increase, ( 0.405 - 0.228 ) 0.405 = 0.437 % . At the same time, it is important to underscore that, even in the absence of the flexible grading policy, the Spring 2020 GPA increased by 7.6% among higher-income students relative to the pre-pandemic mean suggesting that other factors beyond flexible grading may be at play here.

Moving to the differential post-pandemic effect on academic performance across lower- and higher-income students, lower-income students outperformed higher-income ones during Spring 2020 as they earned a 5.1% higher GPA and they failed 28% fewer credits (or exercised fewer NC grades) than their wealthier counterparts. Both effects are statistically significant at the 5% level or lower. Lower-income students’ higher relative academic performance during the first semester of the pandemic is largely associated with the flexible grading policy as the GPA differential by income status vanishes ( β 2 is close to zero and not statistically significant) when I estimate the effect of the pandemic with the GPA prior to students exercising the flexible grading option.

As seen in Panels A and D of Fig. 1 , after five semesters of pre-pandemic parallel trends, we observe an increase in lower-income students’ official GPA and a decrease in credits failed in the Spring 2020 relative to their counterparts. While in Panel B, lower-income students attempted less credits in Spring 2020 than their wealthier counterparts, they also did so in earlier semesters. In Panel C, the relative increase in credits earned between lower- and higher-income students is small and not precisely estimated. As discussed earlier, in the absence of the flexible grading policy, there is no differential effect in the semester GPA post-pandemic between lower- and higher-income students (as seen in Panel E).

Semester GPA and Credits Differential by Income Level. Notes : These figures plot the coefficients on the interaction between the semester dummies and the low-income dummy (and the 95% confidence intervals) from an individual fixed-effects model as in Eq. (2) in the main text. The Fall 2019 semester dummy is the omitted semester. Standard errors are clustered at the individual level. In Panel A, the outcome is the official semester GPA. In Panel B and C, the outcome is credits taken and credits earned, respectively. In Panel D, the outcome is credits failed or NC earned. In Panel E, the outcome is the official semester GPA except for the Spring 2020, in which case I have replaced the official GPA with the GPA the student would have earned had the flexible grading policy not been available. I estimated this GPA using the courses the student enrolled in, and the grades originally earned in those courses (including official and unofficial withdraws) from transcript data.

5. Differential effects by pre-pandemic income status and academic performance

As seen in Panel B of Table 2 , the higher relative average performance among lower-income students is solely driven by students in the bottom quartile of the distribution. These students’ Spring 2020 GPA increased an additional 9% relative to their higher-income counterparts’ pre-pandemic average. This differential effect is marginally statistically significant at the 10% level. Adding the overall post-pandemic effect, ( β 1 + β 2 ) from Eq. (3) results in lower-income students from the bottom quartile of the distribution earning 1.073 additional points on their semester GPA in Spring 2020, an increase of 55% relative to their counterparts’ pre-pandemic GPA mean of 1.939. The coefficient ( β 1 + β 2 ) is statistically significant at the 1% level (standard error = 0.051).

This higher relative GPA among low-performing students in the bottom quartile is strongly associated with the flexible grading policy as the differential vanishes when I use the GPA students would have earned in the absence of the flexible grading policy. Column 5, Panel B in Table 2 shows that β 2 is close to zero, negative, and not statistically significant for this group of students. Indeed, as seen in column 3 of Table 3 , lower-income students from the bottom quartile were 8.1 percentage points more likely to exercise the pass/fail option than their higher-income counterparts. This effect represents a 35% increase in the prevalence of the pass/fail option relative to their counterparts’ prevalence of 22.9% and is statistically significant at the 5% level.

Prevalence and intensity of flexible grading options by low-income status and pre-pandemic academic performance, OLS estimates, outcomes from transcript data.

Notes : The table reports individual OLS on the low-income dummy, quartile dummies, and interactions between the two from a regression where the outcome is indicated in column headings. I estimate Eq. (4) in the main text. The bottom quartile is the omitted quartile. All regressions include a female, race and ethnicity indicators, a USA born indicator, a first-generation student indicator, a transfer-student indicator, an ESL-learner indicator, class-level indicators (including a graduate student indicator), major dummies, and Fall 2019 cumulative GPA. Robust standard errors are reported in parentheses.

*, ** , *** Estimate significantly different from zero at the 0.1 or 0.05 level or 0.01 level.

A distinct pattern is observed between lower-income students in the top and bottom quartile of the distribution relative to the GPA gap by pre-pandemic performance of their higher-income counterparts as shown by a negative and statistically significant δ 4 in column 1, panel B in Table 2 . While the Spring 2020 GPA of top-performing higher-income students, ( β 1 + γ 4 ) , increased by 0.097 points relative to their pre-pandemic mean of 3.718 (a statistically significant 2.6% increase), the Spring 2020 GPA of top-performing lower-income students only increased by half the size as ( β 1 + γ 4 + β 2 + δ 4 ) = 0.041 , and I could not reject the null hypothesis, H 0 : β 1 + γ 4 + β 2 + δ 4 = 0 . 5

Column 5 further reveals that, in the absence of the flexible grading policy, the GPA of lower-income top-performing students, ( β 2 + δ 4 ) , would have been 4% lower than that of their higher-income counterparts relative to the pre-pandemic mean for the comparison group. 6 This effect is statistically significant at the 5% level. To put it differently, in the absence of flexible grading, lower-income top-performing students’ Spring 2020 GPA would have been 0.189 points lower than their pre-pandemic average ( β 1 + γ 4 + β 2 + δ 4 = - 0.189 ) , representing a 5% decrease relative to their counterfactual pre-pandemic mean GPA of 3.718. This effect is statistically significant at the 1% level.

Consistent with these estimates, the event study (shown in Fig. 2 ) paints a quite different picture for lower-income students in the bottom and top quartiles of the Fall 2019 cumulative GPA distribution relative to their wealthier peers. Panel A, which focuses on the bottom quartile, shows a lack of pre-pandemic trends followed by an increase in lower-income students’ Spring 2020 official GPA relative to their wealthier peers (LHS figure). However, no such increase is observed had the flexible grading policy not been available (RHS figure). In contrast, Panel B, which focuses on the top quartile, shows that the lack of pre-pandemic trends is followed by a decrease in lower-income students’ Spring 2020 GPA prior to students exercising the flexible grading policy relative to their wealthier peers (RHS figure), with no post-pandemic change observed with the official GPA (LHS figure). Appendix Fig. A.1 shows the event studies for the other outcomes and quartiles.

Semester GPA by Income Level for Bottom- and Top-Performing Students. Before and After Exercising the Flexible Grading Policy. Notes : These figures plot the coefficients on the interaction between the semester dummies and the low-income dummy (and the 95% confidence intervals) from an individual fixed-effects model as in Eq. (2) in the main text using students in different quartiles of the Fall 2019 cumulative GPA distribution. Standard errors are clustered at the individual level. On the left-hand side, the outcome is the official semester GPA. On the right-hand side, the outcome is the official semester GPA except for the Spring 2020, in which case I have replaced the official GPA with the GPA the student would have earned had the flexible grading policy not been available. I estimated this GPA using the courses the student enrolled in, and the grades originally earned in those courses (including official and unofficial withdraws) from transcript data. Panel A shows estimates using students from the bottom quartile of the Fall 2019 semester cumulative GPA distribution. Panel B shows estimates using students from the top quartile. The Fall 2019 semester dummy is the omitted semester. Appendix Fig. A.1 shows estimates for two middle quartiles and for other outcomes, namely, credits taken, credits earned, and credits failed (or NC earned).

To address concerns that this differential pattern in the official Spring 2020 GPA may be driven by students differentially selecting majors according to their income, I re-estimate Eq. (3) adding the interaction of the Spring 2020 dummy with major dummies. Similarly, to determine whether this differential effect by income is partially driven by different income shares across years-in-college (freshmen, sophomore, junior, or seniors) or graduate school, I re-estimate Eq. (3) adding the interaction of the Spring 2020 dummy with dummies indicating years-in-college and enrollment in graduate school. Finally, because Spring 2017 academic outcomes are somewhat anomalous, I estimate Eq. (3) dropping observations from Spring 2017 semester. The findings in this study (shown in Appendix Table A.3) are robust to the above specifications suggesting that these alternative explanations are not driving this differential pattern by income.

It is worthwhile highlighting that, as for bottom-performing students, the flexible grading policy helped lower-income top-performing students increase their Spring 2020 GPA. In this case, the increase relative to their wealthier counterparts was of 0.088 points (from −0.144 to −0.056), the equivalent of 2.4% increase relative to the pre-pandemic counterfactual mean of 3.718. But, in contrast with lower-income students in the bottom quartile, the GPA for those in the top quartile did not increase relative to their wealthier peers once the flexible grading policy has been taken into account. Table 3 suggests that this may be due to the differential exercise of pass/fail option between top- and bottom-performing lower-income students relative to the differential prevalence among their wealthier peers. Indeed, column 3 shows that top-performing lower-income students were 9.8 percentage points less likely to exercise the pass/fail option than bottom-performing lower-income students relative to the differential observed between their wealthier counterparts. As 17.25% of higher-income students exercised the pass/fail option, this represents a reduction of 57%. This effect is statistically significant at the 5% level.

Yet, this does not explain the differential income effect of the pandemic on top-performing students before students exercised the flexible grading policy. Below, I explore plausible mechanisms behind lower-income top-performing students underperformance relative to their wealthier counterparts.

6. Mechanisms

Because of the disruptions that COVID-19 represented to higher education and the abrupt move to online learning, we would have expected academic performance of college students to drop. As explained in Section IV, this did not happen. Instead, the official Spring GPA of higher-income students increased by an average of 13.4% relative to the pre-pandemic and after controlling for time-invariant heterogeneity. While about two fifths of this increase is explained by the flexible grading policy, a 7.6% remains unexplained.

Several reasons could explain this improvement beyond students’ greater flexibility in their grading choices due to the flexible grading policy: (1) a different assessment process with easier exams and/or more lenient grading; (2) more difficult supervising process as exams were online, leading to potentially greater cheating; (3) an improvement in students’ learning strategies with online learning; (4) lower opportunity costs of studying due to less employment available; and (5) lower financial stress due to greater availability of emergency relief funds from the college or the government.

While I am unable to derive strict tests for the relative importance of these mechanisms, systematic heterogeneity of the effects of the pandemic across different groups of students based on pre-pandemic income and performance inequalities ought to provide suggestive evidence consistent with one mechanism but not with another.

It is plausible that changes in both faculty’s leniency, as well as exams’ assessment and supervision may be behind some of the higher post-pandemic GPA observed for the whole sample. However, given the findings on the role of the flexible grading policy, it is unlikely that they drive the observed academic-performance differences between lower- and higher-income students.

Assuming dynamic complementarity and self-productivity, we would expect multiplier effects of skills and ability ( Cunha and Heckman, 2007 ). In such case, the online-learning costs would be lower for higher- than lower-performing students, consistent with recent findings ( Grewenig et al., 2020 , Kuhfeld et al., 2020 , Kofoed et al., 2021 , Bird et al., 2020 ). Yet, estimates from panel B of Table 2 indicate that the Spring 2020 GPA increase was inversely related to pre-pandemic performance.

To explore the other possibilities, I estimate model (4) using as outcomes students’ survey responses regarding: (1) their perception of having challenges with online learning; (2) their opportunity costs of studying, measured by whether they worked during the Spring 2020, and by whether they worked less due to COVID-19; (3) their receipt of emergency relief funds; and (4) their perception of how flexible grading influenced their grading choices. 7 While these estimates are not capturing a causal relationship, they may present suggestive evidence of which mechanisms may be associated with the differential outcomes by income.

As seen in column 1 in Table 4 , top-performing lower-income students were 6.8 percentage points more likely to experience challenges with online learning than their higher-income counterparts. This effect is statistically significant at the 5% level. A similar income disadvantage is observed among students in third quartile, albeit the effect is only marginally significant. No such income differential effect is observed among students in the bottom half of the distribution.

Students’ challenges and self-reported use of flexible grading by low-income status and pre-pandemic academic performance, OLS estimates, outcomes from survey data.

Lower-income students worked, on average, 3.9 percentage points less due to the pandemic but received 15 percentage points more emergency relief funds than their higher-income peers. Both effects are statistically significant at the 5% or lower (estimates available from author upon request). While there is no differential effect by pre-pandemic performance for receipt of emergency relief funds among lower-income students, the income differential effect on working less in Spring 2020 is driven by the lowest performing students (albeit only marginally significant). This suggests that a plausible driver of the differential Spring 2020 GPA prior to exercising the flexible grading between lower- and higher-income students in the bottom quartile could be the post-COVID-19 lower opportunity cost of studying among lower-income students.

It is noteworthy that, because of the pandemic, top-performing lower-income students were 9.7 percentage points more likely to report asking for an incomplete than their bottom-performing lower-income peers and 5.8 percentage points more likely to ask for an incomplete than their higher-income peers as seen in column 7 of Table 4 . Both estimates are significant at the 1% level. It is important to underscore that the students’ survey responses to the influence of the flexible grading policy is not directly comparable to the observed in the transcripts because they capture a change in behavior relative to the pandemic. To the extent that incompletes are allowed under normal times and students can sometimes also select Pass/No Credit option, the survey responses may be picking up a change whereas the transcript data only measure levels for Spring 2020.

In contrast, bottom-performing lower-income students were 7.1 percentage points more likely to report choosing a pass/fail or NC grade than their higher-income peers as seen in column 6. 8 Even though, this estimate is only marginally significant (at the 10% level), it corroborates results from transcript data suggesting that the bottom-performing lower-income students improved their Spring 2020 academic performance by converting poor grades in pass/fail, preventing them from lowering the semester GPA and dropping courses.

It is interesting to underscore that the bottom-performing lower-income students are 17.1 percentage points more likely than their higher-income peers to report facing challenges to maintain financial aid. To maintain financial aid (including the Pell grant), students must be enrolled in at least 6 credits (12 to receive the full amount) and maintain a GPA of 2.0. Hence, it is not surprising that lower-income bottom-performing students, concerned with losing their Pell Grant or having to return a portion of the funding already received, may had been willing to use the pass/fail grade to avoid getting a grade that would have hurt their GPA.

Interestingly, there is a statistically significant income differential in students’ challenges to maintain financial aid for those in the third and top quartile, although this differential is half to two thirds of the size observed among bottom performers. This income differential among top performers may reflect their need to maintain a certain GPA to preserve merit-based scholarship, which would explain their self-reported higher intake of incompletes due to COVID-19.

7. Conclusion

Using individual students’ administrative academic records from Spring 2017 to Spring 2020 and controlling for individual fixed effects, this paper first documents an increase in college students’ Spring 2020 GPA relative to prior academic performance. Difference-in-differences models and event-study analyses with individual fixed effects reveal heterogeneity by pre-pandemic income and pre-pandemic academic performance inequalities.

I find that bottom-performing lower-income students outperform their higher-income peers. This differential is fully explained by students’ use of the flexible grading policy with lower-income students being 35% more likely to exercise the pass/fail option than their counterparts. Students’ survey responses suggest that lower-income students’ greater concerns with maintaining financial aid may be behind their higher use of flexible grading.

In contrast, there is no GPA advantage by income among top-performing students. Instead, in the absence of the flexible grading policy, lower-income top-performing students would have underperformed relative to their higher-income counterparts. I find suggestive evidence that this lower performance may be driven by lower-income top-performing students experiencing greater challenges with online learning than their wealthier peers. These students also reported a higher use of incompletes than their higher-income peers and being more concerned about maintaining (merit-based) financial aid. Transcript data also reveals that they were less likely to exercise the pass/fail option than bottom-performing lower-income students relative to the differential observed between their wealthier counterparts.

The current research underscores the relevance of the flexible grading policy and its differential use based on students’ particular needs. The findings in this paper suggest that the flexible grading policy was able to counteract negative shocks, especially among the most disadvantaged students. Because low-income students regularly face idiosyncratic challenges, the results in this paper suggest that a higher use of the pass/fail grade (if not for all courses, for certain courses) may support students during critical moments. Future research ought to analyze how the pandemic will affect students’ academic performance in the medium and longer run, especially once the flexible grading policy no longer applies and in-person teaching resumes. Another interesting line of research is to analyze whether flexible grading per se will impact students’ future academic performance.

The author would like to thank the Co-Editor Christopher Walters and two anonymous referees for excellent comments and suggestions on earlier versions of the paper. The author would also like to recognize Eva Fernandez for her support with IRB and survey design, Cheryl Littman and Lizandra Friedland for support with designing the survey and collecting the data, and Jennifer Roff for comments on the paper.

I received IRB approval (IRB file #2020–0475) on July 21st 2020 to conduct the survey, collect, and de-identify administrative records, and merge both data sources. In Spring 2021, I amended the IRB to also have access to students’ Spring 2020 transcripts.

The distribution of majors is shown in Appendix Table A.1.

( 0.405 - 0.228 ) 3.014 = 0.059

In contrast, the pandemic effect for higher-income students in the top quartile is statistically significant at the 1% level.

- 0.144 3.718 = - 0.0387

The survey asked students whether the flexible grading policy implemented as a consequence of COVID-19 influenced them in: not dropping a course; choosing a pass/fail or NC class; or asking for an incomplete for the Spring 2020. Students could also mark that they were not aware of the flexible grading policy. Students were instructed to mark all responses that applied to them.

The survey question did not distinguish between pass/fail grade and NC grade as in the transcript data.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jpubeco.2022.104606 .

Appendix A. Supplementary data

The following are the Supplementary data to this article:

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