Yuxi Tang
Weiguang He*- College of Social Sciences, Shenzhen University, Shenzhen, China
Introduction: The COVID-19 pandemic has had a profound impact on the mental health and academic performance of university students worldwide. Anxiety is one of the most commonly reported mental health issues among this population, but its relationship with academic performance during the pandemic has not been fully explored.
Methods: A meta-analysis was conducted following the PRISMA-P guidelines to synthesize existing research on the relationship between anxiety and academic performance in university students during the COVID-19 pandemic. Four databases were searched (PsycINFO, Web of Science, PubMed, and Scopus) for articles published between December 2019 and June 2022, and studies from five different countries were included in the analysis. A heterogeneity test was performed, and a fixed-effects model was used to calculate the main results.
Results: The meta-analysis revealed a negative correlation between university students' anxiety and academic performance (r = −0.211, k = 5, N = 1,205). Subgroup analysis found no significant regulatory effects for the year of publication, country development level, student type, or anxiety type. The results suggest that negative emotions induced by the pandemic are the most significant factor linking anxiety to poor academic performance.
Discussion: During pandemics with severe global consequences, such as COVID-19, interventions against and for the prevention of university students' negative emotions are important ways to improve university students' mental health and academic achievement.
1. Introduction
The continued prevalence of coronavirus disease 2019 (COVID-19) has not only caused many casualties worldwide but also seriously affected the mental health of university students from different countries by engendering anxiety among this group (Islam S., et al., 2020; Huarcaya-Victoria et al., 2021; Visser and Law-van Wyk, 2021). The anxiety level of university students worldwide has generally increased, which has harmed their academic performance. Understanding the relationship between the anxiety and academic performance of university students and the factors that influence this relationship during the COVID-19 pandemic would be helpful for educational managers to formulate appropriate policies and intervention methods. However, to date, no meta-analysis has assessed this issue. Meta-analysis can help people comprehensively examine and generalize the relationship between academic performance and anxiety and obtain more reliable conclusions. A comprehensive meta-analysis is needed to analyze the relationship between anxiety and academic performance of college students in the context of the pandemic. When educational administrators have a clear understanding of the enormous negative consequences of college students' anxiety, they are more likely to develop systematic ways to intervene in college students' anxiety by protecting college students' mental health and providing them with more social support.
Anxiety is usually defined as a mental disorder (Spitzer et al., 2006) and an emotional state that pertains to coping with possible negative future events (McNaughton, 2018). Many studies have shown that university students are prone to anxiety disorders (Sakin Ozen et al., 2010; Walters et al., 2018; Wang et al., 2020; Li et al., 2022). Previous studies have suggested that anxiety among university students may be engendered by an inability to adapt to new situations, worry about future uncertainty, difficulty in solving problems, and negative life experiences (Vitasari et al., 2010; Arbona et al., 2021; Sustarsic and Zhang, 2022). There are many specific drivers of university students' anxiety, such as learning (Islam M. A., et al., 2020), exams (Fernández-Castillo and Caurcel, 2015; Hamzah et al., 2018), and problematic Internet use (Lozano Blasco et al., 2020). Academic stress is the main cause of anxiety among university students due to their heavy academic load (Zhang et al., 2022). In addition, social and economic pressures may cause anxiety (Jones et al., 2018). The reasons for university students' anxiety also include the transformation of how courses are delivered (i.e., from in-person to remote), increased academic burden, lack of technical literacy, economic difficulties, health concerns, and reduced social interactions (Deng et al., 2021).
Academic performance is defined as the extent to which students have mastered course knowledge and skills, the ability to complete academic tasks, and the overall academic results achieved (Adediwura and Tayo, 2007; Richardson et al., 2012; Talsma et al., 2018). The factors and mechanisms that influence college students' academic performance have been a topic of interest, especially in light of the increased academic challenges faced by college students during the pandemic. College students' academic achievement is influenced by numerous components, including cognition (Singh et al., 2016), psychology (MacCann et al., 2020), and social environment (Doleck and Lajoie, 2018). Among these, mental health has a greater impact on the academic performance of college students.
Past studies have suggested that university students' academic and non-academic anxiety harm learning achievements (Leppavirta, 2011; Adeoye-Agboola and Evans, 2015; Zhang et al., 2019; Liu and Xu, 2021). However, a few studies have also found that students' test of anxiety is not significantly correlated with academic performance (Karjanto and Yong, 2013), which implies that research in this area needs to be further developed. Previous meta-analyses have revealed correlations of −0.21 (Seipp, 1991), −0.28 (Erzen, 2017), and −0.06 (Brumariu et al., 2022) between students' anxiety and overall academic performance. However, the effect sizes reported in previous studies are inconsistent. In Brumariu et al. (2022), the effect sizes for adult students were quite small, while the effect sizes reported by Seipp (1991) and Erzen (2017) were between small and moderate. Thus, it is necessary to conduct meta-analyses that use rigorous methods to determine the true effect sizes. Seipp (1991) did not distinguish between student groups. Furthermore, although Erzen (2017) analyzed college students as a subgroup, however, the study did not distinguish between undergraduate and graduate students. Brumariu et al. (2022) considered the age of students as an important basis for subgroup analysis but did not distinguish between undergraduate and graduate students. Because the learning situation of students in different education and learning stages is quite different, it is necessary to select college students separately for meta-analysis.
In addition, previous meta-analyses did not consider the special context of the pandemic. In the current situation, which is exemplified by the repeated impact of the pandemic, it is especially urgent to expand the meta-analyses in this area. Therefore, this meta-analysis addresses the following two issues: (1) the overall correlation between university students' anxiety and academic performance during the COVID-19 pandemic and (2) variables regulating the relationship between university students' anxiety and academic performance during the COVID-19 pandemic.
2. Methods
This study followed the PRISMA-P guidelines (Moher et al., 2015). A detailed checklist is presented in Supplementary Table 1.
2.1. Literature search
When conducting a meta-analysis, it is necessary to select an appropriate research database (Gusenbauer and Haddaway, 2020). Accordingly, we primarily selected PsycINFO, Web of Science, PubMed, and Scopus as the main search databases and used Google Scholar and Semantic Scholar databases for the supplementary search. We also manually searched the references of the preliminary selected articles to find supplementary articles that met the selection criteria. After discussion, the two researchers reached an agreement on the retrieval strategy and carried out independent literature searches. The search terms used were “college student” or “university student,” “undergraduate” or “graduate,” “COVID-19 pandemic,” “COVID-19,” or “2019 coronavirus,” “anxiety,” “worry,” or “fear,” “academic performance” or “academic achievement,” and “test scores” or “learning achievement.” Since the COVID-19 pandemic began in December 2019, the publication date was limited to between 1 December 2019 and 30 June 2022. More details regarding the literature search strategy are shown in Supplementary Table 2. After reading the title, abstract, and body of the article, the two researchers included articles that they believed met the selection criteria. Duplicate articles were removed. The two researchers discussed the different articles until they reached an agreement regarding the inclusion of the article. If the two researchers could not reach an agreement, a third reviewer was invited to participate in the discussion and determine the preliminary selected articles.
2.2. Inclusion and exclusion criteria
The two researchers primarily selected articles written in English that studied the relationship between university students' anxiety and their academic performance during the COVID-19 pandemic. Specifically, research articles that included university students as participants were selected. There were no specific requirements regarding the research design, but the selected research needed to be within the context of the COVID-19 pandemic, contain assessments of university students' anxiety and academic performance, and analyze the correlation between university students' anxiety and academic performance. Anxiety could be assessed through physiological measurement or self-report. Academic performance could be assessed as actual measured academic performance or as self-reported performance. Articles that did not meet these standards were discarded.
2.3. Data extraction
The two researchers first discussed the data available in the research articles. After a discussion, they agreed that the following information should be collected: author, year of publication, country, research design, student type, the proportion of female students, average age, student major, sample size, anxiety assessment tools used, methods of evaluating academic performance, effect size, and research quality. The two researchers first extracted relevant data independently and then engaged in a discussion to reach an agreement on any divergent content.
2.4. Quality assessment
Previous researchers have used the Joanna Briggs Institute Methodological Quality Questionnaire (MQQ) framework (Acosta et al., 2020) to evaluate the methodological quality of the selected articles. This method quality checklist provides researchers with a basis for critical evaluation in terms of nine dimensions: theoretical definition, operational definition, research design, sample design, sample description, data credibility, data analysis, practical impact, and policy impact. The MQQ contains nine questions, each of which receives a score of 0, 1, or 3, such that the overall maximum score of the questionnaire is 27. In this evaluation standard, since each question includes a two-step review, a score of 0 or 1 is assigned in the first step review. Only after obtaining a score of 1 in the first step review is the second step review conducted. The second review assigns a score of 0 or 2 so that the final possible review score for each question cannot include the value 2. We scored the methodological quality of the preliminary selected articles according to the MQQ standard. A total score of 19–27 points indicated high quality; 9–18 points, medium quality; and 0–8 points, low quality. Low-quality research articles were eliminated from the analysis.
2.5. Data analysis
This meta-analysis used Pearson's r to measure the correlation between university students' anxiety and academic performance. If Pearson's r was reported directly in a given study, no conversion was necessary. If other measures of effect size were reported instead, the method provided by Borenstein (2009) was used to uniformly convert other types of effect sizes into r values. It was necessary to analyze the heterogeneity among the included studies; I2 was used for this purpose (Huedo-Medina et al., 2006). When calculating the total effect, researchers need to choose whether to use a fixed-effect or a random-effect design according to the heterogeneity among studies. In addition, publication bias was assessed, and a sensitivity analysis was conducted (Copas and Shi, 2000). To assess publication bias, a funnel plot was inspected for symmetry (Sterne et al., 2011) in combination with Egger's expression test (Lin and Chu, 2018). In the sensitivity analysis, the selected studies were excluded individually, and the changes in the combined effect values before and after exclusion were observed to judge whether the results were robust. Through a subgroup analysis, variables that potentially affected the overall effect were further assessed (Borenstein and Higgins, 2013).
3. Results
3.1. Literature search results
We first screened the 637 articles that met the criteria independently and manually excluded 278 duplicate articles. We further read the title and abstract of the remaining 359 articles and excluded 244 articles that did not meet the selection criteria of this literature review. We read the full text of the remaining 115 articles, finally excluding 110 articles that did not meet the selection criteria. Finally, we reached a consensus that five articles should be used in the meta-analysis. Detailed information on the studies included in the meta-analysis is provided in the Supplementary material. Figure 1 shows the study inclusion and exclusion processes.
Figure 1. Flow diagram of studies included and excluded. From Moher et al. (2015).
3.2. Research characteristics
We separately coded the included studies and used percent agreement to assess interrater reliability. The coding consistency percentages of the two researchers on the national economic development level, research type, research methodology quality, and other items were all >90%. We discussed and reached an agreement on any codes that exhibited differences. The correlation coefficient between university students' anxiety and academic performance ranged from −0.294 to −0.120. These studies were carried out in five countries, namely the United Kingdom, Indonesia, South Korea, India, and Malaysia. The years of publication of the articles were concentrated in 2021 and 2022. All of the studies were cross-sectional. Among the five studies, three recruited undergraduate students, one assessed graduate and postgraduate students, and one considered postgraduate students. The proportion of female students in the five studies ranged from 50 to 69.2%. The average age of participants ranged from 23.7 to 44.4 years. The sample sizes ranged from 100 to 436, and the total sample size was 1,205. Two of the five studies reported the academic anxiety level of university students, where one reported the test anxiety of university students, one reported the social evaluative anxiety of university students regarding COVID-19, and one reported the economic anxiety of university students. Three of the five studies used university examination results as a measure of academic performance, and two used a learning-output-related scale. Table 1 shows the main characteristics of the included studies.
Table 1. Characteristics of the included studies.
3.3. Research quality assessment
Five studies met the research quality requirements of this literature review and were included in the meta-analysis. We evaluated the five studies according to the MQQ, where four studies were rated as high-quality and one as medium-quality. Overall, the quality of the five studies met the inclusion criterion, with an average MQQ score of 21. Table 2 shows the methodological quality evaluation results of the study.
Table 2. Methodological quality of the included studies.
3.4. Mean correlation and heterogeneity test
The correlation coefficient between university students' anxiety and academic performance in these five studies ranged from −0.294 to −0.120. Through the meta-analysis of these five studies using a fixed-effects model, the average correlation coefficient between university students' anxiety and academic performance during the COVID-19 pandemic was determined to be −0.211 (95% CI: −0.264, −0.156, p < 0.001). There was a low degree of heterogeneity among the five studies (I2 = 16.676%, p = 0.308). Figure 2 shows forest plots of the relationship between the anxiety and academic performance of university students during the COVID-19 pandemic.
Figure 2. Forest plot of the relationship between the anxiety and academic performance of university students during the COVID-19 pandemic.
3.5. Subgroup analysis
To further analyze the influence of different grouping factors on effect size in these five studies, a subgroup analysis was performed using a fixed-effects model. The subgroup analysis was conducted according to the survey year of publication, country development level, student type, and anxiety type (refer to Table 3). The effect size for the research published in 2021 was −0.208 (95% CI: −0.270, −0.144, p < 0.001), while the effect size for the research published in 2021 was −0.218 (95% CI: −0.322, −0.108, p < 0.001); no significant regulatory effect was found (Q = 0.009, p = 0.926). The effect size for the developed country subgroup was −0.148 (95% CI: −0.232, −0.063, p = 0.001), while that for the developing country subgroup was −0.257 (95% CI: -−0.325, −0.108, p < 0.001); no significant regulatory effect was found (Q = 3.728, p = 0.054). The effect size for the undergraduate subgroup was −0.190 (95% CI: −0.251, −0.128, p < 0.001), while the effect size for the non-undergraduate subgroup was −0.287 (95% CI: −0.397, −0.168, p < 0.001); no significant regulatory effect was found (Q = 1.986, p = 0.159). The effect size for the general anxiety subgroup was −0.234 (95% CI: −0.330, −0.134, p < 0.001, while the effect size for the non-general anxiety subgroup was −0.200 (95% CI: −0.264, −0.135, p < 0.001); thus, no significant regulatory effect was found (Q = 0.231, p = 0.631).
Table 3. Subgroup analysis of the anxiety and academic performance of university students during the COVID-19 pandemic.
3.6. Sensitivity analysis
Each study included in the meta-analysis was individually excluded, and the overall effect size was calculated using a fixed-effects model. There was no significant change in the results before and after exclusion (Table 4), indicating that meta-sensitivity was low, and the results obtained were robust.
Table 4. Sensitivity analysis results.
3.7. Publication bias
Using the fixed-effects model, we generated a funnel plot of effect sizes. The funnel diagram was symmetrical around the central axis, whereby the effect sizes of these five studies were evenly distributed on both sides of the total effect (Figure 3). Egger's expression test suggested no evidence of publication bias (p > 0.05).
Figure 3. Funnel plot of the anxiety and academic performance of university students during the COVID-19 pandemic. Each point denotes a study.
4. Discussion
Through the meta-analysis of the correlation between university students' anxiety and academic performance as reported in the five articles, we found that anxiety had a small-to-moderate negative effect on academic performance during the COVID-19 period. The effect size ranged from −0.294 to −0.120, and the mean effect size was −0.211 (p < 0.001). The year of publication, country development level, student type, and anxiety type did not exhibit a significant regulatory effect. This meta-analysis was somewhat different from previous studies in this area. We primarily focused on college students, including both undergraduate and postgraduate students. Previous studies did not subdivide college students and did not consider the impact of specific major epidemics. The effect sizes obtained by previous studies were inconsistent, with correlation coefficients ranging from −0.06 to −0.28 (Seipp, 1991; Erzen, 2017; Brumariu et al., 2022). Our effect sizes differ from those reported by Brumariu et al. (2022), who found an effect size r of only −0.03 in the adult student population (18–25 years old). This may be related to the fact that they only considered a clinical anxiety disorder and did not consider the negative academic impact caused by other types of anxiety. In fact, test anxiety and course anxiety are common types of anxiety in students, which have a small-to-moderate negative impact on their academic performance (von der Embse et al., 2018). Effect sizes may be reduced after excluding these anxiety types. Although the effect sizes we report were close to the effect value reported by Erzen (2017) for the university student group (r = −0.27), the current meta-analysis involved a different social environment than that was present in their study. During the pandemic period, more people exhibited anxiety symptoms, and the degree of anxiety was higher. Thus, even if the effect sizes were close, the negative consequences of this anxiety may be very different in the context of a pandemic, potentially leading to larger-scale negative consequences.
In addition to summarizing the effect sizes, we found that negative emotion could be a mechanism underlying this relationship. Studies have reported that negative emotions are an intermediary variable (Noman et al., 2021; Di Malta et al., 2022). Some studies have indicated that COVID-19 has caused university students to feel worried, afraid, and hopeless (Giusti et al., 2021; Ludwig, 2021). The negative emotions of university students caused by the prevalence of COVID-19 may be the fundamental cause of their diminished academic performance. One possible explanation is that different types of anxiety lead university students to experience negative emotions and difficulties in emotional regulation. Cognitive interference theory suggests that the fear of future negative events affects students' normal cognitive and memory processes (Putwain et al., 2010). The attention control model posits that physical anxiety occupies cognitive processing resources and distracts attention from learning (Eysenck et al., 2007). The motivation mediation model proposes that students' emotions have a negative impact on their motivation and goals (Plass and Kalyuga, 2019), indirectly hindering their academic performance. These theoretical models all suggest that university students' anxiety is closely related to diminished academic performance. Previous studies have also found that anxiety may cause difficulty with emotion regulation (Cisler and Olatunji, 2012; Carl et al., 2014; Jazaieri et al., 2014). The anxiety affected the normal emotional regulation of university students and increased their sense of boredom and fear. Students' emotional regulation can mediate the academic output (Genc, 2017). During the pandemic, university students' emotional regulation ability was hindered, which reduced academic performance.
We also found no regulatory effect on the level of economic development of the country where the students were located nor on the type of anxiety of the students, which is consistent with the conclusion of Seipp (1991). The regulatory effect of publication year was also not statistically significant, which may be related to the short publication time interval of these studies and that there was no significant difference in the environment within which the studies were conducted. Previous conclusions regarding the anxiety level of undergraduate and graduate students have been inconsistent (Chrikov et al., 2020; Xiao et al., 2020; Alhasani et al., 2022). Although postgraduate students may have a stronger emotional regulation ability due to their older age, postgraduate students may experience relatively greater difficulties in completing their graduate thesis (Liang et al., 2021). This may be why there was no statistical difference in effect sizes between postgraduate students and undergraduates.
4.1. Study strengths and limitations
This study, based on our search of several major research databases, is the first meta-analysis to summarize the relationship between university students' anxiety and learning performance in the context of the COVID-19 pandemic. However, this literature review has some limitations. First, there were few articles on this topic, which limited the number of articles that could be included in the analysis. All of the selected research studies employed a cross-sectional design, which is insufficient for determining the causal relationship. We did not distinguish different forms of anxiety, which may have led to bias in the evaluation of university student's anxiety levels. In addition, this literature review considered only English publications; relevant articles published in other languages may have been omitted.
5. Conclusions
This study found for the first time that there was a small-to-moderate negative correlation between university students' anxiety and academic performance during the COVID-19 pandemic. The effects of negative emotions and difficulties with emotion regulation may underlie the influence of university students' anxiety on their academic performance. The year of publication, country development level, student type, and anxiety type had no significant regulatory effect. At present, there are few studies on the impact of different forms of anxiety on academic performance during the COVID-19 pandemic, and it is necessary to further explore this issue. In addition, there is relatively little research on the mechanisms through which university student's anxiety level affects academic performance in this context. Further research is needed to determine the mediating variables and their regulatory role. In the future, it will be necessary to strengthen research on the impact of major pandemics on college student's mental health and academic output, especially given the emerging monkeypox outbreak.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
YXT was responsible for conceptualization and participated in the methodology design, data extraction, analysis, and original draft writing. WGH participated in data extraction, analysis, review, and editing. Both authors read and approved the final version of the manuscript to be considered for publication.
Funding
Funding was provided by the Ministry of Education Teachers of Ideological and Political Theory in Colleges and Universities Special Program Research on the Integration of ‘Four Histories' Education into the Teaching of Ideological and Political Courses in Colleges and Universities during the Intelligent Era (Project No. 21JDSZK092) and the Shenzhen Philosophy and Social Science planning project Embodied Communication of Red Culture among College Students in the Guangdong-Hong Kong-Macao Greater Bay Area in the Metaverse Era (Project No. SZ2022C001). The sponsor provided only financial support for this study and did not assist the researchers in formulating the research plan.
Acknowledgments
We are very grateful to the editors and reviewers for their valuable time.
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.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1018558/full#supplementary-material
References
Acosta, S., Garza, T., Hsu, H. -Y., and Goodson, P. (2020). Assessing quality in systematic literature reviews: a study of novice rater training. J. Sage Open. 10:2158244020939530. doi: 10.1177/2158244020939530
Adediwura, A., and Tayo, B. (2007). Perception of teachers' knowledge, attitude and teaching skills as predictor of academic performance in Nigerian secondary schools. Educ. Res. Rev. 2, 165–171.
Adeoye-Agboola, D. I., and Evans, H. (2015). The relationship between anxiety and academic performance of postgraduate international students in a British University: a cross-sectional quantitative design. Sci. J. Public Health. 3, 331–338. doi: 10.11648/j.sjph.20150303.15
Alhasani, M., Alkhawaji, A., and Orji, R. (2022). Mental health and time management behavior among students during COVID-19 pandemic: towards persuasive technology design. Hum. Behav. Emerg. Technol. 2022, 13. doi: 10.1155/2022/7376748
Arbona, C., Fan, W., Phang, A., Olvera, N., and Dios, M. (2021). Intolerance of uncertainty, anxiety, and career indecision: a mediation model. J. Career Assess. 29, 699–716. doi: 10.1177/10690727211002564
Borenstein, M. (2009). “Effect sizes for continuous data,” in The Handbook of Research Synthesis and Meta-Analysis, eds H. Cooper, L. V. Hedges, and J. C. Valentine (New York, NY: Russell Sage Foundation), 221–235.
Borenstein, M., and Higgins, J. P. T. (2013). Meta-analysis and subgroups. Prev. Sci. 14, 134–143. doi: 10.1007/s11121-013-0377-7
Brumariu, L. E., Waslin, S. M., Gastelle, M., Kochendorfer, L. B., and Kerns, K. A. (2022). Anxiety, academic achievement, and academic self-concept: meta-analytic syntheses of their relations across developmental periods. Dev. Psychoathol. 1–17. doi: 10.1017/S0954579422000323
Carl, J. R., Fairholme, C. P., Gallagher, M. W., Thompson-Hollands, J., and Barlow, D. H. (2014). The effects of anxiety and depressive symptoms on daily positive emotion regulation. J. Psychopathol. Behav. Assess. 36, 224–236. doi: 10.1007/s10862-013-9387-9
Chattopadhyay, M., and Sahoo, P. K. (2022). Test anxiety of M.Ed. trainees during Covid-19 in relation to their academic achievement. Int. J. Creat. Res. Thought. 10, e60–e72. Available online at: https://ijcrt.org/papers/IJCRT2205451.pdf
Chrikov, I., Soria, K. M., Horgos, B., and Jones-White, D. (2020). Undergraduate and Graduate Students' Mental Health During the COVID-19 Pandemic.
Cisler, J. M., and Olatunji, B. O. (2012). Emotion regulation and anxiety disorders. Curr. Psychiatry. Rep. 14, 182–187. doi: 10.1007/s11920-012-0262-2
Copas, J., and Shi, J. Q. (2000). Meta-analysis, funnel plots and sensitivity analysis. Biostatistics. 1, 247–262. doi: 10.1093/biostatistics/1.3.247
Deng, J. W., Zhou, F. W., Hou, W. T., Silver, Z., Wong, C. Y., Chang, O., et al. (2021). The prevalence of depressive symptoms, anxiety symptoms and sleep disturbance in higher education students during the COVID-19 pandemic: a systematic review and meta-analysis. Psychiatry Res. 301:113863. doi: 10.1016/j.psychres.2021.113863
Di Malta, G., Bond, J., Conroy, D., Smith, K., and Moller, N. (2022). Distance education students' mental health, connectedness and academic performance during COVID-19: a mixed-methods study. Distance Educ. 43, 97–118. doi: 10.1080/01587919.2022.2029352
Doleck, T., and Lajoie, S. (2018). Social networking and academic performance: a review. Educ. Inf. Technol. 23, 435–465. doi: 10.1007/s10639-017-9612-3
Erzen, E. (2017). “The effect of anxiety on student achievement,” in The Factors Effecting Student Achievement. Springer. p. 75–94. doi: 10.1007/978-3-319-56083-0_5
Eysenck, M. W., Derakshan, N., Santos, R., and Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion. 7, 336–53. doi: 10.1037/1528-3542.7.2.336
Fadhila, R., and Hernawan, B. (2021). “Relationship between stress, anxiety, and depression with learning achievement in medical student during online learning in the COVID 19 pandemic era,” in Proceedings of ICME 2021 Virtual Conference — Excellence in Health Profession Education Through Globalization & Collaboration, 139–149.
Fernández-Castillo, A., and Caurcel, M. J. (2015). State test-anxiety, selective attention and concentration in university students. Int. J. Psychol. 50, 265–271. doi: 10.1002/ijop.12092
Genc, A. (2017). Coping strategies as mediators in the relationship between test anxiety and academic achievement. Genc.Ana. 50, 51–66. doi: 10.2298/PSI160720005G
Giusti, L., Mammarella, S., Salza, A., Del Vecchio, S., Ussorio, D., Casacchia, M., et al. (2021). Predictors of academic performance during the covid-19 outbreak: impact of distance education on mental health, social cognition and memory abilities in an Italian university student sample. BMC. Psychol. 9, 142. doi: 10.1186/s40359-021-00649-9
Gusenbauer, M., and Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Res. Synthesis Method. 11, 181–217. doi: 10.1002/jrsm.1378
Hamzah, F., Mat, K. C., Bhagat, V., and Mahyiddin, N. S. (2018). Test anxiety and its impact on first year university students and the over view of mind and body intervention to enhance coping skills in facing exams. Res. J. Pharm. Technol. 11, 2220–2228. doi: 10.5958/0974-360X.2018.00411.0
Huarcaya-Victoria, J., Elera-Fitzcarrald, C., Crisol-Deza, D., Villanueva-Zúñiga, L., Pacherres, A., Torres, A., et al. (2021). Factors associated with mental health in Peruvian medical students during the COVID-19 pandemic: a multicentre quantitative study. Rev. Colomb. Psiquiatr. doi: 10.1016/j.rcp.2021.06.002
Huedo-Medina, T. B., Sánchez-Meca, J., Marín-Martínez, F., and Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol. Methods. 11, 193–206. doi: 10.1037/1082-989X.11.2.193
Islam, M. A., Barna, S. D., Raihan, H., Khan, M. N. A., and Hossain, M. T. (2020). Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: a web-based cross-sectional survey. PLoS ONE. 15:e0238162. doi: 10.1371/journal.pone.0238162
Islam, S., Akter, R., Sikder, T., and Griffiths, M. D. (2020). Prevalence and factors associated with depression and anxiety among first-year university students in Bangladesh: a cross-sectional study. Int. J. Ment. Health. Addict. 20, 1289–1302. doi: 10.1007/s11469-020-00242-y
Jazaieri, H., Morrison, A. S., Goldin, P. R., and Gross, J. J. (2014). The role of emotion and emotion regulation in social anxiety disorder. Curr. Psychiatry Rep. 17, 531. doi: 10.1007/s11920-014-0531-3
Jones, P. J., Park, S. Y., and Lefevor, G. T. (2018). Contemporary college student anxiety: the role of academic distress, financial stress, and support. J. Coll. Counsell. 21, 252–264. doi: 10.1002/jocc.12107
Karjanto, N., and Yong, S. T. (2013). Test anxiety in mathematics among early undergraduate students in a British university in Malaysia. Eur. J. Eng. Educ. 38, 11–37. doi: 10.1080/03043797.2012.742867
Kim, S. H., and Park, S. (2021). Influence of learning flow and distance e-learning satisfaction on learning outcomes and the moderated mediation effect of social-evaluative anxiety in nursing college students during the COVID-19 pandemic: A cross-sectional study. Nurs. Educ. Pract. 56, 103197. doi: 10.1016/j.nepr.2021.103197
Leppavirta, J. (2011). The impact of mathematics anxiety on the performance of students of electromagnetics. J. Engin. Educ. 100, 424–443. doi: 10.1002/j.2168-9830.2011.tb00021.x
Li, W., Zhao, Z., Chen, D., Peng, Y., and Lu, Z. (2022). Prevalence and associated factors of depression and anxiety symptoms among college students: a systematic review and meta-analysis. J. Child Psychol. Psychiatry 63, 1222–1230. doi: 10.1111/jcpp.13606
Liang, Z., Zeng, Q., Zhang, M., Luo, H., Huang, S., Li, J., et al. (2021). How course support and academic support impact on Chinese graduate students during the COVID-19: the multiple mediating roles of thesis writing and anxiety. Int. J. Environ. Res. Public Health. 19, 265. doi: 10.3390/ijerph19010265
Lin, L., and Chu, H. J. B. (2018). Quantifying publication bias in meta-analysis. Biometrics. 74, 785–794. doi: 10.1111/biom.12817
Liu, M. H., and Xu, H. L. (2021). Testing effects of foreign language listening anxiety on chinese university students' english listening test performance. Front. Psychol. 12:701926. doi: 10.3389/fpsyg.2021.701926
Lozano Blasco, R., Latorre Cosculluela, C., and Quílez Robres, A. (2020). Social network addiction and its impact on anxiety level among university students. Sustainability. 12, 5397. doi: 10.3390/su12135397
Ludwig, J. (2021). Poor performance in undergraduate math: Can we blame it on COVID-19 despair. Int. J. Innov. Sci. Math. 9, 31–40.
MacCann, C., Jiang, Y., Brown, L. E., Double, K. S., Bucich, M., and Minbashian, A. (2020). Emotional intelligence predicts academic performance: a meta-analysis. Psychol. Bull. 146, 150. doi: 10.1037/bul0000219
McNaughton, N. (2018). What do you mean ‘anxiety'? developing the first anxiety syndrome biomarker. J. R. Soc. N. Z. 48, 177–190. doi: 10.1080/03036758.2017.1358184
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., et al. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 4, 1–9. doi: 10.1186/2046-4053-4-1
Noman, M., Kaur, A., and Nafees, N. (2021). Covid-19 fallout: Interplay between stressors and support on academic functioning of Malaysian university students. Child. Youth. Serv. Rev. 125:106001. doi: 10.1016/j.childyouth.2021.106001
Plass, J. L., and Kalyuga, S. (2019). Four ways of considering emotion in cognitive load theory. Educ. Psychol. Rev. 31, 339–359. doi: 10.1007/s10648-019-09473-5
Putwain, D. W., Connors, L., and Symes, W. (2010). Do cognitive distortions mediate the test anxiety–examination performance relationship? Educ. Psychol. 30, 11–26. doi: 10.1080/01443410903328866
Richardson, M., Abraham, C., and Bond, R. (2012). Psychological correlates of university students' academic performance: a systematic review and meta-analysis. Psychol. Bull. 138, 353. doi: 10.1037/a0026838
Sakin Ozen, N., Ercan, I., Irgil, E., and Sigirli, D. (2010). Anxiety prevalence and affecting factors among university students. Asia Pac. J. Public Health 22, 127–133. doi: 10.1177/1010539509352803
Seipp, B. J. A. (1991). Anxiety and academic performance: a meta-analysis of findings. Anxiety. Res. 4, 27–41. doi: 10.1080/08917779108248762
Singh, S., Malik, S., and Singh, P. (2016). Research paper factors affecting academic performance of students. Indian J. Res. 5, 176–178.
Spitzer, R. L., Kroenke, K., Williams, J. B. W., and Lowe, B. (2006). A brief measure for assessing generalized anxiety disorder - the GAD-7. Arch. Intern. Med. 166, 1092–1097. doi: 10.1001/archinte.166.10.1092
Sterne, J. A., Sutton, A. J., Ioannidis, J. P., Terrin, N., Jones, D. R., Lau, J., et al. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 343:d4002. doi: 10.1136/bmj.d4002
Sustarsic, M., and Zhang, J. (2022). Navigating through uncertainty in the era of COVID-19: Experiences of international graduate students in the United States. J. Int. Stud. 12, 61–80. doi: 10.32674/jis.v12i1.3305
Talsma, K., Schüz, B., Schwarzer, R., and Norris, K. (2018). I believe, therefore I achieve (and vice versa): a meta-analytic cross-lagged panel analysis of self-efficacy and academic performance. Learn. Individ. Differ. 61, 136–150. doi: 10.1016/j.lindif.2017.11.015
Visser, M., and Law-van Wyk, E. (2021). University students' mental health and emotional wellbeing during the COVID-19 pandemic and ensuing lockdown. S. Afr. J. Psychol. 51, 229–243. doi: 10.1177/00812463211012219
Vitasari, P., Wahab, M. N. A., Othman, A., and Awang, M. G. (2010). A research for identifying study anxiety sources among university students. Int. Educ. Stud. 3, 189–196. doi: 10.5539/ies.v3n2p189
von der Embse, N., Jester, D., Roy, D., and Post, J. (2018). Test anxiety effects, predictors, and correlates: a 30-year meta-analytic review. J. Affect. Disord. 227, 483–493. doi: 10.1016/j.jad.2017.11.048
Walters, K. S., Bulmer, S. M., Troiano, P. F., Obiaka, U., and Bonhomme, R. (2018). Substance use, anxiety, and depressive symptoms among college students. J. Child Adolesc. Subst. Abuse 27, 103–111. doi: 10.1080/1067828X.2017.1420507
Wang, X., Chen, H., Liu, L., Liu, Y., Zhang, N., Sun, Z., et al. (2020). Anxiety and sleep problems of college students during the outbreak of COVID-19. Front. Psychiatry. 11:588693. doi: 10.3389/fpsyt.2020.588693
Xiao, H., Shu, W., Li, M., Li, Z., Tao, F., Wu, X., et al. (2020). Social distancing among medical students during the 2019 coronavirus disease pandemic in China: disease awareness, anxiety disorder, depression, and behavioral activities. Int. J. Environ. Res. Public Health. 17:5047. doi: 10.3390/ijerph17145047
Zhang, C., Shi, L., Tian, T., Zhou, Z., Peng, X., Shen, Y., et al. (2022). Associations between academic stress and depressive symptoms mediated by anxiety symptoms and hopelessness among Chinese college students. Psychol. Res. Behav. Manag. 15, 547–556. doi: 10.2147/PRBM.S353778
Keywords: COVID-19 pandemic, student anxiety, academic performance, university student, meta-analysis
Citation: Tang YX and He WG (2023) Meta-analysis of the relationship between university students' anxiety and academic performance during the coronavirus disease 2019 pandemic. Front. Psychol. 14:1018558. doi: 10.3389/fpsyg.2023.1018558
Received: 13 August 2022; Accepted: 16 February 2023;
Published: 14 March 2023.
Edited by:
Jesus de la Fuente, University of Navarra, SpainReviewed by:
Natanael Karjanto, Sungkyunkwan University, Republic of KoreaMustafa Öztürk Akcaoglu, Kastamonu University, Türkiye
Copyright © 2023 Tang and He. 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: Weiguang He, hwg@szu.edu.cn