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Article

Exploring Academic Stress and Coping Experiences Among University Students During the COVID-19 Pandemic

by
Xin Ren
*,
Valerie A. Sotardi
and
Cheryl Brown
Faculty of Education, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(3), 314; https://doi.org/10.3390/educsci15030314
Submission received: 1 December 2024 / Revised: 28 February 2025 / Accepted: 28 February 2025 / Published: 4 March 2025
(This article belongs to the Special Issue Stress Management and Student Well-Being)
Versions Notes

Abstract

:
The COVID-19 pandemic has significantly impacted and disrupted higher education worldwide, creating unprecedented challenges for university students. In New Zealand, universities operated under varying pandemic restrictions, requiring students to frequently transition between online and in-person learning. This distinctive context provides a valuable opportunity to examine students’ academic stress and coping strategies during these transitions. Grounded in the transactional model of Stress–Appraisal–Coping, this research investigates sources of academic stress, coping strategies, and their effectiveness among university students during the pandemic. A mixed-methods approach was employed, with 193 university students completing an online survey incorporating qualitative and quantitative components. The thematic analysis results indicate eight major sources of academic-related stress and three types of coping strategies. Hierarchical regression analysis revealed that proactive and assistance-seeking strategies were associated with effective stress management, while avoidant strategies were linked to poorer outcomes. However, the study is limited by a low response rate (39.68%), which may affect the generalisability of findings. The results underscore the importance of fostering adaptive coping mechanisms in university settings and highlight the need for targeted institutional support to enhance student wellbeing and resilience in the post-pandemic academic landscape.

1. Introduction

Stress can be a significant hindrance to university life, especially for students who lack effective means of coping. Indeed, there is a growing risk of tertiary students experiencing chronic stress (Bewick et al., 2010; Khawaja et al., 2013). Along with obstacles related to learning and assessment, students may have challenges in dealing with their personal concerns, including financial, social, and family obligations (Bulo & Sanchez, 2014; Hurst et al., 2013; Pascoe et al., 2020). The outbreak of COVID-19 and the amplification of stress have attracted researchers worldwide (O’Connor et al., 2021; Rossell et al., 2021). Emerging studies have primarily addressed mental health crises in the general public and specific populations directly exposed to COVID-19, such as frontline workers and infected patients (Bareeqa et al., 2021; Huang & Zhao, 2020; Zhang et al., 2020). Aside from the general public, university students are also a vulnerable group affected by closures, changes in study modes, isolations, and lockdowns. According to UNESCO (2021), billions of world learners were forced to suspend their academic pursuits during the peak period of the pandemic in 2020. Today, students around the globe are still coping with the repercussions of the pandemic, especially university students, who are detrimentally affected by the COVID-19 movement restrictions. Restrictions such as regional lockdowns and institution closures may result in severe psychological stress, under which students may feel isolated and uninvolved with their learning community (Resch et al., 2023). Being separated from their friends and families may also result in students experiencing loneliness. Further, it has become difficult for students to access institutional resources or adapt to the ever-changing nature of learning between online and on-campus modes.
In addition to the pandemic and its unique challenges, students continue to face most of the typical pressures inherent to the education system. The pressure may result from heavy workloads for assignments and exams and personal issues related to health, family, and finances, and may lead to severe consequences. Studies have shown that individuals with high levels of stress and unhealthy coping behaviours are at greater risk of learning difficulties, academic dropout, and mental health issues, including anxiety, burnout, and suicidal intentions (Böke et al., 2019; Shute & Slee, 2016). Amid the pandemic situation, students are experiencing common sources of stress and those specific to COVID-19. Researchers (Alonzi et al., 2020; Ghebreyesus, 2020) found that students who experienced stress prior to the pandemic were more likely to develop mental health issues during the pandemic. According to Haikalis et al. (2022), COVID-related academic challenges are causing university students to experience significant increases in stress and depression symptoms.
In addition, students encountered difficulty switching between different modes of learning during the pandemic. Students might have experienced increased stress levels as they were required to adjust to abrupt transitions between campus and online learning and encountered obstacles along the way (Besser et al., 2022). The online and blended learning approach facilitated by the current public health crisis may become the new norm for higher education. Thus, an integrated support system for university students in the post-pandemic era is essential. Nevertheless, it is difficult to identify the most effective systems and strategies to support students due to the lack of research and evidence regarding university students’ stress and coping experiences. The study focuses on higher education in New Zealand, similar to many other countries, where COVID-19 restrictions have intermittently changed due to pandemic surges. This study seeks to provide detailed information for education providers worldwide regarding university students’ psychological distress during the pandemic, and explore the relationships between stressors, coping strategies, and coping effectiveness.

1.1. Student Stress During the COVID-19 Pandemic in Higher Education

According to seminal research by Lazarus and Folkman (1984), stress emerges when individuals appraise environmental demands exceeding their capability to manage. Cognitive appraisal, which refers to one’s perception of an event and its significance, is central to the stress response process and is shaped by personal and situational factors (Folkman et al., 1986). Personal factors include commitments and beliefs that shape individuals’ evaluations of an event, while situational factors encompass novelty, predictability, uncertainty, and ambiguity (Smith & Kirby, 2009). Research has shown that university students are prone to psychological distress, as university is characterised by numerous uncertainties and unpredictable academic and social changes (Acharya et al., 2018; Larcombe et al., 2022). When looking at tertiary education, any potential changes in students’ academic and personal life domains may increase stress.
From a transitional perspective, students might experience increased stress levels due to the COVID-19 pandemic, as they had to adjust to abrupt transitions between campus and online learning and encountered obstacles along the way. According to Nicholson (1984), transition processes involve four major phases: (a) Preparation; (b) Encounter; (c) Adjustment; and (d) Stabilisation. This process starts with the preparation phase, during which individuals build expectations and motivation before encountering a new environment. With the outbreak of COVID-19 followed by swift lockdown and university closure policies in New Zealand, students were left with little time to prepare for the disruption of learning, which could have posed risks to the transitional process. As a result, common academic stressors were exacerbated by uncertainties surrounding course delivery, access to academic resources, and disruptions to learning routines (Aristovnik et al., 2020). Research indicates that students who were unable to adapt to these changes reported higher levels of distress and decreased academic motivation (Sundarasen et al., 2020).

1.2. How Do University Students Cope with Stress?

The process by which individuals strive to avoid or reduce harm and loss from psychological distress is defined as coping (Carver & Connor-Smith, 2010). The regulation encompasses emotional and behavioural perspectives (Skinner & Zimmer-Gembeck, 2016). The methods of coping could vary among individuals and are influenced by the environment, personal traits, and sociocultural background (Aldwin, 2009). Therefore, some individuals may focus on resolving the problem when stressed, whereas others may attempt to alleviate their negative emotions. Researchers have examined how students cope and the relationship of this to stress levels and academic performance (Coiro et al., 2017; Garriott & Nisle, 2018; Pascoe et al., 2020; Shankar & Park, 2016). However, there has been little discussion about the perceived effectiveness of coping strategies so far. University students cope with problematic situations or stressful feelings, but not all attempts are effective. Riddell et al. (2018) reported that substance use and avoidance might provide short-term relief, but they cannot address the stressor. Aldwin (2009) suggested that when measuring the effect of coping, the correlation between coping and a direct outcome is not enough to understand the comprehensive picture. Therefore, a regression-based modelling approach was used in the present study to understand coping mechanisms better. Moreover, it is noteworthy that the perception of stress and relevant response can differ between students. It has previously been suggested that social demographic factors, including family background, are associated with university students’ psychological distress (Larcombe et al., 2016). Thus, it is expected in the current study that there will be group differences in students’ perceptions of stress, coping strategies, and the effectiveness of their coping strategies.
COVID-19 has significantly increased psychological distress among university students, disrupting academic routines and reducing access to social support (Wang et al., 2020; Elmer et al., 2022). A systematic review found that university students experienced heightened anxiety, depression, and academic burnout due to pandemic-related uncertainties (Ochnik et al., 2021). Students’ pre-existing stress may be exacerbated during the lockdown and campus closures. However, there is insufficient attention on and evidence for university students’ academic-related psychological wellbeing. This study aims at understanding university students’ stress and coping in New Zealand during the pandemic. Specifically, this study explores students’ descriptions of stress, coping strategies, and coping effectiveness. Three research questions were examined:
  • What academic-related stressors did students experience during the COVID-19 pandemic? Did these stressors differ as a function of student demographics?
  • Were there meaningful trends among the respective stressors, their association with COVID-19, and different coping strategies?
  • What factors may potentially impact the effectiveness of coping?

2. Method

2.1. Methodological Design

This study employed a mixed-methods design to explore the academic stress and coping experiences of university students during the COVID-19 pandemic. The mixed-methods approach was chosen to capture both the breadth and depth of students’ experiences, combining quantitative data from structured surveys with qualitative insights from open-ended responses. This design allows for a comprehensive understanding of the stressors faced by students and the effectiveness of their coping strategies. The hierarchical multiple regression model was selected to systematically control for demographic influences before assessing the contributions of stress and coping variables.

2.2. Sample and Procedures

Participants were recruited from a public university in New Zealand using convenience sampling. The sample comprised 193 students (female = 82.0%, male = 16.0%, gender diverse = 0.5%, prefer not to say = 0.5%) attending one public university in New Zealand. Average student age was 26.37 years old (SD = 8.26), and the median age was 24. Students’ ethnicity, consistent with categories used in the New Zealand census, was grouped as New Zealand European (n = 127; 63.5%), Asian (n = 34; 17.0%), other (n = 20; 10.0%), New Zealand Māori (n = 8; 4.0%), Pasifika (n = 6; 3.0%), and Middle Eastern/Latin American/African (n = 1; 0.5%). The sample was primarily domestic New Zealand students (n = 162; 81.0%), with ethnic representation approximating the institution’s demographics.
It is important to note that during the study period, New Zealand’s national lockdown and associated border restrictions significantly limited international student enrolment, resulting in a sample that predominantly reflects the domestic student population (New Zealand Ministry of Health, n.d.). This sample includes a higher proportion of female respondents compared to the institution’s student population. Given the voluntary nature of the study, there was an overrepresentation of female participants, which aligns with previous research indicating that women are generally more likely to participate in survey-based studies (Porter, 2004). While this may not significantly bias the results in areas such as general stress levels, it is important to acknowledge that gender differences in coping strategies exist (Tamres et al., 2002), and future research should consider more balanced sampling. Self-reported ethnicity for this sample approximates the institution’s student population.
Due to the COVID-19 pandemic, New Zealand experienced a swift national lockdown from March 2020 to June 2020. All tertiary institutions were closed during the period, and students unexpectedly transferred to online learning. Participants of this study were recruited from one university in New Zealand, aiming at students who returned to campus-based learning when the new semester started in July 2020. Invitations were emailed to 698 students in August 2020, including a link to an online questionnaire using closed-ended and open-ended questions to gather quantitative and qualitative data. The questionnaire was available for two weeks. The study was anonymised and granted the institution’s ethical approval (2019/85/ERHEC).
Initially, 277 participants accessed the questionnaire, yielding a response rate of 39.68%. In addressing missing data, 84 missing items were identified and deleted in accordance with the notion that imputation is appropriate when only a small number of items are missing (Little & Rubin, 2019). Missing data analysis was conducted using Little’s MCAR test (Little & Rubin, 2019) to determine whether missing values followed a missing completely at random (MCAR) pattern. The test was non-significant (χ2 = −514.95, df = 3655, p = 1.00), indicating that no systematic cause of missingness was detected. This supports the appropriateness of Expectation Maximisation (Dempster et al., 1977) for data imputation. Accordingly, the remaining missing values in the final sample (n = 193) were imputed using expectation maximisation procedures (Dempster et al., 1977), thereby minimising potential bias in subsequent analyses.

2.3. Instruments

2.3.1. University-Related Stress

Academic stress was assessed through both structured and open-ended survey items. Three stress-related variables were included: (1) the nature of the stress, (2) the impact of stress on one’s life, and (3) the degree to which the stressor was related to COVID-19.
Participants were first asked “This semester, have you experienced any university-related stressors?” (Yes/No). Those who responded “Yes” provided a brief description of a specific stressor, which was later thematically coded. The impact of stress was measured on a 5-point Likert scale (1 = No impact; 5 = A great deal of impact). Additionally, participants rated the extent to which their stress was related to COVID-19 on a 5-point scale (1 = Not at all; 5 = A great deal).

2.3.2. Coping Strategies and Perceived Effectiveness

In this part of the questionnaire, two variables related to coping were included: (1) types of coping strategies; (2) effectiveness of coping.
To assess students’ coping strategies, participants who reported experiencing stress were asked “How have you been coping with this stressful situation?”
The Brief COPE instrument (Carver, 1997) was used to measure students’ coping strategies. This instrument is one of the most widely validated scales for assessing coping behaviours and has been extensively used in student populations (Mahmoud et al., 2012; Riddell et al., 2018). It consists of 28 items across 14 subscales, with each subscale containing two items. The 14 coping subscales include:
  • Active Coping (taking active steps to address the stressor);
  • Planning (thinking through ways to cope with the stressor);
  • Positive Reframing (managing distressing emotions by changing one’s perspective);
  • Acceptance (acknowledging the reality of the situation);
  • Humour (using humour to reframe or reduce stress);
  • Religion (seeking support through religious practices);
  • Using Emotional Support (seeking comfort from others);
  • Using Instrumental Support (seeking advice or assistance);
  • Self-distraction (engaging in unrelated activities to divert attention);
  • Denial (refusing to acknowledge the stressor);
  • Venting (expressing distress or frustration);
  • Substance Use (using alcohol or drugs to cope);
  • Behavioural Disengagement (reducing effort in dealing with the stressor);
  • Self-Blame (criticising oneself for the situation).
Participants responded to each item using a 4-point Likert scale ranging from 1 (I have not been doing this at all) to 4 (I have been doing this a lot). The internal consistency of the Brief COPE scale in this study was good (Cronbach’s α = 0.84), demonstrating acceptable reliability.
To understand perceived coping effectiveness, participants were asked, “At present, to what extent have your coping strategies been effective in reducing this university-related stressor?”. Participants responded to this item using a five-point Likert-style scale ranging from 1 (Extremely effective in reducing my stress) to 5 (Not effective at all in reducing my stress).

2.4. Statistical Analysis

2.4.1. Qualitative Data Analysis

To analyse the “nature of stress” reported by participants, an inductive thematic coding approach (Braun & Clarke, 2006) was used to categorise open-ended responses into stressor types. The coding process followed multiple iterations to ensure reliability and validity.
Pre-Coding Preparation: Before formal coding, the dataset was thoroughly reviewed, and key phrases such as “time management difficulties”, “assessment concerns”, and “motivation issues” were highlighted. This preliminary step allowed for a more structured approach to developing thematic categories (Saldaña, 2021).
Coding Process: An initial round of conventional content analysis (Hsieh & Shannon, 2005) generated 14 preliminary codes based on direct participant responses and relevant theoretical frameworks. Given the complexity of responses, a Simultaneous Coding Method (Saldaña, 2021) was employed, allowing multiple codes to be assigned to a single response when applicable. This approach captured the multi-faceted nature of students’ stress experiences. After refining the coding scheme through literature review and theoretical alignment, the codes were consolidated into eight final stressor categories.
Inter-Rater Reliability: To enhance coding reliability, two independent coders applied the finalised codebook to the dataset. The initial agreement between coders was 75%, prompting a review and revision of category definitions. Following adjustments, coding consistency improved to 95% agreement. The final Cohen’s Kappa was estimated at κ = 0.94, indicating almost perfect agreement (Landis & Koch, 1977).
Final Stress Categories: The final eight stressor categories identified through thematic analysis are presented in Table 1, along with representative examples. These categories were used in subsequent quantitative analyses to examine relationships between stressors, coping strategies, and coping effectiveness.

2.4.2. Quantitative Data Analysis

Exploratory Factor Analysis (EFA) was conducted using maximum likelihood estimation with oblique (direct oblimin) rotation. Factor retention was determined through Parallel Analysis, the Scree Plot, and the Kaiser criterion (eigenvalues > 1). The Kaiser-Meyer-Olkin (KMO) measure was 0.73, and Bartlett’s Test of Sphericity was significant (χ2 = 549.62, df = 91, p < 0.001), confirming data suitability. EFA was selected due to the exploratory nature of this study and the context-dependent variability of coping strategies (Lazarus & Folkman, 1984). Given the unprecedented nature of COVID-19 stressors, traditional coping models might not fully capture emerging strategies. EFA allows for data-driven identification of coping dimensions without enforcing a predetermined structure. Factor retention was determined using multiple criteria: Parallel analysis indicated that the three retained factors exceeded random eigenvalues. The scree plot showed a clear inflection point after the third factor. The Kaiser criterion (eigenvalues > 1) supported the retention of three factors. The three identified factors align with previous coping research (Carver, 1997; Skinner et al., 2003), representing Proactive Coping, Avoidance Coping, and Support-Seeking Coping, reinforcing their theoretical validity.
While Confirmatory Factor Analysis (CFA) is often used to validate factor structures, it was not conducted in this study due to its requirement for a larger sample size and a predefined theoretical structure. Instead, construct validity was assessed by aligning the extracted factors with established coping frameworks. Future research with larger datasets could apply CFA to confirm the structure derived from EFA and assess its generalizability across populations.
Second, bivariate correlations were conducted to examine the relationships between the three most common stressors, coping strategies, and coping effectiveness. Given the number of correlations tested, there is an increased risk of Type I error inflation.
Last, Hierarchical Multiple Regression (HMR) was conducted to examine predictors of coping effectiveness. This approach was selected to control for demographic influences before assessing the effects of stress and coping strategies, allowing for a clearer interpretation of the unique contributions of each predictor. A preliminary analysis was conducted to ensure no violation of the assumptions of normality (histograms, Q-Q plots), linearity (scatterplots demonstrate approximately linear relationships between), multicollinearity (Variance Inflation Factor (VIF) values, which were all below 5) and homoscedasticity (residual plots were inspected, showing no significant heteroscedasticity concerns). The order of variable entry was informed by established stress and coping theories, which emphasise the foundational influence of demographic factors on stress perception and coping responses (Lazarus & Folkman, 1984; Carver, 1997).
Step 1: Demographic variables (age, gender, first-generation student status) were entered first to account for individual differences that influence coping effectiveness. Age and gender, for instance, are critical predictors of coping styles (Tamres et al., 2002). Additionally, first-generation students may experience higher stress due to academic unfamiliarity and a lack of institutional support (Gibbons et al., 2019).
Step 2: COVID-19-related stress and its perceived negative impact were added to assess their role beyond demographic influences. The pandemic introduced high-intensity stressors such as uncertainty, learning disruptions, and social isolation (Son et al., 2020), affecting students’ coping responses and distinguishing adaptive vs. maladaptive mechanisms (Zacher & Rudolph, 2021).
Step 3: Coping strategies (Proactive Coping, Avoidance Coping, Support-Seeking Coping) were entered last to evaluate their unique predictive value in coping effectiveness after controlling for personal background and stress exposure. Coping strategies represent intentional efforts to manage stress, with studies indicating that proactive coping enhances resilience, whereas avoidance coping is associated with higher distress (Carver et al., 1989; Skinner et al., 2003).

3. Results

3.1. Types of Stressors

Our first research goal is to understand the university-related stress that students have perceived during the unstable situation of the COVID-19 pandemic. Among all 193 participants, 157 participants (81.3%) responded that they had experienced university-related stress since March 2020. The responses to the open-ended question were analysed using thematic analysis. The first version of the codebook includes 14 codes. It was then revised and improved based on the self-check within the research group and external checks by three other invited researchers. At the last round of checking, the agreement between researchers upon codes reached 95%. The researcher recorded a detailed analysis journal to ensure the study’s reliability. Following these steps, the current study was confirmed as credible, transferable, and reliable (Guba & Lincoln, 1989; Nowell et al., 2017).
Examples of each stressor and the frequencies are shown in Table 1. Among all stressors, 52.6% of students found it challenging to allocate their time properly or finish tasks in the time given. Participant 17 commented, “Time pressure has been a big one. I am under huge amounts of time pressure and finding I have to study 9 to 5 most days, even at the weekends, to get stuff done, and I’m still behind on content”. Some participants had noticed differences between lockdown and campus-based learning; Participant 1 commented:
“I’ve found time management hard, as, during online uni for COVID, I was able to work at my own speed and prioritise work that needed more attention at the time. Now that I’m back on campus, I feel like there isn’t enough time in the day to get everything done during lockdown.”
In addition, 32.7% of participants reported stressors related to learning motivation and adjustment to different learning environments during the pandemic. University closure was reported to affect learning significantly: “I think COVID stopped my motivation because being stuck at home and doing work was very unproductive (I needed a quiet place, and the university library was closed). (participant 16)” A noteworthy observation is that participants have differing preferences between distance learning during lockdowns and campus-based learning. Some participants had difficulties adapting to online learning: “For me, university/lab environment and colleagues make me strong to focus on my goals. Moreover, the face-to-face interaction with your supervisor gave me more confidence, and I can explain well, which is lacking somehow during lockdown (participant 149)”. Returning to campus was challenging for some students: “I find it challenging to go and attend lectures as I got so used to staying at home and watching lectures online. (participant 6)”

3.2. Types of Coping Strategies

Exploratory factor analysis (EFA) in SPSS version 26 was conducted using maximum likelihood estimation with oblique (direct oblimin) rotation. The Kaiser-Meyer-Olkin (KMO) measure confirmed sampling adequacy (0.73), and Bartlett’s Test of Sphericity was significant (χ2 = 549.62, df = 91, p < 0.001), indicating that factor analysis was appropriate. Three factors emerged with eigenvalues greater than one: Factor I (2.96, 29.61%) and Factor II (2.20, 21.96%), and Factor III (1.29, 12.97%).
All standardised factor loadings were above the recommended cut-off of 0.40 (ranging from 0.65 to 0.90), as shown in Table 2. The total variance explained by the three factors was 64.54%.
Factor 1: Proactive Coping (Cronbach’s α = 0.76)—Included Active Coping, Planning, and Positive Reframing, reflecting efforts to actively manage stressors.
Factor 2: Avoidance Coping (Cronbach’s α = 0.72),—Comprised Behavioral Disengagement, Denial, Self-Blame, and Self-Distraction, indicating of orienting away from the stressor or avoiding it.
Factor 3: Support-Seeking Coping (Cronbach’s α = 0.77)—Consisted of Emotional Support, Instrumental Support, and Venting, representing efforts to gain external assistance to help with the situation or emotion.
The results suggest that students primarily engage in proactive, avoidance, and support-seeking coping mechanisms, providing a basis for examining their effectiveness in subsequent analyses. The identified factors were used in bivariate correlations and hierarchical regression models to assess their predictive relationships with coping effectiveness.

3.3. Descriptive Statistics

To explore the associations between stressors and other variables in the study, all eight stressors were transformed into binary variables with 0 = participant did not mention as a stressor and 1 = participant mentioned as a stressor. We began our analysis with an inspection of descriptive statistics among the variables of interest, as shown in Table 3. These results demonstrate a range of scaled responses that is adequate and indicate low skewness and kurtosis values (which reflect normal distributions).
We inspected whether the eight stressors and 14 coping strategies differed across demographic characteristics: age, year level at university, and whether the individual is the first member in their family to attend university (“first-in-family”; FiF). Among them, FiF was associated with students’ perceptions of stress and their deployment of coping strategies. According to the results of Chi-square tests of independence, FiF was linked to the UniLecSup stressor (χ2 = 6.70, p = 0.010). Independent-samples t-tests were conducted to explore the association between FiF and coping strategies. Results indicated that students who were not FiF tended to use the Emotional Support strategy more frequently (M = 2.69, SD = 0.92) than FiF students (M = 2.33, SD = 0.99), t (155) = −2.35, p = 0.020. Similarly, students who were not FiF tended to use Instructional Support Strategy more frequently (M = 2.50, SD = 0.92) than FiF students (M = 2.20, SD = 0.88), t (155) = −1.99, p = 0.048. While these descriptive statistics provide valuable insights, it is important to acknowledge that self-reported data may be subject to biases such as social desirability and recall effects (Podsakoff et al., 2003). Future studies should consider longitudinal tracking or mix-method approaches to gain a more comprehensive understanding of coping effectiveness over time.

3.4. Bivariate Correlations

In Table 4, we presented bivariate correlations between the variables of interest and three sub-groups based on the perception of the three most commonly reported stressors. Results showed that, at the general level, coping effectiveness positively correlated with Approach Strategies and Support Seeking Strategies, and it was negatively correlated with the Avoidance Strategies. In addition, the relevance of COVID-19 to stressors was positively correlated with the Negative Impact of Stress and Support Seeking Strategies. Notably, students who struggled with task and time management due to COVID-19 were more likely to report with the Avoidance Strategies. However, students who struggled with a lack of motivation due to COVID-19 were more likely to report Approach Strategies. Students who suffered from the more significant negative impact of stress were more likely to cope with Support Seeking and Avoidance Strategies. Meanwhile, they reported lower effectiveness of coping. Given the number of comparisons, marginally significant findings (p-values between 0.01 and 0.05) should be interpreted cautiously, as they may represent false positives due to Type I error inflation.

3.5. Hierarchical Multiple Regression

Hierarchical multiple regression was used to assess the effectiveness of coping strategies after controlling students’ gender, age, year level and FiF. This approach allowed for an incremental assessment of variance explained at each stage, addressing Research Question 2 regarding the impact of these variables on coping effectiveness. A preliminary analysis was conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity and homoscedasticity.
As shown in Table 5, presents the regression coefficients and model statistics for each step. The final model explained 40.8% of the variance in coping effectiveness 40.8%, F (21, 128) = 4.44, p < 0.001, with each step contributing significantly to the explained variance:
In Step 1, predictors including gender, age, year level, and FiF were entered, explaining 1.5% of the variance of coping effectiveness. In Step 2, after entering the Relevance of COVID-19 to Stressors, the Negative Impact of Stressors, and Approach, Avoidance and Support Seeking Strategies, the total variance explained by the model was 40.8%, F (21, 128) = 4.44, p < 0.001. The coping strategies explained an additional 31.5% of the variance in the effectiveness of coping, after controlling for demographic variables, R squared change = 0.30, F change (9, 140) = 12.28, p < 0.001. In the final model, three types of coping strategies and the Negative Impact of Stress were statistically significant. The Approach Strategies (β = −0.31, p < 0.001) recorded a higher β value than other factors. These findings directly address Research Question 2, confirming that coping effectiveness is shaped by demographic variables, COVID-19-related stress, and coping strategies. The strong contribution of Approach Coping aligns with resilience-based theories (Windle, 2011), while the negative association with Avoidance Coping reinforces its maladaptive nature, consistent with previous studies indicating that avoidance strategies are linked to increased distress and poorer mental health outcomes (Compas et al., 2017; Skinner et al., 2003). These results suggest that interventions promoting adaptive coping strategies, particularly proactive coping, may improve students’ ability to manage academic stress and enhance long-term resilience (Cheng et al., 2014).

4. Discussion

The purpose of this study was to fill a research gap regarding university students’ academic stress during the COVID-19 pandemic and gain a better understanding of how individuals cope and whether these strategies are seen as effective. Our findings are among the first to explore university students’ stress and coping experience during the pandemic. The results of our study indicate that students in one public university in New Zealand had a higher prevalence of stress than the average rate of 39% among 27 studies on college students during COVID-19, as reported in a meta-analysis (Li et al., 2021). Although different measures of stress have been collected, one possible interpretation is that even before COVID-19, New Zealand students were more likely to report higher stress when compared to other nations (Menzies et al., 2020). We expanded on this literature by identifying the nature of stress during COVID-19. It is noteworthy that most stressors resulted from the pandemic and relevant policies, and had adversely impacted students’ daily lives. The management of time and tasks, lack of motivation, and the transition between online and campus-based learning environments were identified as three major stressors. The results are not surprising, as university students may struggle with these stressors in any given semester (Robotham & Julian, 2006). During COVID-19, it appears as though these challenges were exacerbated. The lack of preparation phase of the transitional process could result in challenges in adapting to the online learning environment (Nicholson, 1984). Students may attempt--but struggle--to cope effectively with typical academic challenges while their learning and course schedules were interrupted. Our findings also suggest that students who are the first in their families to attend college were more vulnerable and tended to feel a lack of information and support. Consistent with extant literature, first-generation university students may lack sufficient family support and have obstacles adjusting to university (O’Shea et al., 2024). The academic stress of vulnerable students may be reduced if institutions and lecturers offer additional assistance to combat the lack of support from their families and social circles.
These findings provide practical implications for educational providers in elevating students’ academic stress and supporting their transition to blended learning during and after the pandemic. We propose that identifying students’ voices should be the first step to improving support programmes’ effectiveness. By doing so, scarce supportive resources can be allocated more efficiently (Baik et al., 2019). Our study suggests that students’ academic stress could be reduced if lecturers provide students with time and task allocation suggestions before delivering the learning content (Tabvuma et al., 2022). Providing students with online and physical learning options and organising workshops on learning strategies in various scenarios may benefit students by increasing their flexibility in different learning environments. Further, students’ academic stress could be reduced with the effort of institutions and faculties to foster a better learning environment during and after the pandemic. Initiatives that aim to enhance student engagement through instructional and pedagogical design (Resch et al., 2023) and increase access to resources (Kilgour et al., 2019) could be beneficial in this circumstance. By creating and encouraging opportunities for communication and collaboration with peers, students may also benefit from a supportive learning atmosphere despite distance constraints.
Although our study provides valuable insights, alternative explanations for the observed relationships must be considered.
First, individual personality traits, such as conscientiousness and neuroticism, may moderate the relationship between stress and coping effectiveness (Connor-Smith & Flachsbart, 2007). Students with high conscientiousness may be predisposed to proactive coping, while those with higher neuroticism may be more likely to rely on avoidance strategies. Future research should incorporate personality measures to refine coping models.
Second, academic workload and institutional support were not explicitly measured in this study but are likely to have influenced stress levels and coping behaviours. Universities offering structured mental health and academic support services may have helped buffer students from pandemic-related stress, influencing their coping effectiveness (Baik et al., 2019). Future longitudinal research could examine how institutional responses to student stress evolve.
Third, cultural differences in coping strategies should be explored in future studies. Research suggests that students from collectivist cultures may prioritise social and familial support, whereas students from individualist cultures may favour self-regulation and cognitive restructuring (Kuo, 2013). Investigating cross-cultural variations in stress and coping responses could enhance the generalisability of these findings.
Under the pandemic context, we have discovered important trends concerning coping strategies and different stressors. Students who experienced stress from a lack of motivation tended to cope proactively if the experience was closely related to COVID-19. There is the possibility that students who demonstrated higher levels of self-regulation prior to the pandemic were more likely to perceive changes in learning status and a lack of motivation, particularly during this challenging time. Thus, these students are more likely to exhibit solution-oriented behaviour (Miller & Brickman, 2004). Results indicate that students concerned with managing tasks and time due to COVID-19 tend to use avoidant coping strategies and seek support. However, the search for assistance does not always lead to satisfactory results. As Participant 63 commented, “I do not feel completely supported by the university or like my wellbeing is being put first”. This finding revealed the discrepancy between the increasing demand from students and the insufficient support provided by institutions and faculties. In responding to the critical need, universities and lecturers should first raise the awareness of the psychological impact of COVID-19 on students’ learning. It can be started with institutions providing lecturers and faculties with information and training on the psychological effects of unexpected interruptions in students’ learning. Additionally, institutions can develop and implement projects that seek to understand students’ experiences and develop programmes that incorporate diverse resources and platforms accessible to a wide range of students. For example, an online system where students can launch their questions and receive timely responses from staff would be helpful.
Our results of hierarchical regression indicated that coping effectiveness was positively predicted by approach and support-seeking strategies yet negatively predicted by avoidance strategies, which is consistent with previous study findings suggesting the factors contributing to effective coping (Aldwin, 2009; Skinner & Zimmer-Gembeck, 2016; Vanstone & Hicks, 2019). In addition to facilitating comprehensive supporting systems and policies, institutions and faculties must provide students with instruction on effective ways of coping. Institutions can help mitigate the effects of stress on students by providing students with the knowledge of healthy and effective coping methods and enriching the available resources to support their academic engagement and performance (Vizoso et al., 2018).

5. Limitations

Our study provides robust evidence on the specific stressors that students experience when facing unexpected changes. It also yields insight into the trends between stress, coping strategies and effectiveness. Alongside the contribution to research, theory and practice, several limitations should be addressed in subsequent research A key limitation of this study is the gender imbalance in the sample, with 82% of participants identifying as female. Prior research suggests that men and women may adopt different coping strategies when managing academic stress, including emotion-focused and support-seeking coping strategies (Matud, 2004; Tamres et al., 2002). This overrepresentation of female students may have influenced findings, particularly in the higher reported use of support-seeking coping strategies. Future research should aim for a more balanced gender distribution or conduct gender-specific analyses to explore potential differences in stress responses and coping effectiveness. Second, we adopted EFA to identify the structure of coping strategies. While EFA successfully identified three coping dimensions, some overlapping variance suggests that additional refinement is needed to ensure the distinctiveness of each factor. Future studies should apply larger, more diverse samples to validate the structure and consider alternative factor extraction methods to enhance clarity. Furthermore, since EFA is inherently exploratory, findings should be interpreted with caution until validated using CFA in future research.
Another limitation concerns the hierarchical multiple regression (HMR) approach. While HMR allows for the stepwise control of demographic influences before assessing coping strategies, it assumes linear relationships among variables and does not account for potential interaction effects. Although interaction terms were explored in preliminary analyses, no significant moderating effects were found. However, future research could apply alternative modelling techniques, such as structural equation modelling (SEM), to examine mediation and indirect pathways between stress, coping, and mental health outcomes. Additionally, longitudinal designs could provide further insights into how coping strategies evolve over time and their long-term effectiveness.
In addition, the limited sample size and low response rate may restrict the generalizability of findings and highlight potential non-response bias. While efforts were made to recruit a diverse sample, future studies should aim for higher response rates to improve representativeness. Our study used self-report, cross-sectional data collection. Using self-report measures could lead to biases, and cross-sectional designs limit the ability to explain processes over time. Thus, we recommend that future studies use various measures and longitudinal methods to measure psychological factors that change over time. Our study focuses on the perceptions and experiences from students’ perspectives. Future studies can further analyse the experience of academics during the pandemic.

6. Conclusions

This study explored the academic stressors and coping mechanisms of university students in New Zealand during the COVID-19 pandemic, addressing a gap in the literature on how students adapted to pandemic-related challenges. Using a mixed-methods approach, we identified key academic stressors, including time and task management, motivation, and the transition between online and in-person learning. Our findings further confirmed that proactive coping strategies were associated with greater coping effectiveness, while avoidance coping strategies correlated with poorer outcomes. The results contribute to the literature on student stress and coping, underscoring the importance of institutional support in fostering adaptive coping mechanisms. The findings indicate that universities should focus on targeted interventions such as resilience training, structured learning support, and increased student engagement initiatives to improve coping effectiveness and academic wellbeing. In conclusion, this study examined the psychological impact of COVID-19, addressed the role that lecturers and universities play in supporting students’ mental health and learning experience, and proposed practical suggestions. Facilitating an engaging learning environment for online and offline options, and promoting diverse supporting platforms accessible to students, would benefit the long-term development and recovery of higher education in the post-pandemic era.

Author Contributions

Conceptualization, X.R., V.A.S. and C.B.; methodology, X.R., V.A.S. and C.B.; Software, X.R. and V.A.S.; validation, X.R.; investigation, X.R. and V.A.S.; data curation, X.R.; writing—original draft preparation, X.R.; writing—review and editing, V.A.S. and C.B.; supervision, V.A.S. and C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Educational Research Human Ethic Committee of the University of Canterbury (protocol code: 2019/85/ERHEC; date of approval: 22 January 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data is unavailable due to ethical restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Eight common stressors in university students in New Zealand with descriptions, examples and frequencies (n = 193).
Table 1. Eight common stressors in university students in New Zealand with descriptions, examples and frequencies (n = 193).
StressorsDescriptionExamplesnPercentPercent of Cases
Task and Time Management
(TTM)		Challenges related to allocating time properly and finishing tasks in the time given.“I have had difficulties with managing my workload as I also have been trying to balance work, improving my mental health and university”. P (Participant)38229.8%52.6%
Motivation in Learning
(MoL)		Challenges related to engagement and attention during the course, involvement in learning activities, and self-report level of motivation.“It is hard to motivate me to study”. P41
“Trouble focusing and staying on task for long periods of time”. P55		5118.5%32.7%
Adjustment between Online and Campus-based Learning
(Adjustment)		Challenges in shifting between online learning and campus-based learning during the pandemic.“Feeling that everything is on ‘pause’”. P94
“Change in lockdown levels and having to go from on campus to mostly learning online”. P134		5118.5%32.7%
Concerns about Learning and Assessment
(ConLA)		Challenges related to understanding the learning contents, test requirements, and concerns about assessments and assignments.“Assessment concerns are the major stressor this semester”. P191
“Don’t understand course structure”. P168		3211.6%20.5%
Negative Emotions
(NegEmo)		Unpleasant feelings during the pandemic include homesickness, longlines, uncertainty, anxiety, worries, and so on.“Feeling trapped on a certain life path”. P42
“Giving university the engagement it needs due to uncertainty and anxiety”. P44		279.8%17.3%
Lack of Information and Support from University and Lecturers
(UniLecSup)		Feeling of lack of information and clarification from lecturers and insufficient support and communications from the university.“Lecturers often have a disregard for what goes on outside of their class and expect you to devote every living moment to their topic of learning”. P48165.8%10.3%
Interpersonal and Social Relationships
(InSoR)		Challenges in social associations and connecting with other people.“I am an International student from China; quite hard to be involved in local societies, and it’s really hard to make friends”. P21114.0%7.1%
Other stressors
(Others)		Financial difficulties, health-related issues“Due to COVID and the financial strain it has caused, I was also unable to attend classes”. P15462.2%3.8%
Table 2. Exploratory factor analysis pattern matrix with factor loadings depicting Approach Strategies, Avoidance Strategies, and Support Seeking Strategies using Maximum Likelihood extraction and oblique rotation (n = 193).
Table 2. Exploratory factor analysis pattern matrix with factor loadings depicting Approach Strategies, Avoidance Strategies, and Support Seeking Strategies using Maximum Likelihood extraction and oblique rotation (n = 193).
Coping Strategies ItemsFactor Loading
123
Factor 1: Approach Coping
Active Coping0.882
Planning0.801
Positive Reframing0.760
Factor 2: Avoidance Coping
Behavioural Disengagement 0.820
Denial 0.748
Self-Blame 0.733
Self-Distract 0.647
Factor 3: Support-Seeking Coping
Emotional Support −0.897
Instrumental Support −0.824
Venting −0.730
Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalisation. Rotation converged in 8 iterations.
Table 3. Descriptive statistics of Effectiveness of Coping, Relevance of COVID-19 to Stressors, Approach Coping Strategies, Avoidance Coping Strategies and Support Seeking Coping Strategies in University Students in New Zealand (n = 193).
Table 3. Descriptive statistics of Effectiveness of Coping, Relevance of COVID-19 to Stressors, Approach Coping Strategies, Avoidance Coping Strategies and Support Seeking Coping Strategies in University Students in New Zealand (n = 193).
VariableMinMaxMSDSkewnessKurtosis
Effectiveness of Coping152.600.910.170.03
Relevance of COVID-19 to Stressors153.601.23−0.56−0.58
Negative Impact of Stress153.350.900.07−0.29
Approach Coping142.680.65−0.13−0.51
Avoidance Coping162.291.060.990.73
Support Seeking Coping142.360.74−0.11−0.91
Table 4. Bivariate correlations between effectiveness of coping, relevance of COVID-19 to stressors, negative impact of stress and three types of coping strategies in university students in New Zealand (n = 193).
Table 4. Bivariate correlations between effectiveness of coping, relevance of COVID-19 to stressors, negative impact of stress and three types of coping strategies in university students in New Zealand (n = 193).
Pearson’s Correlations by Group (G-General; S1, n = 82; S2, n = 51; S3, n = 51)
123456
1. Effectiveness of CopingGeneral1−0.021−0.231 **0.369 **−0.280 **0.280 **
Stressor 1 0.087−0.1270.500 **−0.1860.344 **
Stressor 2 0.181−0.2560.502 **−0.332 *0.247
Stressor 3 −0.072−0.313 *0.601 **−0.1610.296 *
2. Relevance of COVID-19 to StressorsGeneral−0.02110.254 **0.1170.1540.187 *
Stressor 1 0.2150.1510.288 **0.248 *
Stressor 2 0.1500.286 *−0.0750.167
Stressor 3 0.180−0.0020.0500.022
3. Negative Impact of StressGeneral−0.231 **0.254 **10.1220.402 **0.268 **
Stressor 1 0.0970.424 **0.334 **
Stressor 2 0.1210.356 *0.329 *
Stressor 3 −0.0770.328 *0.383 **
4. Approach CopingGeneral0.369 **0.1170.1221−0.0190.341 **
Stressor 1 −0.0500.337 **
Stressor 2 −0.0820.409 **
Stressor 3 −0.0880.437 **
5. Avoidance CopingGeneral−0.280 **0.1540.402 **−0.01910.166 *
Stressor 1 0.241 *
Stressor 2 0.121
Stressor 3 0.241
6. Support Seeking General0.280 **0.187 *0.268 **0.341 **0.166 *1
Stressor 1
Stressor 2
Stressor 3
* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). Stressor 1—Task and Time Management; Stressor 2—Motivation in Learning; Stressor 3—Adjustment.
Table 5. Summary of hierarchical regression analysis for variables predicting effectiveness of coping (n = 193).
Table 5. Summary of hierarchical regression analysis for variables predicting effectiveness of coping (n = 193).
VariableBSEβ ΔR2tSig.
Step 1 0.015
Gender−0.120.20−0.05 −0.620.54
Age0.020.110.02 0.190.85
Year Level0.030.050.05 0.540.59
First-in Family0.140.150.08 0.900.37
Step 2 0.315
Gender−0.120.17−0.05 −0.690.49
Age−0.010.10−0.01 −0.100.92
Year Level0.020.040.05 0.570.57
First-in Family0.010.140.01 0.090.93
Relevance of COVID-19 to Stressors−0.010.05−0.02 −0.220.83
Negative Impact of Stressors−0.250.08−0.25 −3.100.00
Approach Coping0.430.110.31 4.040.00
Avoidance Coping−0.170.07−0.20 −2.570.01
Support Seeking Coping0.350.100.28 3.560.00
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Ren, X.; Sotardi, V.A.; Brown, C. Exploring Academic Stress and Coping Experiences Among University Students During the COVID-19 Pandemic. Educ. Sci. 2025, 15, 314. https://doi.org/10.3390/educsci15030314

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Ren X, Sotardi VA, Brown C. Exploring Academic Stress and Coping Experiences Among University Students During the COVID-19 Pandemic. Education Sciences. 2025; 15(3):314. https://doi.org/10.3390/educsci15030314

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Ren, Xin, Valerie A. Sotardi, and Cheryl Brown. 2025. "Exploring Academic Stress and Coping Experiences Among University Students During the COVID-19 Pandemic" Education Sciences 15, no. 3: 314. https://doi.org/10.3390/educsci15030314

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Ren, X., Sotardi, V. A., & Brown, C. (2025). Exploring Academic Stress and Coping Experiences Among University Students During the COVID-19 Pandemic. Education Sciences, 15(3), 314. https://doi.org/10.3390/educsci15030314

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