short literature review about students academic stress

ORIGINAL RESEARCH article

Academic stress and mental well-being in college students: correlations, affected groups, and covid-19.

• 1 Department of Neurology, Rutgers New Jersey Medical School, Newark, NJ, United States
• 2 Rutgers New Jersey Medical School, Newark, NJ, United States
• 3 Office for Diversity and Community Engagement, Rutgers New Jersey Medical School, Newark, NJ, United States
• 4 Department of Biology, The College of New Jersey, Ewing, NJ, United States

Academic stress may be the single most dominant stress factor that affects the mental well-being of college students. Some groups of students may experience more stress than others, and the coronavirus disease 19 (COVID-19) pandemic could further complicate the stress response. We surveyed 843 college students and evaluated whether academic stress levels affected their mental health, and if so, whether there were specific vulnerable groups by gender, race/ethnicity, year of study, and reaction to the pandemic. Using a combination of scores from the Perception of Academic Stress Scale (PAS) and the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS), we found a significant correlation between worse academic stress and poor mental well-being in all the students, who also reported an exacerbation of stress in response to the pandemic. In addition, SWEMWBS scores revealed the lowest mental health and highest academic stress in non-binary individuals, and the opposite trend was observed for both the measures in men. Furthermore, women and non-binary students reported higher academic stress than men, as indicated by PAS scores. The same pattern held as a reaction to COVID-19-related stress. PAS scores and responses to the pandemic varied by the year of study, but no obvious patterns emerged. These results indicate that academic stress in college is significantly correlated to psychological well-being in the students who responded to this survey. In addition, some groups of college students are more affected by stress than others, and additional resources and support should be provided to them.

Introduction

Late adolescence and emerging adulthood are transitional periods marked by major physiological and psychological changes, including elevated stress ( Hogan and Astone, 1986 ; Arnett, 2000 ; Shanahan, 2000 ; Spear, 2000 ; Scales et al., 2015 ; Romeo et al., 2016 ; Barbayannis et al., 2017 ; Chiang et al., 2019 ; Lally and Valentine-French, 2019 ; Matud et al., 2020 ). This pattern is particularly true for college students. According to a 2015 American College Health Association-National College Health Assessment survey, three in four college students self-reported feeling stressed, while one in five college students reported stress-related suicidal ideation ( Liu, C. H., et al., 2019 ; American Psychological Association, 2020 ). Studies show that a stressor experienced in college may serve as a predictor of mental health diagnoses ( Pedrelli et al., 2015 ; Liu, C. H., et al., 2019 ; Karyotaki et al., 2020 ). Indeed, many mental health disorders, including depression, anxiety, and substance abuse disorder, begin during this period ( Blanco et al., 2008 ; Pedrelli et al., 2015 ; Saleh et al., 2017 ; Reddy et al., 2018 ; Liu, C. H., et al., 2019 ).

Stress experienced by college students is multi-factorial and can be attributed to a variety of contributing factors ( Reddy et al., 2018 ; Karyotaki et al., 2020 ). A growing body of evidence suggests that academic-related stress plays a significant role in college ( Misra and McKean, 2000 ; Dusselier et al., 2005 ; Elias et al., 2011 ; Bedewy and Gabriel, 2015 ; Hj Ramli et al., 2018 ; Reddy et al., 2018 ; Pascoe et al., 2020 ). For instance, as many as 87% of college students surveyed across the United States cited education as their primary source of stress ( American Psychological Association, 2020 ). College students are exposed to novel academic stressors, such as an extensive academic course load, substantial studying, time management, classroom competition, financial concerns, familial pressures, and adapting to a new environment ( Misra and Castillo, 2004 ; Byrd and McKinney, 2012 ; Ekpenyong et al., 2013 ; Bedewy and Gabriel, 2015 ; Ketchen Lipson et al., 2015 ; Pedrelli et al., 2015 ; Reddy et al., 2018 ; Liu, C. H., et al., 2019 ; Freire et al., 2020 ; Karyotaki et al., 2020 ). Academic stress can reduce motivation, hinder academic achievement, and lead to increased college dropout rates ( Pascoe et al., 2020 ).

Academic stress has also been shown to negatively impact mental health in students ( Li and Lin, 2003 ; Eisenberg et al., 2009 ; Green et al., 2021 ). Mental, or psychological, well-being is one of the components of positive mental health, and it includes happiness, life satisfaction, stress management, and psychological functioning ( Ryan and Deci, 2001 ; Tennant et al., 2007 ; Galderisi et al., 2015 ; Trout and Alsandor, 2020 ; Defeyter et al., 2021 ; Green et al., 2021 ). Positive mental health is an understudied but important area that helps paint a more comprehensive picture of overall mental health ( Tennant et al., 2007 ; Margraf et al., 2020 ). Moreover, positive mental health has been shown to be predictive of both negative and positive mental health indicators over time ( Margraf et al., 2020 ). Further exploring the relationship between academic stress and mental well-being is important because poor mental well-being has been shown to affect academic performance in college ( Tennant et al., 2007 ; Eisenberg et al., 2009 ; Freire et al., 2016 ).

Perception of academic stress varies among different groups of college students ( Lee et al., 2021 ). For instance, female college students report experiencing increased stress than their male counterparts ( Misra et al., 2000 ; Eisenberg et al., 2007 ; Evans et al., 2018 ; Lee et al., 2021 ). Male and female students also respond differently to stressors ( Misra et al., 2000 ; Verma et al., 2011 ). Moreover, compared to their cisgender peers, non-binary students report increased stressors and mental health issues ( Budge et al., 2020 ). The academic year of study of the college students has also been shown to impact academic stress levels ( Misra and McKean, 2000 ; Elias et al., 2011 ; Wyatt et al., 2017 ; Liu, C. H., et al., 2019 ; Defeyter et al., 2021 ). While several studies indicate that racial/ethnic minority groups of students, including Black/African American, Hispanic/Latino, and Asian American students, are more likely to experience anxiety, depression, and suicidality than their white peers ( Lesure-Lester and King, 2004 ; Lipson et al., 2018 ; Liu, C. H., et al., 2019 ; Kodish et al., 2022 ), these studies are limited and often report mixed or inconclusive findings ( Liu, C. H., et al., 2019 ; Kodish et al., 2022 ). Therefore, more studies should be conducted to address this gap in research to help identify subgroups that may be disproportionately impacted by academic stress and lower well-being.

The coronavirus disease 19 (COVID-19) pandemic is a major stressor that has led to a mental health crisis ( American Psychological Association, 2020 ; Dong and Bouey, 2020 ). For college students, the COVID-19 pandemic has resulted in significant changes and disruptions to daily life, elevated stress levels, and mental and physical health deterioration ( American Psychological Association, 2020 ; Husky et al., 2020 ; Patsali et al., 2020 ; Son et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Lopes and Nihei, 2021 ; Yang et al., 2021 ). While any college student is vulnerable to these stressors, these concerns are amplified for members of minority groups ( Salerno et al., 2020 ; Clabaugh et al., 2021 ; McQuaid et al., 2021 ; Prowse et al., 2021 ; Kodish et al., 2022 ). Identifying students at greatest risk provides opportunities to offer support, resources, and mental health services to specific subgroups.

The overall aim of this study was to assess academic stress and mental well-being in a sample of college students. Within this umbrella, we had several goals. First, to determine whether a relationship exists between the two constructs of perceived academic stress, measured by the Perception of Academic Stress Scale (PAS), and mental well-being, measured by the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS), in college students. Second, to identify groups that could experience differential levels of academic stress and mental health. Third, to explore how the perception of the ongoing COVID-19 pandemic affected stress levels. We hypothesized that students who experienced more academic stress would have worse psychological well-being and that certain groups of students would be more impacted by academic- and COVID-19-related stress.

Materials and Methods

Survey instrument.

A survey was developed that included all questions from the Short Warwick-Edinburgh Mental Well-Being ( Tennant et al., 2007 ; Stewart-Brown and Janmohamed, 2008 ) and from the Perception of Academic Stress Scale ( Bedewy and Gabriel, 2015 ). The Short Warwick-Edinburgh Mental Well-Being Scale is a seven-item scale designed to measure mental well-being and positive mental health ( Tennant et al., 2007 ; Fung, 2019 ; Shah et al., 2021 ). The Perception of Academic Stress Scale is an 18-item scale designed to assess sources of academic stress perceived by individuals and measures three main academic stressors: academic expectations, workload and examinations, and academic self-perceptions of students ( Bedewy and Gabriel, 2015 ). These shorter scales were chosen to increase our response and study completion rates ( Kost and de Rosa, 2018 ). Both tools have been shown to be valid and reliable in college students with Likert scale responses ( Tennant et al., 2007 ; Bedewy and Gabriel, 2015 ; Ringdal et al., 2018 ; Fung, 2019 ; Koushede et al., 2019 ). Both the SWEMWBS and PAS scores are a summation of responses to the individual questions in the instruments. For the SWEMWBS questions, a higher score indicates better mental health, and scores range from 7 to 35. Similarly, the PAS questions are phrased such that a higher score indicates lower levels of stress, and scores range from 18 to 90. We augmented the survey with demographic questions (e.g., age, gender, and race/ethnicity) at the beginning of the survey and two yes/no questions and one Likert scale question about the impact of the COVID-19 pandemic at the end of our survey.

Participants for the study were self-reported college students between the ages of 18 and 30 years who resided in the United States, were fluent in English, and had Internet access. Participants were solicited through Prolific ( https://prolific.co ) in October 2021. A total of 1,023 individuals enrolled in the survey. Three individuals did not agree to participate after beginning the survey. Two were not fluent in English. Thirteen individuals indicated that they were not college students. Two were not in the 18–30 age range, and one was located outside of the United States. Of the remaining individuals, 906 were full-time students and 96 were part-time students. Given the skew of the data and potential differences in these populations, we removed the part-time students. Of the 906 full-time students, 58 indicated that they were in their fifth year of college or higher. We understand that not every student completes their undergraduate studies in 4 years, but we did not want to have a mixture of undergraduate and graduate students with no way to differentiate them. Finally, one individual reported their age as a non-number, and four individuals did not answer a question about their response to the COVID-19 pandemic. This yielded a final sample of 843 college students.

Data Analyses

After reviewing the dataset, some variables were removed from consideration due to a lack of consistency (e.g., some students reported annual income for themselves and others reported family income) or heterogeneity that prevented easy categorization (e.g., field of study). We settled on four variables of interest: gender, race/ethnicity, year in school, and response to the COVID-19 pandemic ( Table 1 ). Gender was coded as female, male, or non-binary. Race/ethnicity was coded as white or Caucasian; Black or African American; East Asian; Hispanic, Latino, or of Spanish origin; or other. Other was used for groups that were not well-represented in the sample and included individuals who identified themselves as Middle Eastern, Native American or Alaskan Native, and South Asian, as well as individuals who chose “other” or “prefer not to answer” on the survey. The year of study was coded as one through four, and COVID-19 stress was coded as two groups, no change/neutral response/reduced stress or increased stress.

Table 1 . Characteristics of the participants in the study.

Our first goal was to determine whether there was a relationship between self-reported academic stress and mental health, and we found a significant correlation (see Results section). Given the positive correlation, a multivariate analysis of variance (MANOVA) with a model testing the main effects of gender, race/ethnicity, and year of study was run in SPSS v 26.0. A factorial MANOVA would have been ideal, but our data were drawn from a convenience sample, which did not give equal representation to all groupings, and some combinations of gender, race/ethnicity, and year of study were poorly represented (e.g., a single individual). As such, we determined that it would be better to have a lack of interaction terms as a limitation to the study than to provide potentially spurious results. Finally, we used chi-square analyses to assess the effect of potential differences in the perception of the COVID-19 pandemic on stress levels in general among the groups in each category (gender, race/ethnicity, and year of study).

In terms of internal consistency, Cronbach's alpha was 0.82 for the SMEMWBS and 0.86 for the PAS. A variety of descriptors have been applied to Cronbach's alpha values. That said, 0.7 is often considered a threshold value in terms of acceptable internal consistency, and our values could be considered “high” or “good” ( Taber, 2018 ).

The participants in our study were primarily women (78.5% of respondents; Table 1 ). Participants were not equally distributed among races/ethnicities, with the majority of students selecting white or Caucasian (66.4% of responders; Table 1 ), or years of study, with fewer first-year students than other groups ( Table 1 ).

Students who reported higher academic stress also reported worse mental well-being in general, irrespective of age, gender, race/ethnicity, or year of study. PAS and SWEMWBS scores were significantly correlated ( r = 0.53, p < 0.001; Figure 1 ), indicating that a higher level of perceived academic stress is associated with worse mental well-being in college students within the United States.

Figure 1 . SWEMWBS and PAS scores for all participants.

Among the subgroups of students, women, non-binary students, and second-year students reported higher academic stress levels and worse mental well-being ( Table 2 ; Figures 2 – 4 ). In addition, the combined measures differed significantly between the groups in each category ( Table 2 ). However, as measured by partial eta squared, the effect sizes were relatively small, given the convention of 0.01 = small, 0.06 = medium, and 0.14 = large differences ( Lakens, 2013 ). As such, there were only two instances in which Tukey's post-hoc tests revealed more than one statistical grouping ( Figures 2 – 4 ). For SWEMWBS score by gender, women were intermediate between men (high) and non-binary individuals (low) and not significantly different from either group ( Figure 2 ). Second-year students had the lowest PAS scores for the year of study, and first-year students had the highest scores. Third- and fourth-year students were intermediate and not statistically different from the other two groups ( Figure 4 ). There were no pairwise differences in academic stress levels or mental well-being among racial/ethnic groups.

Table 2 . Results of the MANOVA.

Figure 2 . SWEMWBS and PAS scores according to gender (mean ± SEM). Different letters for SWEMWBS scores indicate different statistical groupings ( p < 0.05).

Figure 3 . SWEMWBS and PAS scores according to race/ethnicity (mean ± SEM).

Figure 4 . SWEMWBS and PAS scores according to year in college (mean ± SEM). Different letters for PAS scores indicate different statistical groupings ( p < 0.05).

The findings varied among categories in terms of stress responses due to the COVID-19 pandemic ( Table 3 ). For gender, men were less likely than women or non-binary individuals to report increased stress from COVID-19 (χ 2 = 27.98, df = 2, p < 0.001). All racial/ethnic groups responded similarly to the pandemic (χ 2 = 3.41, df = 4, p < 0.49). For the year of study, first-year students were less likely than other cohorts to report increased stress from COVID-19 (χ 2 = 9.38, df = 3, p < 0.03).

Table 3 . Impact of COVID-19 on stress level by gender, race/ethnicity, and year of study.

Our primary findings showed a positive correlation between perceived academic stress and mental well-being in United States college students, suggesting that academic stressors, including academic expectations, workload and grading, and students' academic self-perceptions, are equally important as psychological well-being. Overall, irrespective of gender, race/ethnicity, or year of study, students who reported higher academic stress levels experienced diminished mental well-being. The utilization of well-established scales and a large sample size are strengths of this study. Our results extend and contribute to the existing literature on stress by confirming findings from past studies that reported higher academic stress and lower psychological well-being in college students utilizing the same two scales ( Green et al., 2021 ; Syed, 2021 ). To our knowledge, the majority of other prior studies with similar findings examined different components of stress, studied negative mental health indicators, used different scales or methods, employed smaller sample sizes, or were conducted in different countries ( Li and Lin, 2003 ; American Psychological Association, 2020 ; Husky et al., 2020 ; Pascoe et al., 2020 ; Patsali et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Lopes and Nihei, 2021 ; Yang et al., 2021 ).

This study also demonstrated that college students are not uniformly impacted by academic stress or pandemic-related stress and that there are significant group-level differences in mental well-being. Specifically, non-binary individuals and second-year students were disproportionately impacted by academic stress. When considering the effects of gender, non-binary students, in comparison to gender-conforming students, reported the highest stress levels and worst psychological well-being. Although there is a paucity of research examining the impact of academic stress in non-binary college students, prior studies have indicated that non-binary adults face adverse mental health outcomes when compared to male and female-identifying individuals ( Thorne et al., 2018 ; Jones et al., 2019 ; Budge et al., 2020 ). Alarmingly, Lipson et al. (2019) found that gender non-conforming college students were two to four times more likely to experience mental health struggles than cisgender students ( Lipson et al., 2019 ). With a growing number of college students in the United States identifying as as non-binary, additional studies could offer invaluable insight into how academic stress affects this population ( Budge et al., 2020 ).

In addition, we found that second-year students reported the most academic-related distress and lowest psychological well-being relative to students in other years of study. We surmise this may be due to this group taking advanced courses, managing heavier academic workloads, and exploring different majors. Other studies support our findings and suggest higher stress levels could be attributed to increased studying and difficulties with time management, as well as having less well-established social support networks and coping mechanisms compared to upperclassmen ( Allen and Hiebert, 1991 ; Misra and McKean, 2000 ; Liu, X et al., 2019 ). Benefiting from their additional experience, upperclassmen may have developed more sophisticated studying skills, formed peer support groups, and identified approaches to better manage their academic stress ( Allen and Hiebert, 1991 ; Misra and McKean, 2000 ). Our findings suggest that colleges should consider offering tailored mental health resources, such as time management and study skill workshops, based on the year of study to improve students' stress levels and psychological well-being ( Liu, X et al., 2019 ).

Although this study reported no significant differences regarding race or ethnicity, this does not indicate that minority groups experienced less academic stress or better mental well-being ( Lee et al., 2021 ). Instead, our results may reflect the low sample size of non-white races/ethnicities, which may not have given enough statistical power to corroborate. In addition, since coping and resilience are important mediators of subjective stress experiences ( Freire et al., 2020 ), we speculate that the lower ratios of stress reported in non-white participants in our study (75 vs. 81) may be because they are more accustomed to adversity and thereby more resilient ( Brown, 2008 ; Acheampong et al., 2019 ). Furthermore, ethnic minority students may face stigma when reporting mental health struggles ( Liu, C. H., et al., 2019 ; Lee et al., 2021 ). For instance, studies showed that Black/African American, Hispanic/Latino, and Asian American students disclose fewer mental health issues than white students ( Liu, C. H., et al., 2019 ; Lee et al., 2021 ). Moreover, the ability to identify stressors and mental health problems may manifest differently culturally for some minority groups ( Huang and Zane, 2016 ; Liu, C. H., et al., 2019 ). Contrary to our findings, other studies cited racial disparities in academic stress levels and mental well-being of students. More specifically, Negga et al. (2007) concluded that African American college students were more susceptible to higher academic stress levels than their white classmates ( Negga et al., 2007 ). Another study reported that minority students experienced greater distress and worse mental health outcomes compared to non-minority students ( Smith et al., 2014 ). Since there may be racial disparities in access to mental health services at the college level, universities, professors, and counselors should offer additional resources to support these students while closely monitoring their psychological well-being ( Lipson et al., 2018 ; Liu, C. H., et al., 2019 ).

While the COVID-19 pandemic increased stress levels in all the students included in our study, women, non-binary students, and upperclassmen were disproportionately affected. An overwhelming body of evidence suggests that the majority of college students experienced increased stress levels and worsening mental health as a result of the pandemic ( Allen and Hiebert, 1991 ; American Psychological Association, 2020 ; Husky et al., 2020 ; Patsali et al., 2020 ; Son et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Yang et al., 2021 ). Our results also align with prior studies that found similar subgroups of students experience disproportionate pandemic-related distress ( Gao et al., 2020 ; Clabaugh et al., 2021 ; Hunt et al., 2021 ; Jarrett et al., 2021 ; Lee et al., 2021 ; Chen and Lucock, 2022 ). In particular, the differences between female students and their male peers may be the result of different psychological and physiological responses to stress reactivity, which in turn may contribute to different coping mechanisms to stress and the higher rates of stress-related disorders experienced by women ( Misra et al., 2000 ; Kajantie and Phillips, 2006 ; Verma et al., 2011 ; Gao et al., 2020 ; Graves et al., 2021 ). COVID-19 was a secondary consideration in our study and survey design, so the conclusions drawn here are necessarily limited.

The implications of this study are that college students facing increased stress and struggling with mental health issues should receive personalized and specific mental health services, resources, and support. This is particularly true for groups that have been disproportionately impacted by academic stress and stress due to the pandemic. Many students who experience mental health struggles underutilize college services due to cost, stigma, or lack of information ( Cage et al., 2020 ; Lee et al., 2021 ). To raise awareness and destigmatize mental health, colleges can consider distributing confidential validated assessments, such as the PAS and SWEMWBS, in class and teach students to self-score ( Lee et al., 2021 ). These results can be used to understand how academic stress and mental well-being change over time and allow for specific and targeted interventions for vulnerable groups. In addition, teaching students healthy stress management techniques has been shown to improve psychological well-being ( Alborzkouh et al., 2015 ). Moreover, adaptive coping strategies, including social and emotional support, have been found to improve the mental well-being of students, and stress-reduction peer support groups and workshops on campus could be beneficial in reducing stress and improving the self-efficacy of students ( Ruthig et al., 2009 ; Baqutayan, 2011 ; Bedewy and Gabriel, 2015 ; Freire et al., 2020 ; Green et al., 2021 ; Suresh et al., 2021 ). Other interventions that have been effective in improving the coping skills of college students include cognitive-behavioral therapy, mindfulness mediation, and online coping tools ( Kang et al., 2009 ; Regehr et al., 2013 ; Molla Jafar et al., 2015 ; Phang et al., 2015 ; Houston et al., 2017 ; Yusufov et al., 2019 ; Freire et al., 2020 ). Given that resilience has also been shown to help mediate stress and improve mental well-being during the COVID-19 pandemic, interventions focusing on enhancing resilience should be considered ( Surzykiewicz et al., 2021 ; Skalski et al., 2022 ). Telemental health resources across colleges can also be implemented to reduce stigma and improve at-risk students' access to care ( Toscos et al., 2018 ; Hadler et al., 2021 ). University campuses, professors, and counselors should consider focusing on fostering a more equitable and inclusive environment to encourage marginalized students to seek mental health support ( Budge et al., 2020 ).

Limitations

While our study has numerous strengths, including using standardized instruments and a large sample size, this study also has several limitations due to both the methodology and sample. First, the correlational study design precludes making any causal relationships ( Misra and McKean, 2000 ). Thereby, our findings should be taken in the context of academic stress and mental well-being, and recognize that mental health could be caused by other non-academic factors. Second, the PAS comprised only the perception of responses to academic stress, but stress is a multi-factorial response that encompasses both perceptions and coping mechanisms to different stressors, and the magnitude of stress varies with the perception of the degree of uncontrollability, unpredictability, or threat to self ( Miller, 1981 ; Hobfoll and Walfisch, 1984 ; Lazarus and Folkman, 1984 ; Wheaton, 1985 ; Perrewé and Zellars, 1999 ; Schneiderman et al., 2005 ; Bedewy and Gabriel, 2015 ; Schönfeld et al., 2016 ; Reddy et al., 2018 ; Freire et al., 2020 ; Karyotaki et al., 2020 ). Third, the SWEMSBS used in our study and the data only measured positive mental health. Mental health pathways are numerous and complex, and are composed of distinct and interdependent negative and positive indicators that should be considered together ( Margraf et al., 2020 ). Fourth, due to the small effect sizes and unequal representation for different combinations of variables, our analysis for both the PAS and SWEMSBS included only summed-up scales and did not examine group differences in response to the type of academic stressors or individual mental health questions.

An additional limitation is that the participants in our study were a convenience sample. The testing service we used, prolific.co, self-reports a sample bias toward young women of high levels of education (i.e., WEIRD bias) ( Team Prolific, 2018 ). The skew toward this population was observed in our data, as 80% of our participants were women. While we controlled for these factors, the possibility remains that the conclusions we draw for certain groups, such as nonbinary students, ethnic/racial minorities, and men, may not be as statistically powerful as they should be. Moreover, our pre-screening was designed to recruit undergraduate level, English-speaking, 18–30-year-olds who resided in the United States. This resulted in our participant demographics being skewed toward the WEIRD bias that was already inherent in the testing service we used. Future research will aim to be more inclusive of diverse races/ethnicities, sexual orientations, languages, educational backgrounds, socioeconomic backgrounds, and first-generation college students.

Another limitation of our study is the nature of satisficing. Satisficing is a response strategy in which a participant answers a question to satisfy its condition with little regard to the quality or accuracy of the answer ( Roberts et al., 2019 ). Anonymous participants are more likely to satisfice than respondents who answer the question face-to-face ( Krosnick et al., 2002 ). We sought to mitigate satisficing by offering financial incentives to increase response rates and decrease straight-lining, item skipping, total missing items, and non-completion ( Cole et al., 2015 ). Concerns of poor data quality due to surveys offering financial incentives found little evidence to support that claim and may do the opposite ( Cole et al., 2015 ). On the other hand, social desirability bias may have influenced the participant's self-reported responses, although our anonymous survey design aimed to reduce this bias ( Joinson, 1999 ; Kecojevic et al., 2020 ).

Future Studies

Future studies should replicate our study to validate our results, conduct longitudinal cohort studies to examine well-being and perceived academic stress over time, and aim for a more representative student sample that includes various groups, including diverse races/ethnicities, sexual orientations, socioeconomic backgrounds, languages, educational levels, and first-generation college students. Additionally, these studies should consider examining other non-academic stressors and students' coping mechanisms, both of which contribute to mental health and well-being ( Lazarus and Folkman, 1984 ; Freire et al., 2020 ). Further explorations of negative and other positive indicators of mental health may offer a broader perspective ( Margraf et al., 2020 ). Moreover, future research should consider extending our work by exploring group differences in relation to each factor in the PAS (i.e., academic expectations, workload and examinations, and self-perception of students) and SWEMBS to determine which aspects of academic stress and mental health were most affected and allow for the devising of targeted stress-reduction approaches. Ultimately, we hope our research spurs readers into advocating for greater academic support and access to group-specific mental health resources to reduce the stress levels of college students and improve their mental well-being.

Utilizing two well-established scales, our research found a statistically significant correlation between the perceived academic stress of university students and their mental well-being (i.e., the higher the stress, the worse the well-being). This relationship was most apparent among gender and grade levels. More specifically, non-binary and second-year students experienced greater academic burden and lower psychological well-being. Moreover, women, non-binary students, and upper-level students were disproportionately impacted by stress related to the COVID-19 pandemic.

Studies regarding broad concepts of stress and well-being using a questionnaire are limited, but our study adds value to the understanding of academic stress as a contributor to the overall well-being of college students during this specific point in time (i.e., the COVID-19 pandemic). Competition both for admission to college ( Bound et al., 2009 ) and during college ( Posselt and Lipson, 2016 ) has increased over time. Further, selective American colleges and universities draw applicants from a global pool. As such, it is important to document the dynamics of academic stress with renewed focus. We hope that our study sparks interest in both exploring and funding in-depth and well-designed psychological studies related to stress in colleges in the future.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Review Board at Rutgers University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

GB and MB contributed to conceptualization, study design, IRB application, manuscript drafting, and revision. XZ participated in the conceptualization and design of the questionnaires. HB participated in subject recruitment and questionnaire collection. KP contributed to data analysis, table and figure preparation, manuscript drafting, and revision. XM contributed to conceptualization, study design, IRB application, supervision of the project, manuscript drafting, and revision. All authors contributed to the article and approved the submitted version.

This study was made possible by a generous donation from the Knights of Columbus East Hanover Chapter in New Jersey.

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.

Acknowledgments

The authors wish to thank Shivani Mehta and Varsha Garla for their assistance with the study. We also thank all the participants for their efforts in the completion of the study.

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Keywords: academic stress, well-being, college students, Perception of Academic Stress, Short Warwick-Edinburgh Mental Well-Being Scale, COVID-19

Citation: Barbayannis G, Bandari M, Zheng X, Baquerizo H, Pecor KW and Ming X (2022) Academic Stress and Mental Well-Being in College Students: Correlations, Affected Groups, and COVID-19. Front. Psychol. 13:886344. doi: 10.3389/fpsyg.2022.886344

Received: 28 February 2022; Accepted: 20 April 2022; Published: 23 May 2022.

Reviewed by:

Copyright © 2022 Barbayannis, Bandari, Zheng, Baquerizo, Pecor and Ming. 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: Keith W. Pecor, pecor@tcnj.edu

† These authors have contributed equally to this work and share first authorship

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.

Academic Stress in the Final Years of School: A Systematic Literature Review

Affiliations.

• 1 Department of Psychology, Centre for Emotional Health, Macquarie University, Sydney, Australia. [email protected].
• 2 Department of Psychology, Centre for Emotional Health, Macquarie University, Sydney, Australia.
• PMID: 32180075
• DOI: 10.1007/s10578-020-00981-y

Heightened academic stress in the final years of schooling is a common concern, yet little is known about how stress changes over time and what individual, school and family factors are associated with distress. We conducted a systematic review to examine the nature of distress in students in their final two years of secondary school. Sixty studies were eligible for inclusion. The main findings indicated severity of distress differed across the 17 countries sampled and measures used. There was some consistencies suggesting about 1 in 6 students experienced excessive distress. Female gender and anxiety proneness were consistently associated with increased distress, and freedom from negative cognitions with reduced distress. There was some evidence that individual characteristics (perfectionism, avoidance, coping, self-efficacy, resilience), lifestyle (sleep, homework), school, family and peer connectedness were associated with distress. Overall at-risk students can be predicted by theoretical models of anxiety and distress targeted with psychological interventions.

Keywords: Academic stress; Examinations; High stakes testing; Senior school; Test anxiety.

Publication types

• Comparative Study
• Systematic Review
• Adaptation, Psychological
• Anxiety Disorders / diagnosis
• Anxiety Disorders / psychology
• Cross-Cultural Comparison
• Educational Status*
• Self Efficacy
• Social Support
• Stress, Psychological / complications
• Stress, Psychological / diagnosis
• Stress, Psychological / psychology*
• Students / psychology*
• Test Anxiety Scale

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Advances and challenges in the detection of academic stress and anxiety in the classroom: A literature review and recommendations

• Published: 28 September 2022
• Volume 28 , pages 3637–3666, ( 2023 )

Cite this article

• Laura P. Jiménez-Mijangos 1 ,
• Jorge Rodríguez-Arce   ORCID: orcid.org/0000-0003-0289-7061 1 , 2 ,
• Rigoberto Martínez-Méndez 1   na1 &
• José Javier Reyes-Lagos 2   na1  

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In recent years, stress and anxiety have been identified as two of the leading causes of academic underachievement and dropout. However, there is little work on the detection of stress and anxiety in academic settings and/or its impact on the performance of undergraduate students. Moreover, there is a gap in the literature in terms of identifying any computing, information technologies, or technological platforms that help educational institutions to identify students with mental health problems. This paper aims to systematically review the literature to identify the advances, limitations, challenges, and possible lines of research for detecting academic stress and anxiety in the classroom. Forty-four recent articles on the topic of detecting stress and anxiety in academic settings were analyzed. The results show that the main tools used for detecting anxiety and stress are psychological instruments such as self-questionnaires. The second most used method is acquiring and analyzing biological signals and biomarkers using commercial measurement instruments. Data analysis is mainly performed using descriptive statistical tools and pattern recognition techniques. Specifically, physiological signals are combined with classification algorithms. The results of this method for detecting anxiety and academic stress in students are encouraging. Using physiological signals reduces some of the limitations of psychological instruments, such as response time and self-report bias. Finally, the main challenge in the detection of academic anxiety and stress is to bring detection systems into the classroom. Doing so, requires the use of non-invasive sensors and wearable systems to reduce the intrinsic stress caused by instrumentation.

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1 Introduction

“Stress is the biological and psychological response of the body to a stimulus (stressor) caused by an event, object, or person”, while, “anxiety is a state of agitation characterized by the anticipation of danger in which cognitive and physiological symptoms predominate” (Sierra, 2003 ). Anxiety can be a manifestation of stress and can also act as a stressor on the subject (Folkman, 2013 ; Owczarek et al., 2020 ). In psychology, a subject’s adaptive response determines their ability to predict upcoming stressful events and achieve self-control over the situation to cope with it adequately (Maturana & Vargas, 2015 ).

Stress and anxiety are present in daily life, and students are not exempt from experiencing these emotional states. Academic stress is “an adaptive systemic process, essentially psychological, on the part of students in school environments, to demands that in the subject’s assessment are considered stressful” (Toribio & Franco, 2016 ). Students at all educational levels deal with academic tasks that could generate stress during the teaching and learning process, such as a presentation, an exam, math exercises, among others.

Feelings of stress and anxiety are so prevalent that they have become normalized in students’ lives and, as a consequence, negatively affect academic performance (Baird, 2016 ; Wang et al., 2014 ). There exists a need to study the effects and consequences of stress and anxiety on students’ emotional state and academic performance (Berrío-García & Mazo-Zea, 2011 ).

In some subjects, the stress students are subjected to can serve as a driver to improve their results; however, perfomance in other subjects is negatively affected by stress (Leppavirta, 2011 ). Those who cannot adequately manage their stress response tend to show deficiencies in their grades and, in some cases, drop out (Corsini et al., 2012 ; Reyes-Carmona et al., 2017 ). For this reason, educational institutions must have tools and protocols to identify and manage the impact of stress on students and thus reduce the adverse effects on their academic performance.

UNESCO in their guide titled “Minding our mind during COVID-19” (Massod, 2020 ) specifies that students’ mental health must be a priority to provide an educational environment that meets basic human rights requirements. The authors decided to conduct this review because we did not identify any viable computing or technological platform which helps educational institutions to identify students with mental health problems when returning to face-to-face classes following COVID-19. In this way, this work identifies the challenges the academic community must face to propose practical and viable solutions to society.

The Organization for Economic Cooperation and Development (OECD) surveyed more than 500,000 students aged between 15 and 16 in 72 countries. Results indicate that more than 60% of the respondents feel stressed and anxious due to the fear of obtaining low grades, and more than 50% of the students suffer from anxiety when answering a test, even when they have studied and are prepared (Pascoe et al., 2020 ). Other studies have shown that students with higher stress levels are more likely to suffer from poor academic performance that may even lead them to drop out (Roso-Bas et al., 2016 ). Although the results indicate that stress should be identified and assessed in students as early as possible, doing so is not easy.

The most commonly used tools to identify, measure, and evaluate anxiety and stress states are interviews, questionnaires, and self-reports. However, the disadvantage of these tools is that their results depend on the subject’s response and the evaluator’s interpretation, which can lead to debatable results (Balanza-Galindo et al., 2008 ). The scientific community has proposed using sensors, electronic measurement systems, and data processing algorithms to acquire and analyze different physiological signals and biological markers to overcome this obstacle. The signal values or levels can be related to the body’s biological responses to stress and anxiety. Consequently, some authors have developed electronic diagnostic platforms that help experts identify stress and anxiety states in subjects in a quantifiable way, from a biological and psychological perspective, thus reducing subjectivity (García et al., 2016 ; Melillo et al., 2011 ; Rodríguez et al., 2020 ).

Papers on proposed biomarkers that correlate with the body’s physiological response to states of stress and anxiety have been published. Morera et al. ( 2019 ) identified that heart rate (HR) and blood pressure (BP) are the most commonly used biomarkers in stress and anxiety detection. In the same year, a similar study was published by Giannakakis et al. ( 2019 ). They determined that skin conductance response (SCR) and skin conductance level (SCL) can be used to identify stress in subjects. SCR and SCL were obtained by way of an electrodermal activity signal (EDA) sensor, also called galvanic skin response (GSR). Alberdi et al. ( 2016 ), analyzed more than 40 articles on stress in the workplaces. They observed that the main challenges for identifying workplace stress are the subjects’ privacy, ethical issues regarding the procedures, costs, efficiency, and the reliability of the systems, among others. Because of this, they propose improving sensors and measurement systems, emphasizing the advantages that monitoring and tele-assistance systems could bring over conventional stress and anxiety detection tools.

It is essential to mention that most of the studies conducted in order to identify stress and anxiety have been carried out in controlled environments, limiting the generalization of results. On the other hand, there are few studies on the measurement and evaluation of stress and anxiety in students in academic settings; therefore, there is no consensus among the scientific community on how to measure the impact stress and anxiety may have on students’ performance and the results obtained thus far.

In order to establish a current perspective of the identification of academic stress and anxiety, it is necessary to review published works in the area whose purpose has been the identification of these in academic environments. As an initial result of the bibliographic search, no scientific work establishing the progress and challenges in detecting stress and anxiety in academic environments in recent years was found.

This work presents a review of the current state-of-the-art regarding academic stress and anxiety detection. This review aims to identify relevant studies in the area and to discuss the procedures used and results obtained in academic settings. Current challenges and limitations are established, outlining future lines of research. An overview is provided to guide readers interested in the topic. The research questions that guided this work are:

Which psychological instruments, technological tools (data acquisition systems and sensors), and data processing techniques are most commonly employed in procedures to identify stress and anxiety in academic settings?

What are the limitations, challenges, and lines of research to be addressed in developing and implementing technological platforms to identify academic stress and anxiety in the classroom?

Data were collected from four databases: Google Scholar, Microsoft Academic, Science Direct, and PubMed .

The search was performed manually using combinations of the following words: stress, anxiety, academic, university students, undergraduate students; identification, detection, and recognition . This search yielded 4 , 762 , 360 manuscripts. In order to provide an overview of the advances and identify the challenges in the detection of academic stress and anxiety in the classroom, this systematic review presents the most recent results on this topic. In consequence, the search was limited to papers published in recent years (between 2011 and 2022) and aimed at identifying or detecting stress and anxiety in a university setting, resulting in 774 papers. The titles and abstracts of the papers were checked to avoid duplication, and the papers that mentioned the participation of university students were selected, resulting in 65 articles.

Finally, only papers that met the following inclusion criteria were considered: a) research articles published in journals or congresses, b) the aim of the study was the detection and recognition of stress and anxiety in university settings (regardless of the number of participants in the studies, as long as the participants were university students); and c) the research focused on the use of psychological tools or biological signals in stress and anxiety detection procedures. A total of 44 articles were selected. Table 1 summarizes the procedure for selecting the sample of articles that was analyzed.

Cooper’s method (Randolph, 2009 ) was used to formulate the research questions, the search methodology, the criteria to select the literature, and abstract the collected data to address current limitations. The characteristics of the anxiety and stress detection systems reported in the articles were identified; these characteristics are as follows: the primary tool employed in the stress and anxiety identification procedure (use of biological signal measurements, use of psychological tools, or a combination of both); the tasks and situations considered as stress generators (stressors); the tools used to perform the data analysis; and the phenomenon detected (stress, anxiety, stressful situations, and academic stress). These characteristics are summarized in Table 2 .

44 scientific articles were reviewed; 41.00% of them (18 articles) were written in the Americas, 29.50% (13 articles) in Europe, 25.00% (11 articles) in Asia, and 4.50% (2 articles) in Africa. The year with the fewest publications on the topic was 2014 with two publications, and the year with the most publications was 2020 with six published articles.

3.1 Tools used for the evaluation of subjects

Due to the psychological and biological components that prevail in stress and anxiety states, the detection procedures for these states can generally be classified into two groups: a) those based on the use of psychological instruments and b) those that rely on biological markers. It was found that psychological instruments were used to assess subjects in 20 articles (45.50%). In 15 articles (34.00%), measurements of physical signs, physiological signals, and biological markers were used. In 9 articles (20.50%), a combination of both tools (psychological instruments in combination with biological markers) was used.

3.1.1 Psychological instruments

The most commonly used psychological instruments are self-reports and interviews. In the first two articles, the responses were evaluated by using scales or indexes previously validated in the population. The disadvantage of this meethod is that the responses depend on the subject’s perception. Some studies conducted interviews to reduce bias in the results as a result of the respondent’s perception. The test responses were validated through concordance indexes such as Cronbach’s index ( α c ) (Cronbach, 1951 ). Table 3 shows the number of times (N) that each psychological instrument was found in the reviewed literature. In some studies, two or more psychological instruments were used to identify stress and anxiety states. In addition, Tables 4 and 5 summarize previous studies focused on the psychological-tool-based systems for academic stress and anxiety detection.

Table 3 demonstrates that, the most used psychological instrument is the State-Trait Anxiety Inventory (STAI). This test is popular because it is easy to apply and the results are easy to interpret. The STAI test was used in 9 (33.33%) of the 27 articles in which a psychological instrument was used. It is important to consider that one disadvantage of the use of psychological instruments is that each tool must be appropriate and valid for the population surveyed due to cultural, social, environmental, educational, and demographic differences between countries (Tsang et al., 2017 ). Then, it is reasonable to state that the instruments used in the literature should have been validated and appropriate for each case, explaining the lack of generalization and consensus regarding the use of the same instrument by the scientific community.

3.1.2 Biological measurements

Anxiety manifests itself by activating the autonomic, motor and central, endocrine, and immune, nervous systems (Kaniusas, 2011 ). On the other hand, stress activates the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). These manifestations modify the values and characteristics of various biomarkers and biosignals.

Physical signs are described as a measure of body alteration that can be directly perceived by an expert, evaluator, or medic; for example: muscle activity, pupil size, eye movements, blinking, semi-involuntary limb movements, facial expression, head movements and voice characteristics, volume, pitch and rate of speech (Giannakakis et al., 2019 ). On the other hand, physiological signals are directly related to vital bodily functions, and most of them respond to the SNS and the PNS of the human body. Some examples are electroencephalography (EEG), electrocardiography (ECG), heart rate (HR), heart rate variability (HRV), galvanic skin response (GSR), electromyography (EMG), photoplethysmography (PPG), blood volume per pulse (BVP), skin temperature (ST), oxygenation (SpO2), and respiration or breathing rate (Br). Finally, biomarkers measure hormone levels, such as cortisol and catecholamine (Sharma & Gedeon, 2012 ). In other words, physical signs and physiological signals are considered biological signals (biosignals). Hormone levels, and other biochemical measures are called biomarkers (Giannakakis et al., 2019 ; Morera et al., 2019 ). Tables 4 and 5 summarize the systems based on biological signals and biomarkers for academic stress and anxiety detection.

Tables 6 and 7 summarize the reviewed literature related to the use of biological signals and biomarkers to identify stress in academic settings. The sensors and electronic data acquisition systems are primarily commercial and were used to perform data acquisition, conditioning, and storage. In 18 papers, commercial sensors and platforms were used as data acquisition systems along with audiovisual software for task presentation (stressors); in 4 papers, the use of custom-made GSR sensors is reported, and in 1 paper, a portable device (LG Smartwatch) was used for biosignal measurement. Some of the most commonly used electronic devices are: Ates Medical Easy ECG Pocket, Biopac EDA100C, Biopac ECG100C, Respiration Monitor Belt, Easycap EEG Recording Cap, Polar V800 HR monitor, Lafayette Instrument Flicker Fusion Control Unit Model 12021, Sallivette sampling devices for cortisol, and PAXgene blood RNA tubes for collecting blood samples.

Since stress and anxiety generate a biological response due to the subject’s primary instinct for survival and well-being, biosignals and biomarkers are reliable indicators of the presence of anxiety and stress. Technological developments have improved the accuracy of subject measurements, data extraction, and analysis of signal characteristics. Moreover, it is feasible to use portable and wearable devices or mobile applications (Egilmez et al., 2017 ).

According to the literature review, HRV analysis using ECG is the most commonly used physiological indicator of stress, followed by the GSR signal. In third place, measurements of hormone levels such as cortisol and cytokines were taken (see Table 6 ). The raw ECG signal and HR values were minimally used. The least used signals were EEG, SpO2, and Br. Some authors reported HRV signals and cortisol levels as reliable indicators of stress states (Morera et al., 2019 ). In addition, the use of the time and frequency of HRV signals as biomarkers for stress detection was analyzed due to their reliability (Castaldo et al., 2015 ). Other signals have also been analyzed as stress indicators, physical signs such as changes in pupil size, voice, or physiological signals such as temperature, respiration rate, and photoplethysmography (Giannakakis et al., 2019 ).

Thus, it can be summarized that the previously mentioned biological measurements are relevant in the context of academic stress identification given that they quantify neuroautonomic changes associated with the onset of stress, such as physiological time series (e.g., HRV and GSR) (Dalmeida & Masala, 2021 ; Pop-Jordanova & Pop-Jordanov, 2020 ). Additionally, changes in the concentration of biomarkers such as hormones and cytokines are also relevant since they are related to neuroendocrine-immune modifications produced by stress (Menard et al.,  2017 ).

3.1.3 Combined systems

Tables 8 and 9 show systems based on biological signals, biomarkers and psychological tools for academic stress and anxiety detection. In these studies, the authors propose using a combination of psychological tools and biosignals to correlate scores of the indices and questionnaires of the psychological tools with the behavior of the biosignals in anxiety and stress situations. A positive correlation between questionnaire scores and biosignal statistical values was reported (Honda et al., 2013 ; Hoskin et al.,  2013 ; Barbic et al., 2020 ). However, although some authors report favorable results for stress and anxiety detection, in most cases, their experiments were conducted in controlled environments using laboratory activities (stressors), limiting the possibility to generalize their results to academic environments. Also, it is impossible to have a perspective of the impact of stress and anxiety on students’ performance.

3.2 Application of detection system

In the articles reviewed, data acquisition and analysis, experiments, measurements, surveys, interviews, questionnaire, and sampling were carried out. The most general aspects of data acquisition and analysis methods are presented below. This information can be used for further studies related to the identification of academic stress and anxiety.

3.3 Sample characteristics

In the systems based on biological signals and markers, the minimum population was 6 students, and the maximum was 110; the mean sample size was 48 subjects. In the systems based on psychological tools, the minimum number of participants was 19 students, and the maximum was 1,400; the mean sample size was 353 subjects. Likewise, in the systems with a combination of biological signals and markers with psychological tools, the minimum population was 12, and the maximum was 80, with 46 subjects on average.

University degrees in which the studies were conducted are as follows: Medical Sciences in 22 articles (50.00%), Engineering in 11 studies (25.00%), Education in 3 (7.00%), and Psychology in 1 (2.00%). In 7 papers (16.00%), the area of professional training to which the students belonged was not reported. All subjects in the reviewed studies were students except in one case (Hoskin et al., 2013 ), in which academic staff also participated.

3.4 Stressors

Situations, people, or objects that stimulate physiological and psychological stress responses are called stressors. Various academic activities, such as school workload, examination and evaluation periods, interaction with teachers, and even work periods in hospitals were used as stressors. Non-academic factors such as students’ physical and mental health, demographic and social situation, logical and, cognitive activities, and physically uncomfortable (stressful) situations such as classroom temperature or putting a hand in a bucket of ice were used as stressors. Additionally, playful and competitive activities such as singing, playing video games, watching pictures and eating were also used as stressors. The purpose of applying stressors was to promote changes in the measured signals or to obtain responses in situations that could be differentiated from each other, before and after an evaluation. These changes were used as indicators of a response to stress and/or anxiety.

Although the stressors used correspond mostly to real-life situations and, given that biological signals were measured and questionnaires were answered in controlled conditions, it is considered that anxiety and stress conditions were also controlled. In all but three papers (Ramírez-Adrados, Fernández-Martínez, et al., 2020 ; Ramírez-Adrados, Beltrán-Velasco, et al., 2020 ; Morales-Fajardo et al., 2022 ), measurements were taken during the students’ university exams. Then, post-experiment analysis of the signals obtained was carried out. Table 10 shows a summary of the stressors used in the reviewed papers. N indicates the number of times each stressor was used, either individually or in combination with others.

3.5 Data analysis for stress and anxiety recognition

Once the signals, markers, and questionnaire responses had been acquired, data were processed and analyzed to identify stress and anxiety states. The reviewed works use two main analysis methods: statistical analysis and pattern recognition.

The calculation of statistical parameters was used in 32 (73.00%) of the articles reviewed. Measures of central and non-central tendency, dispersion, measures of shape, statistical moments, analysis of variance (ANOVA), and two-dimensional measures such as a correlation between variables were reported to analyze the behavior of the data obtained.

On the other hand, pattern recognition classifies objects into several categories or classes. Depending on the application, these objects may be signals resulting from measurements and classified into patterns (Theodoridis & Koutroumbas, 2003 ). The algorithms for pattern recognition used in the academic environment, according to the documentation reviewed, are the following: logistic regressions, linear regressions, multiple regressions, and multivariate regressions (Stijnen & Mulder, 1999 ).; These algorithms were used in five articles (11.50%) as part of the statistical analysis. The K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) (Wu et al., 2007 ) algorithms were used in four papers (9.00%). Linear Discriminant Analysis (LDA) (Fisher, 1936 ) and Decision Tree (DT) (Rokach & Maimon, 2007 ) algorithms were used in three papers (7.00%). Random Forest (RF) (Ho, 1995 ) algorithms were used in two papers (4.50%). Whether individually or in combination with other tools, the following algorithms were used for data analysis in one article: Genetic Algorithm (Whitley, 1994 ); Gaussian Processes (Rasmussen, 2004 ), and Fuzzy Logic (Mockor et al., 1999 ). One of the most commonly used measures of the performance of a pattern-recognition algorithm is accuracy. The maximum accuracy achieved for classifying the presence of anxiety states was 100.00%, and the minimum accuracy reported was 77.18% (see Table 11 ).

3.6 Detected phenomenon

The objectives of the articles analyzed can be separated into five groups according to the variables of interest related to academic stress and anxiety.

Four articles set out to analyze stressors or stressful tasks to determine whether students perceived them and which ones were most representative.

Eight papers focused on assessing the consequences of stress and anxiety, such as physical and psychological health problems, poor academic performance, and/or social adjustment problems.

Nine papers were about anxiety recognition in university students using daily activities as stressors, such as health, social and work-related issues.

Twelve articles identified stress using academic activities similar to those in an academic setting as stressors.

Twelve papers were centered on detecting stress related to activities in the non-academic lives of university students.

The variety of phenomena detected indicates that both academic stress and anxiety and their manifestations, sources, and consequences are of interest to researchers.

4 Discussion

This review provides a current perspective on identifying academic stress and anxiety in students to outline the advances and challenges facing the scientific community.

Forty-four papers were reviewed; 41.00% were written in the Americas and 29.50% in Europe. These studies focus mainly on analyzing the detection of stress and anxiety to improve students’ academic performance to reduce absenteeism, failure, and dropout rates (the studies were primarily conducted in public institutions). On the other hand, 25.00% of the studies were conducted in Asia to reduce the prevalence of depression in students subjected to strict academic systems and reduce suicide rates. Finally, 4.50% of the studies were conducted in Africa, which shows interest in improving students’ coping strategies when experiencing stress and anxiety as a result of academic activities. In general, the studies focused on the implementation or evaluation of a platform aimed at providing non-invasive, inexpensive, portable, and/or reliable stress detection systems. In all cases, such platforms were viewed as support tools for the experts.

On the other hand, the assessment of stress and anxiety from questionnaires depends on students’ self-perception. In many cases, the internal consistency of questionnaire items is not representative of the stress or anxiety of the subject (Eack et al., 2006 ). Furthermore, although questionnaires can be answered by many students simultaneously, they cannot be answered while conducting academic tasks. The evaluation of responses takes time, so the information on stress and anxiety extracted from questionnaires may not be immediate. To overcome the disadvantage of using some psychological instruments with university students, some authors propose using electronic measurement platforms which rely on biosignals to reduce the subjectivity of self-reports or questionnaires.

There is no conclusive data on which sensors/biomarkers are more suitable for academic environments. In the literature review, the most employed sensors are ECG sensors used to detect HRV signals. Such sensors can be placed on the arms, but they perform best when the electrodes are placed on the chest (Sioni & Chittaro, 2015 ). Although not reported, the placement site could generate a recognition results bias of stress and anxiety states. Studies in other contexts have shown that stress levels in subjects are increased due to the presence and placement of the sensors before starting the experimental tests. For example, most women find having the sensors placed on the chest area uncomfortable, invasive, and stress-inducing (MacLean et al., 2013 ).

On the other hand, there are no conclusive results on the used of other biological signals whose sensor placement is considered less invasive in an academic setting. For example, the photoplethysmography signal in which the sensor can be placed on the wrist or fingertip under specific conditions is considered appropriate for detecting HRV signals (Selvaraj et al., 2008 ). Then, the objectives ought to be the creation of stress identification platforms that use non-invasive methods to reduce the stress induced by the placement and presence of sensors, and the identification of the most suitable biosignals and/or biomarkers for identifying anxiety and stress states in an academic environment in real-time.

Regarding the characteristics of the sample used in the reviewed papers, it was identified that the systems involving biological measurements have the smallest sample size, with a minimum of 6 students (Desai et al., 2020 ). Although it is difficult to define a minimum sample size to protect against skewness, some authors recommend a sample size equal to 30 (Fay & Gerow, 2013 ). Moreover, when using large sample sizes (>30), the distribution of the data can be ignored (Ghasemi & Zahediasl, 2012 ). In contrast, small sample sizes could render any experiment much less powerful. In general, 25% of the reviewed papers used a sample size of fewer than 30 subjects. Studies in which the sample size is smaller than 30 subjects limit the generalization and comparison of results. However, sample size was not an exclusion criterion for the reviewed papers. Thus, it is suggested that future studies consider sample size as a main character in the experimental protocol in order that the results are statistically significant.

Regarding the identification of the psychological instruments, technological tools (data acquisition systems and sensors), and data processing techniques most commonly employed to identify stress and anxiety in academic settings, the results indicate that there is still no conclusive evidence. Various stress induction protocols, psychological tools (either alone or in combination with data acquisition systems, in the case of biosignals), and various data analysis techniques have been used to identify stress and anxiety in the academic environment. The results do not allow for the identification of any consensus among the scientific community on the use of protocols, psychological tools, sensors for data acquisition, and data analysis techniques. The most commonly used tool is psychological questionnaires used alone or in combination with biosignals. This was the case in 29 (66.00%) of the 44 studies reviewed.

The STAI is the most widely used psychological instrument in the academic setting. It was used in 10 (28.00%) studies of the reviewed papers. Stressful situations commonly elicit feelings of anxiety in subjects (Jamieson et al., 2013 ). Moreover, anxiety is considered as an emotional response to a subject’s perception of a stressful activity or experience (Koelsch et al., 2011 ; Hook et al.,  2013 ). A correlation between STAI score and cortisol level has been determined; r = 0.509 (Kurokawa et al., 2011 ) and r = 0.375 (Honda et al., 2013 ). Typically, higher cortisol levels correlate with higher STAI scores. The advantages of the STAI assessment are the easy interpretation of results and the short time required to answerall the questions on the form. The disadvantage of cortisol level analysis is that the process requires specialized and expensive equipment. The test requires blood or saliva samples (for the latter, the sampling and analysis process takes at least 15 minutes; otherwise, the results are not valid). Thus, using the STAI questionnaire seems a viable option for stress and anxiety identification.

Regarding the use of biosignals and biomarkers during the stress screening procedure, ECG, GSR, EEG signals, and cortisol tests in saliva and blood were reported in 19 (79.00%) of the 24 studies. The most commonly used feature is HRV, reported in 8 (33.50%) studies. The use of this signal reached an accuracy of more than 90.00% (Castaldo et al., 2019 ; Melillo et al., 2011 ) in the classification and identification of stress in the academic setting. However, there is evidence that the GSR signal could be used in conjunction with pattern recognition models for the identification of academic stress with an accuracy more than 95.00% (Durán Acevedo et al., 2021a ; Rodríguez et al., 2020 ). New emerging methods, such as rPPG in combination with demographic data or e-nose, achieved an accuracy over 96.00% (Durán-Acevedo et al., 2021b ; Morales-Fajardo et al., 2022 ).

Data analysis for identifying anxiety and stress states is mainly performed using statistical analysis techniques. However, in recent years, pattern recognition algorithms have been increasingly used in academic settings. In most cases, although data acquisition can be carried out on-site and in a short time, data analysis is still not done immediately, i.e., in most studies, data is first acquired, and further analysis is then performed.

Detecting stress and anxiety in college students leads to the possibility of correlating these conditions with predictable or treatable physical and mental illnesses based on knowledge of their stress status. Academic performance can also be improved by teaching coping strategies to students whose stress level has been detected as a detrimental factor. Some authors recognize stressors and triggers of anxiety related to the academic environment. However, without conclusive results and in some cases due to the sample size, these cannot be generalized. The best accuracy (100%) reported was achieved by the SVM algorithm with a GSR signal, but the characteristics used for the algorithm are not reported. The most used signal is ECG; and the one with the second best performance (99.50%) was reported in those studies in which this signal was used in combination with Fuzzy Logic.

Relevant studies highlight the potential benefits of physiological data and psychological scales in developing tools that provide learners with immediate support as they learn. For example, a previous study combined single-item self-report questionnaires with electrodermal activity data to examine how subjects’ perceptions and physiological responses interact during collaborative learning and how they affect academic performance (Dindar et al., 2020 ). That study found that physiological synchrony, measured by electrodermal activity, was significantly positively associated with cognitive change in high school students in self-regulated learning. Hence, opportune detection of academic stress or anxiety by physiological or psychological-based systems could support the development of psycho-pedagogical interventions to improve student learning.

Finally, the limitations can be summarized as follows. These limitations should be considered in future research:

Small sample sizes for systems based on biosignal measurement.

Lack of conclusive results on the use of biosignals in academic contexts and the use of non-invasive sensors to obtain physiological signals.

Not all studied performed the tests in academic spaces or real situations. It is preferable to avoid controlled environments or laboratory tests (stressors).

Instrumentation of students is time-consuming and leads to bias in measurements due to the possible stress induced by the placement of sensors or participation in the experiment. Consequently, alternatives for signal acquisition and measurement should be proposed, or pre-test and post-test conditions should be considered in the experimental protocols.

Bias in results due to students’ self-perception in filling-out self-reports and inventories.

Need to develop and validate specific psychological tools for student populations in academic settings.

The results of academic stress and anxiety detection are not immediate, limiting the possibility of providing feedback to students on specific academic activities.

4.1 Implications

Some studies have shown that using psychological instruments in combination with the measurement of biological signals is an adequate strategy for identifying academic stress and anxiety. However, it is impossible to have conclusive results for which signals have greater feasibility in academic settings. In addition, there are no conclusive results on the impact that stress and anxiety states have on students’ academic life. The authors suggest conducting research focused on identifying the correlation between psychological instruments and biosignals and markers in real academic environments to avoid bias in the results derived from students’ perception when answering inventories or self-reports in controlled spaces.

Regarding the conditions for data acquisition, most of the stressors employed were laboratory activities that attempted to replicate academic work in the school term; assessment periods (exams), and students’ daily life contexts. Some authors report stress and anxiety induction protocols that include non-academic tasks (Egilmez et al., 2017 ), or apply the punishment-reward phenomenon to the stress-detection task (Rodríguez et al., 2020 ). Stress is a multifactorial process; thus, its origin outside controlled environments is virtually unidentifiable. The detection of stress and anxiety during academic tasks in a specific place with immediate results has not been reported. In two studies, measurements were taken during oral presentations, but the data analysis was performed with recorded signals, leading to a lack of immediate results. Whether it is a specific academic task such as an oral presentation or a conversation with a professor, there is evidence that the stress response and anxiety are prevalent in advance of the occurrence of the event (Lin et al., 2019 ; Sánchez et al., 2019 ). In addition, it is necessary to identify academic activities that could represent the classification of stress and anxiety states within the classroom.

The strategies for data analysis were divided into two main groups: descriptive statistical analysis and pattern recognition. The most commonly-used analysis by the authors was statistical analysis, and the most-commonly analyzed parameters were the correlation between variables and mean value (Honda et al., 2013 ; Waqas et al., 2015 ). On the other hand, some authors employed pattern recognition methods. The review showed that the most used algorithms are regressions and machine learning algorithms such as KNN and SVM. Using these in combination was also reported (Rodríguez et al., 2020 ). The accuracy reached by the pattern recognition algorithms was 100.00% in the best case (Durán Acevedo et al., 2021a , b ), and 77.18% in the worst case (García et al., 2016 ). It can be proposed that the accuracy of the pattern recognition algorithm on stress and anxiety detection lies between these values. It is worth mentioning that while more frequently used features are temporal, frequential, and nonlinear HRV signals in stress detection in academia; statistical measures, which are less complicated to compute, of the GSR signal yield better accuracies; above 95.00% (Durán Acevedo et al., 2021a ; Rodríguez et al., 2020 ; Santos et al., 2011 ). In addition, GSR sensors are less invasive, given that they are placed on the fingers; all this could indicate the need to explore the potential of the GSR signal in stress and anxiety detection. However, this review shows that the resources of pattern recognition science are not widely exploited in academia; one reason for this could be the expertise needed for its implementation. Therefore, future work should propose using pattern recognition techniques to develop and implement measurement platforms to identify anxiety and stress states in real time and provide feedback to students and teachers to improve the learning environment in the classroom.

The presence of chronic stress and anxiety in students increases the likelihood of developing illness (Mejía-Rubalcava et al., 2012 ), sleep problems (Waqas et al., 2015 ), depression problems (Dube et al., 2018 ; Lin et al., 2019 ; Mahroon et al., 2017 ; Romo-Nava et al., 2019 ), social and environmental maladjustment (Sánchez et al., 2019 ), anticipatory anxiety before stressful events (Ramírez-Adrados, Fernández-Martínez, et al., 2020 ; Ramírez-Adrados, Beltrán-Velasco, et al., 2020 ; Honda et al., 2013 ; Melillo et al., 2013 ), and neurosis (Chen et al., 2020 ). However, the presence of episodic stress reported by study participants served in some cases to improve academic performance (Barbic et al., 2020 ; Reyes-Carmona et al., 2017 ). This is in contrast to cases where the presence of anxiety diminished participants’ cognitive performance, and caused episodic stress (Hoskin et al., 2013 ; Pozos-Radillo et al., 2014 ; Martínez-Otero, 2014 ; Nepal et al., 2018 ). On the other hand, some studies report that participants’ ability to cope with the stress response had a positive impact on their performance and adaptation (Bati et al., 2013 ; Rivas-Acuná et al., 2014 ). While chronically experienced stress and anxiety are precursors of physical and mental illness, when they appear episodically, students’ lack of coping skills negatively affects their cognitive performance. Then, the use of stress and anxiety detection systems in the academic environment and the evaluation of students’ academic performance implies aiming to develop accompanying strategies to improve students’ coping skills and the intervention of educational institutions in the process.

4.2 Challenges ahead

The most widely used signal for stress detection in academia is HRV. Cortisol has been used in correlation with the STAI questionnaire. Commercial measurement systems, the cost of which is not affordable for many educational institutions, have also been reported. The stress that the placement of the sensors can induce has been commented on, making it challenging to identify physiological signals that can serve as indicators of stress and anxiety using less invasive sensors and developing systems which are more accessible to institutions. In addition to HRVs, the GSR signal appears to be an excellent candidate to address this challenge.

In the reviewed studies using psychological instruments, although most of them were performed under actual stress conditions, the questionnaires could not be answered at the same time as academic activities. On the other hand, in the biosignal-based measurement systems, only three experimental tests were developed during the performance of academic activities (doing an exam) (Ramírez-Adrados, Fernández-Martínez, et al., 2020 ; Ramírez-Adrados, Beltrán-Velasco, et al., 2020 ; Morales-Fajardo et al., 2022 ). The rest were performed under controlled stress conditions. Besides, in all the studies reviewed, data analysis was performed offline. For sensing systems to be helpful in an academic context, they need to be brought into students’ daily activities, wearable measurement systems, optimization of recognition algorithms, mobile applications, and integration of sensing tasks could be helpful to address this. Although questionnaire response and data evaluation takes a long time, almost all students have access to smart mobile devices. Then, an integrated project with a short stress and anxiety screening inventory targeted at students and an application for questionnaire response and data analysis may result in a portable, reliable, low-cost stress and anxiety screening system. These may reduce response time and may be suitable for the characteristics of the student population.

Encouragement of teachers’ interest in detecting stress and anxiety among students to improve their quality of life and academic performance can reduce failure rates and dropout, while increasing the chance of success in the learning process. In addition, it would contribute to the reduction of stress-related health problems and it could increase the capacity for self-control and adaptation in social interaction. Shortening the time it takes to deliver the results of systems through online-data analysis and their incorporation in the classroom is one of the main challenges, along with the design and implementation of stress management and coping strategies.

4.3 Limitations of the study

Since only research published in indexed journals and conference proceedings was reviewed, other works that could enrich this analysis and these conclusions are likely outside this inclusion criterion. The characteristics analyzed aimed to answer two specific questions in the field of engineering. However, some other questions or characteristics may be analyzed for other areas of knowledge.

The limitations found in previous studies are summarized:

There are no conclusive results on the use of stress and anxiety detection systems in academia.

A lack of consensus regarding which anxiety and stress detection tools are most appropriate in the academic setting: psychological instruments or biological signals.

The necessary conditions and appropriate activities for academic stress and anxiety testing in the academic setting are unclear.

No unified guidelines on experimental protocols have been established in academia to acquire and measure biological signals.

5 Conclusions

A systematic search for information related to the detection of academic stress and anxiety was carried out, which resulted in the analysis of 44 scientific articles (published between 2011 and 2022). This analysis allowed us to identify the current limitations in this topic and identify the advances and challenges in the detection of students’ stress and anxiety in academic environments.

Systems based on psychological instruments are the most widely used in anxiety and stress detection among university students. However, the use of psychological instruments suffers from a lack of reliability due to their dependence on the students’ self-perception and the need for design and validation for a specific population.

The HRV is the most commonly used parameter in stress detection systems based on biosignal measurements. However, its effectiveness depends on experiments in controlled environments and sensor placement, as both factors are stress triggers, generating a bias in the results. Nevertheless, some results show that the use of other signals, such as PPG and GSR, could serve as biological signal patterns to be used in academic environments, but more studies are needed to obtain conclusive outcomes.

Data analysis in the academic environment is mainly performed using statistical tools; only nine papers use pattern recognition without statistical analysis. In general, these studies achieve an accuracy between 80.00% and 100.00% in academic stress and anxiety detection by using biosignals in combination with pattern recognition algorithms in controlled environments. Therefore, the authors suggest exploring the use of pattern recognition in academic environments for academic stress and anxiety identification.

Limitations have been presented, and lines of work have been suggested to address the issues in the reviewed papers. Bringing electronic platforms and wearable systems into the classroom in order to identifying stress and anxiety in the short term in real situations is still a challenge.

The interest of educators in the detection of stress and anxiety among students aims to improve students’ quality of life, increase coping and self control abilities, and achieve greater adaptability to social interaction and context. Thus, using physiological data and psychological scales to identify critical moments that trigger stress or anxiety could aid in developing interventions that can provide learners with temporary support as they struggle with a challenge in terms of a specific problem. Shortening the response time of the detection systems and the strategies designed and implemented by educational institutions’ staff can lead the way to overcoming it.

Data availability

Not applicable.

Code availability

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Acknowledgements

The authors would like to thank the Autonomous University of the State of Mexico (UAEM) for the financial support of this project (6210/2020CIB). Jiménez-Mijangos acknowledges the Mexican National Council for Science and Technology (CONACYT) for the scholarship CONACYT-CVU 351022 to carry out this research. The authors also thank Professor Liam J. Curtis for his support in translating the document into English.

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Rigoberto Martínez-Méndez and José Javier Reyes-Lagos contributed equally.

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Facultad de Ingeniería, Universidad Autónoma del Estado de México, Avenida Universidad, Toluca, 50100, Estado de México, México

Laura P. Jiménez-Mijangos, Jorge Rodríguez-Arce & Rigoberto Martínez-Méndez

Facultad de Medicina, Universidad Autónoma del Estado de México, Paseo Tollocan, Toluca, 50180, Estado de México, México

Jorge Rodríguez-Arce & José Javier Reyes-Lagos

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jiménez-Mijangos and Rodríguez-Arce. The first draft of the manuscript was written by Jiménez-Mijangos and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Jiménez-Mijangos, L.P., Rodríguez-Arce, J., Martínez-Méndez, R. et al. Advances and challenges in the detection of academic stress and anxiety in the classroom: A literature review and recommendations. Educ Inf Technol 28 , 3637–3666 (2023). https://doi.org/10.1007/s10639-022-11324-w

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Received : 27 December 2021

Accepted : 04 September 2022

Published : 28 September 2022

Issue Date : April 2023

DOI : https://doi.org/10.1007/s10639-022-11324-w

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