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Article

Academic Motivation of Canadian Undergraduate Students: Mental Health, Sociodemographic and COVID Predictors

by
Max J. Marshall
,
Kesaan Kandasamy
and
Lixia Yang
*
Department of Psychology, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
*
Author to whom correspondence should be addressed.
Psychiatry Int. 2026, 7(4), 151; https://doi.org/10.3390/psychiatryint7040151
Submission received: 13 April 2026 / Revised: 22 June 2026 / Accepted: 1 July 2026 / Published: 8 July 2026
(This article belongs to the Section Mental Health)
Review Reports Versions Notes

Abstract

In the context of the COVID-19 pandemic, the relationship between academic motivation and mental health remains unclear among Canadian undergraduate students. This study examined associations between academic motivation and mental health, sociodemographic, and COVID-related factors in Canadian undergraduate students during the period of returning back to in-person learning (third stage of the pandemic from January–March 2022). A sample of 1868 undergraduates across British Columbia, Ontario, and Quebec completed a cross-sectional online survey in Winter 2022. Regression models revealed that depression was associated with lower academic motivation (βs ≥ 0.12, ps < 0.001), whereas stress and resilience were associated with higher academic motivation (βs ≥ 0.07, ps ≤ 0.001), controlling for related sociodemographic or COVID-related factors. Results also identified some sociodemographic (e.g., year of study, gender, English as first language status) and COVID-related factors (e.g., preference for in-person vs. online learning) for academic motivation. Results highlight factors associated with the academic motivation of students when returning back to in-person learning.

1. Introduction

In March 2020, the World Health Organization (WHO) declared the rapid spread of the 2019 coronavirus disease (COVID-19) a global pandemic [1]. Academic institutions across the globe were mandated to close campuses and transition from in-person programming to remote teaching and learning. As a consequence, university students were faced with adjusting to ongoing and unpredictable transitions related to course delivery, campus closures, and overall university culture throughout the pandemic. The impact of these COVID-19 changes on students’ mental health has been well-reported in the literature, especially in earlier stages of the pandemic (see [2] for a systematic review). Notably, it has been reported that relative to non-students, students’ mental health may have been more impacted by the pandemic [3,4]. Specifically, early survey data suggests that students endorsed greater depression and anxiety symptoms relative to before the pandemic [5,6]. It has also been revealed that the impacts of the COVID-19 pandemic on psychological well-being of students also varied by sociodemographic variables. For example, previous data found some gender differences in the impacts of the pandemic on student’ mental health, with women experiencing greater depression than men [7,8]. Given the impact of the COVID-19 pandemic on students’ mental health and documented sociodemographic differences in these experiences, it is important to understand how mental health and sociodemographic factors are associated with academic motivation during the transition back to in-person learning, as the return to in-person learning mode may introduce new academic, social, and health-related demands that may shape the relationship between students’ mental health and academic motivation.

1.1. Student Mental Health During COVID-19

Postsecondary students are already vulnerable to mental health challenges due to academic, financial, and social demands. The COVID-19 pandemic adds a unique and novel stressor that has given rise to significant mental health challenges among students [9]. Cross-sectional survey data indicate that younger adults’ (aged 18–29) psychosocial wellbeing was most negatively affected during the COVID-19 pandemic [10]. Since the pandemic, there have been notable increases in depression, anxiety, stress, loneliness, and suicidal ideation among younger adults [10,11,12,13]. These findings have been consistently replicated across several Western universities [14]. For example, one study found that over three quarters of university students endorsed psychopathological symptoms, representing a drastic negative shift in mental health from pre-pandemic levels [15]. Furthermore, over 72% of students reported feeling that their wellbeing was seriously impaired during the pandemic [16].
Data collected throughout the pandemic revealed that increases in depression, anxiety, and stress were the most prominent mental health impacts of the pandemic. It was found that half of students surveyed reported significant levels of stress early in the pandemic [17]. Research also revealed that levels of anxiety and depression increased exponentially for postsecondary students during the pandemic [18,19,20]. In a survey with over 5000 undergraduate students, levels of significant anxiety and depression were approaching 50% and 35% respectively [21]. Over 70% of students reported an increase in both stress and anxiety during the pandemic compared to before [22]. Together, this literature indicates that depression, anxiety, and stress were among the most prominent mental health concerns experienced by postsecondary students during the pandemic.

1.2. Academic Motivation During the Pandemic

Academic motivation has been demonstrated to be an important predictor for academic performance and success in students (e.g., [23]). Importantly, previous studies have demonstrated that students’ academic motivation has been negatively impacted by the pandemic. For example, a study showed that university students reported significant decreases in motivation and increases in school-related anxiety in the transition from in-person to online learning in the early stages of the pandemic [24]. Another study with American university students found that over three quarters of students reported a decrease in motivation for school and an increase in stress during the transition from in-person to online learning in the spring of 2020 [25].
Mental health status has been identified as a contributing factor to academic motivation in postsecondary students prior to the pandemic. For example, it has been found that higher levels of depression and anxiety were correlated with lower academic motivation [26]. Given the parallel increase in negative mental health status and decrease in academic motivation during the pandemic, the literature has also sought to examine the relationship between mental health and academic motivation amongst university students in the pandemic context, specifically to determine whether higher negative mental health status (e.g., depression, anxiety, distress) may associate with lower academic motivation. Indeed, one paper reported that COVID-related psychological distress was negatively correlated with academic motivation among American undergraduate students [27]. Further, Goksu et al. [28] collected data from 1500 Turkish university students and found that depression, anxiety, and stress levels were all negatively correlated with distance-learning motivation (i.e., motivation for online learning).
While some studies have explored the association between negative mental health indices and academic motivation, fewer studies have explored the role of protective factors such as resilience and its association with academic motivation. Resilience has been identified as a protective factor that positively correlates with mental wellbeing (see [29] for a scoping review). Research conducted prior to the pandemic revealed that resilience positively predicted academic motivation [30]. In the context of COVID-19, Abdolrezapour and colleagues [31] found that resilience was positively correlated with academic motivation among a sample of South Iranian undergraduate students. Given that resilience has been shown to be a positive factor for both mental health and academic motivation, it is important to examine how resilience is associated with academic motivation in the context of a transition period to remote learning during the pandemic.

1.3. Sociodemographic and COVID-Related Variables

It is critical to understand the intersection of academic motivation and mental health as it relates to differences between sociodemographic groups. In studies prior to the pandemic, findings regarding the relationship between gender and academic motivation have been mixed. While a number of studies showed weaker intrinsic motivation in man students than woman students [32,33], others found no such effect [34,35]. Year of study may also have an effect on academic motivation, with greater academic motivation among senior relative to junior undergraduates [36]. Socioeconomic status may also be related to academic motivation. For example, in a sample of Italian students, it was found that students with lower socioeconomic status (SES) showed greater vulnerability for self-motivation in school compared to their higher SES counterparts [37]. Notably, international students on average have been shown to have higher academic motivation than domestic students [38,39], but it is less clear how this may change with the pandemic. Those in higher levels of study and those who spend more time in extracurricular activities (e.g., student clubs or organizations) have been found to show higher academic motivation [40,41]. While previous data suggest that academic motivation may differ based on a range of sociodemographic factors, less is known about whether these sociodemographic differences remain in the context of the pandemic. Moreover, much less is known about whether students’ COVID-related educational experiences (mode of course delivery, willingness to return to campus, COVID-19 contraction fears) were associated with academic motivation during the transition back to in-person learning. There remains a paucity of research examining how these COVID-related experiences, along with sociodemographic variables, contribute to academic motivation among Canadian university students during this period of transition.

1.4. The Current Study

The available literature provides evidence that the mental health and academic motivation of students have declined over the course of the pandemic; however, less is known about the relationship between mental health and academic motivation during the transition period of the COVID-19 pandemic from online back to in-person learning. To fill this gap, the present study investigated the association between depression, anxiety, and stress (three key mental health indices), resilience (an important protective factor against negative mental health outcomes), and academic motivation among Canadian undergraduate students during the transition from online to campus education. It was hypothesized that depression, anxiety, and stress would negatively associate with academic motivation whereas greater resilience would positively associate with academic motivation. Additionally, the present study examined a broader set of sociodemographic and COVID-related variables that may have influenced academic life experiences during this period. Sociodemographic variables included gender, school, year of study, and living arrangement which may potentially influence academic motivation during this period. COVID-related variables included such factors as course delivery preferences, willingness to return to campus, and contraction worry. These variables were examined as covariates to determine whether mental health indices and resilience were associated with academic motivation over and above students’ demographic and COVID-related experiences. Taken together, this study sought to gain a comprehensive understanding of COVID-related behaviours and their implications in the association between mental health and academic motivation. It also aimed to explore these relationships during an adjustment period from virtual to in-person learning.

2. Methods

2.1. Sample

The study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Board of Toronto Metropolitan University (REB 2021-409) on 19 January 2022. Full-time undergraduate university students were recruited from three provinces: British Columbia, Ontario, and Quebec in Canada. Program administrators from Business, Computer Science, Criminology, Engineering, Nursing, and Psychology programs across 17 universities were invited to distribute our survey link to undergraduate students in their programs via email. See Table 1 for sample characteristics. Participation in the survey was voluntary, and informed consent was obtained prior to participation. A screening question was administered before the survey to exclude anyone who was not enrolled as a full-time undergraduate university student in the 2021/2022 academic year. There were a total of 2216 responses to the survey. After the initial data cleaning to remove incomplete responses (n = 228), the final sample included 1868 respondents. The sample is largely composed of females (71.7%) and with English as first language (70.3%). Based on the 21-Item Depression, Anxiety, and Stress Scale (DASS-21) scores [42], 33.78%, 38.38%, and 27.84% of them reported severe or extremely severe depression, anxiety, and stress symptoms, respectively. The average academic motivation scores were 5.07 (SD = 4.11). Table 1 and Table 2 present its distribution along with all sociodemographic and COVID-related experience variables.

2.2. Measures

The survey was built in Qualtrics and the data were collected between 19 January and 4 March in 2022, a period when major Canadian universities were transitioning from virtual to in-person schooling. The survey included measures of academic motivation (as the outcome variable), mental health (e.g., depression, anxiety, stress), resilience, and sociodemographic and COVID-related variables as predictors in the regression models.
The Academic Motivation Scale (College Version) [AMS-C 28]. The AMS-C 28 (or AMS) is a 28-item validated scale that assesses academic motivation in students [43]. The college version is designed for post-secondary students. The current study slightly modified the language to be less American-centric. Specifically, the term “college” was replaced with “post-secondary” across all items, as the Canadian post-secondary education system differentiates between college and university programs. The present study only collected data from university students. Participants rate each item on a 7-point Likert scale from 1 (does not correspond at all) to 7 (corresponds exactly) on how well each item corresponds to a reason that they attend post-secondary education (e.g., “Because I experience pleasure and satisfaction while learning new things”). This scale showed excellent reliability in the current study (Cronbach’s α = 0.88).
The 21-Item Depression, Anxiety, and Stress Scale (DASS-21). The DASS-21 is a 21-item scale that assesses depression, anxiety, and stress symptoms [42], including 7 items for each subscale of depression, anxiety, and stress. In this scale, participants rate to what degree each statement applied to them over the past week (e.g., “I couldn’t seem to experience any positive feeling at all”) based on a 4-point Likert scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time). The DASS-21 shows good test–retest reliability (e.g., [44]). This scale showed excellent reliability in this study (Cronbach’s α = 0.94).
The Brief Resilience Scale (BRS). The BRS is a 6-item scale that measures resilience [45]. Participants are asked to rate each item (e.g., “I tend to bounce back quickly after hard times”) using a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The BRS has demonstrated good test–retest reliability in previous research in student populations [46]. It showed excellent reliability in the current study (Cronbach’s α = 0.90).
Sociodemographic Variables. The survey included sociodemographic variables such as age, gender, school, program year of study, mature student status (i.e., whether they identified as a mature student), English as first language status, living arrangements, financial status. It also collected general health and lifestyle information, such as self-rated physical and mental health status, weight change during the pandemic, frequency of exercise and alcohol/smoking/vaping.
COVID-Related Variables. In addition to abovementioned sociodemographic variables, the survey included several questions pertaining to experiences specific to the pandemic, such as online schooling experiences and preference, home internet quality, time zone, hours spent in extracurricular activities, vaccination status, COVID-19 contraction history, and COVID-19 contraction worry (for self and family).

2.3. Data Analysis

All data analyses were conducted in IBM SPSS 24.0. For clarity purposes, categorical variables as potential predictors (sociodemographic or COVID-related) were recoded into binary or 3-level variables based on the explorative frequency analysis on the distribution of the AMS scores across each of these variables to best capture the variance of AMS. All scale questions with at least a 5-point Likert scale were treated as continuous variables based on the frequency distribution analysis. The potential categorical covariate predictors for AMS were identified through two univariate analyses of variance (ANOVA) models for sociodemographic and COVID-related variables respectively, based on a conventional cutoff score of p ≤ 0.20 [47]. The continuous sociodemographic and COVID-related predictors were identified through Pearson correlations, based on a significance cutoff score of p < 0.05. These potential predictors were subsequently entered into two hierarchical linear regression models as covariates, with mental health variables as primary predictors, and AMS as outcome variable. This holistic and inclusive approach models previous work (e.g., [48]) and it allowed us to ensure the association between the key mental health variables and academic motivation was robust even after controlling for all possibly related sociodemographic and COVID-related variables. All the mental health variables (e.g., depression, anxiety, stress, and resilience) were entered in Step 1, and potential covariates (sociodemographic or COVID-related) were added to the model in Step 2. Normality of AMS and all mental health predictors was checked through visual inspection of the QQ plots. Skewness (−0.80) and kurtosis (0.51) were well below the commonly referenced thresholds of |skewness| = 2 and |kurtosis| = 7 [49], confirming the acceptable normal distributions. Missing data points were removed with a listwise deletion approach.

3. Results

3.1. Potential Sociodemographic Covariates for Academic Motivation

A univariate ANOVA was conducted to identify potential categorical sociodemographic predictors (see Table 1). The results identified the following significant sociodemographic variables for academic motivation: school, year of study, English as first language, drinking frequency, and exercise frequency (ps ≤ 0.039). Based on the cut-off of p ≤ 0.20, gender, mature student status, and smoking frequency were also identified as potential predictors to be entered in the subsequent regression model as covariates (ps = 0.091–0.166). Pearson correlations (Table 2, top panel) revealed the following significant continuous sociodemographic correlates for academic motivation: financial satisfaction and physical and mental health status (rs = 0.18–0.23, ps ≤ 0.01). All these variables were consequently included as covariate predictors in the subsequent regression model.

3.2. Potential COVID-Related Covariates for Academic Motivation

The univariate ANOVA (Table 3) identified some significant COVID-related factors for academic motivation: preference for in-person over online schooling, not being in a different time zone, and hours spent on extracurricular activities (ps ≤ 0.042). In addition, being an international student, vaccination status, and pandemic weight changes were identified as potential predictors (ps = 0.053–0.190) based on the cutoff p ≤ 0.20. Pearson correlations (Table 2, middle panel) identified the following COVID-related correlates for academic motivation: home internet quality, whether attending in-person schooling, challengingness attending online class, and willingness to return to campus (if not yet), with absolute rs = 0.10–0.18 (ps < 0.01).

3.3. Mental Health Prediction for Academic Motivation

Importantly, academic motivation showed significant and consistent correlations with all mental health variables (depression, anxiety, stress, and resilience), absolute rs = 0.17–0.35, ps < 0.01, in the expected directions, with a negative correlation with depression, anxiety, and stress, whereas a positive correlation with resilience (Table 2, bottom panel). Two hierarchical linear regression models were conducted, one with sociographic covariates and the other with COVID-related experience covariates, and both with mental health scores as primary predictors. All the mental health indexes were entered in Step 1, and the potential sociodemographic (Table 4) or COVID-related covariates (Table 5) identified above were added in Step 2 in the corresponding regression model.

3.4. Mental Health Prediction

Both regression models identified all mental health variables as significant predictors for academic motivation in Step 1, in the model either with sociodemographic covariates (Table 4, absolute βs = 0.029–0.464, R2 = 0.143, F = 77.32, ps ≤ 0.041) or with COVID-related covariates (Table 5, absolute βs = 0.029–0.471, R2 = 0.144, F = 77.922, ps ≤ 0.035). Specifically, depression negatively predicted whereas both stress and resilience positively predicted academic motivation. The effects of depression, stress and resilience remained to be significant in Step 2, controlling for sociodemographic (Table 4, absolute βs = 0.070–0.399, R2 = 0.190, F = 12.25, ps ≤ 0.001) or COVID experience covariates (Table 5, absolute βs = 0.077–0.369, R2 = 0.197, F = 23.88, ps ≤ 0.002). However, anxiety was no longer significant in Step 2 of both models (absolute βs = 0.018–0.021 ps ≥ 0.135).

3.5. Sociodemographic and COVID-Related Predictors

Sociodemographic Predictors. The regression also identified several significant sociodemographic predictors (i.e., year of study, gender, English as first language, drinking and exercise frequency, financial satisfaction and physical health status as in Table 4). Specifically, compared to first-year students, those in the second, third, or fourth year showed lower academic motivation (β = −0.953–−0.786, ps ≤ 0.002). Men showed lower academic motivation than women (β = −0.667, p = 0.002). Those with English not as a first language showed higher academic motivation than those who identified English as first language (β = 0.503, p = 0.013). Having quit drinking alcohol negatively predicted academic motivation compared to those who drink on a daily basis (β = −2.372, p = 0.035). Those who never exercised showed lower academic motivation compared to those who exercise every day (β = −0.837, p = 0.015). Both financial satisfaction (β = 0.234, p = 0.012) and physical health status (β = 0.315, p = 0.004) positively predicted academic motivation among Canadian undergraduate students.
COVID-Related Predictors. The regression also identified significant COVID-related predictors for academic motivation (i.e., in-person/online course preference, time zone, international student status, pandemic weight change, home internet quality, mode of course delivery last term, challengingness attending online courses, and willingness to return to campus as in Table 5). Specifically, no preference or in-person course preference positively contributed to academic motivation compared to online course preference (βs = 0.687–0.944, ps ≤ 0.009). Being in the same time zone positively predicted academic motivation than those living in a different time zone (β = 0.687, p = 0.001). Compared to international students, domestic students showed lower academic motivation (β = −0.482, p = 0.046). Further, compared to those who lost weight during the pandemic, those who maintained a stable weight (i.e., no change) showed lower academic motivation (β = −0.511, p = 0.026). Furthermore, more reliable internet quality, more in-person courses in F2021, lower challenge attending online classes, and higher willingness to return to campus showed positive predictions for academic motivation (absolute βs = 0.128–0.401, ps ≤ 0.049).

4. Discussion

The current study sought to identify mental health, sociodemographic and COVID-related factors that are associated with academic motivation of Canadian undergraduate university students in later stages of the COVID-19 pandemic when most Canadian universities were transitioning from online to in-person education on campus. In line with our hypotheses, the results showed that higher depression and anxiety were associated with lower academic motivation whereas higher resilience was associated with higher academic motivation. However, the effect of anxiety was not sustained after controlling for sociodemographic or COVID-related variance. Contrary to our hypothesis, stress was positively associated with academic motivation. In addition, academic motivation was also significantly associated with several sociodemographic variables, including year of study, gender, English as first language status, alcohol consumption, and exercise status. Given that several of these sociodemographic factors were included as contextual covariates rather than primary hypothesis-driven variables, these associations were interpreted cautiously and in relation to their effect sizes. Interestingly, of the COVID-related variables, preference for in-person learning, greater in-person course delivery, and willingness to return to campus were significantly associated with higher academic motivation.

4.1. Mental Health and Academic Motivation

With respect to the associations between mental health indices and academic motivation, our findings are mostly consistent with previous literature e.g., [27,28] that revealed a negative relationship between academic motivation and anxiety or depressive symptoms among students in the context of the COVID-19 pandemic. Notably, however, only depression remained to be a significant predictor for academic motivation after controlling for sociodemographic or COVID-related covariates. The sustained association between depression and academic motivation may reflect in part the inherent relationship between depression and motivation given that a central feature of depression is a loss of interest or pleasure in activities [50]. Data from the current study suggest that students reporting greater depressive symptoms also reported lower academic motivation. Depression is also characterized by low energy, increased fatigue, and reductions in concentration, all of which are features associated with lower academic motivation [51].
Interestingly, anxiety was not significantly associated with academic motivation after controlling covariates. This may be due to the complex non-linear relationship between motivation and anxiety, where different levels of anxiety may be associated with specific motivational profiles. For example, school-related worry (e.g., to reach a particular grade and perform at a particular level) might be associated with increased motivation and higher engagement in schoolwork. However, if the anxiety level is clinically significant, it may have a harmful impact on motivation. Although the current study did not collect data on clinical levels of mental health indices, the mean anxiety score of the current sample was 13.33, falling within the moderate level, with notable variability (SD = 9.97). This may have masked the prediction of anxiety. It is also possible that the association between anxiety and motivation was partially accounted for by related sociodemographic and COVID-related variables. In this sense, the lack of a statistically significant association should not be interpreted as evidence that anxiety is independent of academic functioning.
Surprisingly, greater stress was associated with higher academic motivation. Previous research on the association between stress and academic motivation has been inconclusive. Some studies have found a positive association [52,53,54,55], while others have revealed a largely negative association [56]. In the context of the COVID-19 pandemic specifically, Rahe and Jansen [57] found that higher stress was associated with higher academic motivation, which aligns with the findings from the current study. The most parsimonious explanation for these discrepant findings is that this relationship may change based on the level of perceived stress. That is, a moderate level of stress may be associated with an optimal level of performance by increasing arousal, leading to greater attention and focus of cognitive pursuits such as academics. Furthermore, when academic-related or other environmental stressors exceed a student’s perceived ability to cope, it may be associated with a decrease in motivation. In the current sample, the mean level of stress was 18.55 (out of a total possible 42; SD = 10.09), falling in the mild–moderately stressed category of the DASS-21. It is possible that within this range, stress level might be positively associated with academic motivation.
These findings come in line with other stress-response theories. For example, according to the challenge and threat model [58], individuals’ responses to stressors are influenced by their appraisal of the situation. Those who perceive stress as a challenge that can be overcome with effort may experience increased motivation. Lastly, pandemic-induced challenges and stress might enhance a need to increase agency and mastery of life, and thereby strengthened academic motivation among students.

4.2. Sociodemographic Factors for Academic Motivation

The current study identified several sociodemographic variables that were associated with academic motivation in the context of COVID-19. However, these variables were included to account for the broader social context for the in-person transition in an exploratory manner. Therefore, the significant associations should be interpreted as exploratory, rather than as conclusive evidence of a causal relationship. The results showed that academic motivation was highest in first-year students compared to second-, third- and fourth-year students, consistent with the earlier finding [58] that motivation decreases over years spent in school. This is likely due to the cumulative academic burden and related requirements that wear on students’ motivation to learn over time. In the context of the current study, students at different levels of study were differentially impacted by pandemic interruption of in-person education. For example, senior students may have felt less motivated in virtual environments, likely due to the lack of strict academic environments (i.e., online testing), individualized guidance and social support, which are vital for upper year curriculum. Conversely, first-year students who had no prior in-person university experience may have felt more curious and motivated to perform well in an adaptation to university life. Consistent with previous findings [59,60] women were also identified to have higher academic motivation on average compared to men.
International students and English as Second Language (ESL) students showed differentially higher academic motivation. This finding should be interpreted cautiously, as it may capture several co-related factors, such as migration history/status, language background, and financial status. These students are likely from families with a solid financial foundation. They may face language/cultural adaptation challenges which might reinforce their motivation to put extra effort into achieving academic success. Please note the interpretation remains speculative. Nevertheless, the findings add to a rich body of literature that identifies challenges faced by international students during the pandemic, such as a greater risk for developing mental health issues, separation from their family or loved ones due to school closures and travel restrictions during the pandemic [61,62,63].
The current study found that being in the same time zone as your school was also associated with higher motivation. It may be that being in the same time zone may have helped improving engagement with course activity. Interestingly, having stopped/quitted drinking was associated with poorer academic motivation than regular daily drinkers. The speculation is that the extra burden/stress related to quitting drinking in a situation of vulnerability could have interfered with academic motivation. Additionally, students who self-reported a better physical health status scored higher in academic motivation. Those who never exercised had lower academic motivation compared to those who exercise every day. These findings support previous findings of positive relationship between exercise and academic motivation in post-secondary students due to a variety of factors such as stress-management, socialization opportunities, and intrinsic motivation [64]. The current study also found that greater financial satisfaction was associated with greater motivation, consistent with previous finding of financial well-being as a predictor for college persistence during the pandemic [65]. Greater financial satisfaction and security may have allowed students to focus on their academics, while students with lower financial satisfaction may have been burdened by other related stressors (e.g., employment, financial instability), which can impact ability to stay motivated for school.

4.3. COVID-Related Experiences for Academic Motivation

Results indicated that students who preferred in-person course delivery or those who had no preference scored higher on academic motivation compared to those who preferred online learning. Relatedly, more in-person course engagement in the past semester and higher willingness to be back for in-person education were all associated with greater academic motivation. There may be a few explanations for these findings. First, many students struggled to adapt to virtual learning environments and showed decreased academic motivation, in the early stages of the pandemic. As such, the return to in-person campus education might boost their academic motivation. Furthermore, in-person education might be related to the increased access and opportunities for social/community connection/engagement (i.e., student clubs, classroom conversation) that might support students’ psychological wellbeing and motivation to strive.
The current study was conducted during the period when all schools were in the transition period back to an in-person education mode. Many students might have been eager for in-person interactions after a long time of social isolation and online learning. In support of this, more students (48.6%) reported a preference for in-person learning over those with a preference for online learning (33.2%), and 18.3% reported no preference.
Better internet quality and lower reported challenges attending online class were also associated with higher academic motivation. These might be related to living environment, family socioeconomic status, and other environmental challenges which make online learning less accessible, less effective, and less convenient for certain groups of students. These factors might be barriers for online education that are associated with lower participation and engagement and thus may reduce a student’s motivation to learn. Importantly, these results highlight that accessibility of online learning is an important consideration of academic motivation in a virtual environment.
Outside of education-specific COVID-related variables, results also suggest that no change in weight was associated with greater academic motivation compared to those who reported a decrease in weight. This may suggest a potential relationship between weight stability during a pandemic and academic motivation. For example, it is possible that maintaining a stable weight during challenging times can contribute to positive feelings of self-control and accomplishment given the relationship between weight and self-esteem and self-efficacy [66]. Nevertheless, given the exploratory nature of the analyses, this finding is interesting but should be taken with caution.

5. Limitations and Conclusions

A key limitation to consider was that a number of the sociodemographic and COVID-related variables were coded into categorical variables which may have affected sensitivity of the data, limiting how the relationships between these variables and the outcome variable were captured. Another limitation was the inclusion of a broad range of sociodemographic and COVID-related covariates as part of our exploratory approach, which increases the possibility of small-effect significant associations without a strong theoretical basis. Therefore, the sociodemographic and COVID-related covariates should be treated as exploratory and explained with caution. Another limitation to consider was the larger proportion of Ontario respondents as compared to other provinces, but the large sample size may partially buffer the effect of this sampling bias.
The present study takes a novel approach by surveying undergraduate university students across Canada to identify the mental health predictors for academic motivation. To our knowledge, this is the first study surveying a national Canadian sample of undergraduate university students on their academic motivation in the context of the transition from online back to in-person learning. Founded on a rich body of literature from the early stages of the pandemic when academic institutions first moved online, this study provides novel insights on the reserve transition at the late stage of the pandemic in a Canadian context. Importantly, academic motivation showed a negative association with depression and anxiety, but a positive association with stress and resilience. This study also identified unique sociodemographic and COVID-related factors associated with academic motivation. Nevertheless, these contextual factors should be interpreted as exploratory and too preliminary to draw any strong conclusions. The results suggest that university students’ academic motivation is not a stable trait but rather a malleable experience that is impacted by mental health and related experiences. Future research would benefit from understanding more nuanced mechanisms of mental health and academic motivation to implement interventions that address both constructs relatedly.

Author Contributions

M.J.M. played a leading role in study conceptualization, data collection, data analysis and manuscript preparation. K.K. played a leading role in study conceptualization and manuscript preparation. L.Y. played a leading and supervisory role in study conceptualization, data curation, project administration, and supervision of data analysis and manuscript preparation. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by a subgrant from the New Frontiers Research Fund through the Canadian Institutes of Health Research (CIHR) [NFRF-2019-00012] and an internal Social Sciences and Humanities Research Council (SSHRC) Grant [892-2022-3086] awarded to Lixia Yang.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Toronto Metropolitan University Research Ethics Board (protocol code REB 2021-409 and date of approval 19 January 2022).

Informed Consent Statement

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

Data Availability Statement

The original data presented in the study are openly available in OSF at https://osf.io/8rsqw/?view_only=1b1ef3e09f4c4546abbca66635bac82d (accessed on 7 March 2024).

Acknowledgments

We would like to thank our research assistants (Linke Yu and Natalie Loserro) for their support in data collection and cleaning.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Table 1. Sample distribution and academic motivation as stratified by categorical sociodemographic variables.
Table 1. Sample distribution and academic motivation as stratified by categorical sociodemographic variables.
VariablesN (%)Academic Motivation
M (SD)Fp
GenderWoman1339 (71.7)5.18 (4.07)2.400.091
Man480 (25.7)4.87 (4.20)
Other49 (2.6)3.00 (4.20)
SchoolBishop’s University163 (8.7)5.45 (4.08)2.140.019
McGill University457 (24.5)5.44 (4.18)
Queen’s University39 (2.1)5.49 (3.73)
Toronto Metropolitan University320 (17.1)4.45 (3.93)
Simon Fraser University138 (7.4)4.18 (4.17)
University of British Columbia153 (8.2)5.24 (4.07)
University of Guelph80 (4.3)4.87 (4.20)
University of Toronto187 (10)5.32 (3.94)
Western University110 (5.9)6.05 (3.79)
Other200 (10.7)4.59 (4.45)
ProgramBusiness139 (7.4)5.79 (3.87)1.290.260
Computer Science163 (8.7)4.84 (4.16)
Criminology312 (16.7)4.45 (4.18)
Engineering227 (12.1)4.75 (3.99)
Nursing158 (8.5)5.18 (3.84)
Psychology643 (34.6)5.31 (4.13)
Other226 (12.1)5.25 (4.23)
Year of StudyFirst year327 (17.5)5.65 (3.71)3.970.003
Second year446 (23.8)4.81 (4.16)
Third year607 (32.5)5.07 (4.10)
Fourth year371 (19.9)4.92 (4.41)
Fifth year or above117 (6.3)4.95 (3.88)
English as First LanguageYes1313 (70.3)4.95 (4.02)4.250.039
No554 (29.7)5.37 (4.29)
Mature StudentYes166 (8.9)5.42 (4.11)2.030.154
No1701 (91.1)5.02 (4.11)
Living ArrangementAlone207 (11.1)4.92 (4.38)0.530.663
With family842 (45.1)4.85 (4.10)
With friends/roommates762 (40.8)5.35 (4.02)
Other57 (3.1)5.28 (4.30)
Smoking FrequencyEvery day153 (8.2)4.43 (4.25)1.620.166
1–2 times a week94 (5.0)4.89 (3.84)
1–3 times a month 147 (7.9)5.53 (3.93)
Never1375 (73.6)5.13 (4.10)
Quit98 (5.2)4.65 (4.34)
Drinking FrequencyEvery day14 (0.7)4.90 (3.56)5.980.000
1–2 times a week412 (22.1)5.64 (3.77)
1–3 times a month820 (43.9)5.18 (3.98)
Never559 (29.9)4.69 (4.32)
Quit61 (3.3)3.19 (5.07)
Exercise FrequencyEvery day442 (23.7)5.82 (3.84)11.730.000
1–2 times a week 750 (40.1)5.09 (4.00)
1–3 times a month464 (24.8)4.90 (4.31)
Never212 (11.3)3.85 (4.25)
Note. Bold p values refer to the variables identified as potential predictors for academic motivation (ps < 0.20) and these variables would be entered into the corresponding regression model.
Table 2. Correlations between continuous predictors and academic motivation.
Table 2. Correlations between continuous predictors and academic motivation.
VariablesAcademic Motivation
M (SD)r
Sociodemographic
Age20.58 (1.64)0.01
Financial Satisfaction1 = Very dissatisfied; 5 = Very satisfied3.13 (1.02)0.18 **
Physical Health Status1 = Very poor; 5 = Very good3.00 (0.96)0.19 **
Mental Health Status (general)1 = Very poor; 5 = Very good2.58 (1.02)0.23 **
COVID-related
Home Internet Quality1 = Extremely reliable;
5 = Extremely unreliable		2.12 (0.83)−0.10 **
Attend School Fall 20211 = All remotely; 5 = All in person2.40 (1.41)0.10 **
Challenge Attending Online Class1 = Extremely challenging;
5 = Extremely unchallenging		2.74 (1.22)0.12 **
Willing to Return to Campus1 = Extremely unwilling;
5 = Extremely willing		3.52 (1.24)0.18 **
Pandemic is an Ongoing Threat1 = Completely agree;
5 = Completely disagree		1.94 (1.09)−0.01
Mental health predictors
Depression 16.878 (11.83)−0.35 **
Anxiety 13.33 (9.97)−0.22 **
Stress 18.55 (10.09)−0.17 **
Resilience 3.14 (0.83)0.21 **
Note. Bold r values denote significant correlations between continuous predictors and the outcome variable (i.e., academic motivation). These variables would be entered into the corresponding regression models. ** p < 0.01 (2-tailed).
Table 3. Sample distribution and academic motivation as stratified by categorical COVID-related variables.
Table 3. Sample distribution and academic motivation as stratified by categorical COVID-related variables.
Variables Academic Motivation
N (%)M (SD)Fp
Prefer Online vs. In personPrefer online620 (33.2)4.23 (4.26)15.860.000
No preference341 (18.3)5.17 (4.07)
Prefer in person907 (48.6)5.61 (3.91)
Different Time ZoneYes402 (21.5)4.70 (4.12)4.160.042
No1466 (78.5)5.18 (4.10)
International StudentYes295 (15.8)5.56 (3.80)2.930.087
No1573 (84.2)4.98 (4.16)
Hours Spent on Extracurricular0769 (41.2)4.51 (4.44)5.390.001
1–5747 (40.0)5.42 (3.78)
5–10227 (12.2)5.73 (3.71)
Over 10125 (6.7)5.25 (4.13)
Vaccination Status3 doses957 (51.2)5.29 (4.01)1.660.190
2 doses885 (47.4)4.86 (4.21)
Unvaccinated/1 dose24 (1.3)4.25 (3.56)
COVID DiagnosisYes423 (22.6)5.10 (4.32)0.090.763
No1444 (77.3)5.06 (4.04)
Family Diagnosed with COVIDYes802 (42.9)5.13 (4.29)0.52 0.470
No1065 (57.0)5.03 (3.96)
Pandemic Weight ChangeDecreased387 (20.7)5.38 (4.20)2.950.053
Stayed the same846 (45.3)5.19 (3.91)
Increased635 (34.0)4.72 (4.28)
Note. Bold p values refer to the variables identified as potential predictors for academic motivation and these variables would be entered into the corresponding regression model.
Table 4. Regression on mental health and sociodemographic predictors for academic motivation.
Table 4. Regression on mental health and sociodemographic predictors for academic motivation.
StepPredictors and CodingAcademic Motivation
β95% CIR2F
1Depression−0.138 ***−0.159, −0.1170.14377.32 **
Anxiety−0.029 *−0.056, −0.001
Stress0.079 ***0.051, 0.107
Resilience0.464 ***0.223, 0.706
2Depression−0.125 ***−0.148, −0.1020.19012.25 **
Anxiety−0.021−0.048, 0.006
Stress0.070 ***0.041, 0.098
Resilience0.399 ***0.153, 0.644
SchoolTMU (reference)
Bishop’s University0.343−0.375, 1.061
McGill University0.429−0.136, 0.994
Queen’s University0.755−0.498, 2.008
Simon Fraser University−0.422−1.182, 0.338
University of British Columbia0.622−0.108, 1.352
University of Guelph−0.053−0.984, 0.877
University of Toronto0.529−0.158, 1.216
Western University0.768−0.086, 1.623
Other−0.043−0.715, 0.629
GenderWomen (reference)
Men−0.667 **−1.083, −0.251
Other−0.151−1.240, 0.938
Year of StudyFirst year (reference)
Second year−0.832 **−1.383, −0.281
Third year−0.786 **−1.313, −0.260
Fourth year−0.953 **−1.544, −0.361
Fifth year or above−0.795−1.627, 0.036
English as First LanguageYes (reference)
No0.503 *0.106, 0.899
Mature Student StatusYes (reference)
No −0.290−0.906, 0.327
Smoking FrequencyEvery day (reference)
Once or twice a week −0.317−1.290, 0.657
1–3 times a month0.202−0.655, 1.059
Never0.306−0.354, 0.966
Quit−0.095−1.058, 0.867
Drinking FrequencyEvery day (reference)
Once or twice a week−0.112−2.137, 1.912
1–3 times a month−0.522−2.536, 1.493
Never−0.894−2.926, 1.138
Quit−2.372 *−4.577, −0.167
Exercise FrequencyEvery day (reference)
Once or twice a week−0.200−0.671, 0.271
1–3 times a month−0.053−0.601, 0.495
Never−0.837 *−1.514, −0.160
Financial Status Satisfaction
(1 = very dissatisfied; 5 = very satisfied)		0.234 * 0.051, 0.417
Physical Health Status
(1 = very poor; 5 = very good)		0.315 **0.102, 0.528
Mental Health Status
(1 = very poor; 5 = very good)		−0.029−0.255, 0.197
Note. TMU = Toronto Metropolitan University. *** p < 0.001; ** p < 0.01; * p < 0.05.
Table 5. Regression on mental health and pandemic predictors for academic motivation.
Table 5. Regression on mental health and pandemic predictors for academic motivation.
StepPredictors and CodingAcademic Motivation
β95% CIR2F
1Depression−0.138 ***−0.159, −0.1170.14477.92 **
Anxiety−0.029 *−0.057, −0.002
Stress0.079 ***0.051, 0.108
Resilience0.471 ***0.230, 0.713
2Depression−0.131 ***−0.151, −0.1100.19723.88 **
Anxiety−0.018−0.045, 0.009
Stress0.077 ***0.049, 0.105
Resilience0.369 **0.132, 0.607
Prefer Online or In personPrefer online (reference)
No preference0.687 **0.170, 1.205
Prefer in person0.944 ***0.443, 1.446
Different Time ZoneYes (reference)
No0.687 ***0.263, 1.110
International StudentYes (reference)
No−0.482 *−0.954, −0.009
Hours Spent in ExtracurricularZero (reference)
1–50.375−0.006, 0.756
5–100.448−0.115, 1.011
More than 100.019−0.694, 0.732
Vaccination Status3 does (reference)
2 doses −0.206−0.549, 0.137
Unvaccinated/1 dose−0.316−0.787, 0.155
Pandemic Weight ChangeDecreased (reference)
No change−0.511 *−0.962, −0.061
Increased−0.316−0.787, 0.155
Home Internet Quality
(1 = extremely reliable; 5 = extremely unreliable)		−0.293 **−0.501, −0.086
Course delivery Fall 2021
(1 = all remote; 5 = all in person)		0.128 *0.000, 0.255
Challenge Attending Online Classes
(1 = extremely challenging; 5 = extremely unchallenging)		0.401 ***0.248, 0.555
Willing to Return to Campus
(1 = extremely unwilling; 5 = extremely willing)		0.316 ***0.141, 0.490
Note. *** p < 0.001; ** p < 0.01; * p < 0.05.
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Marshall, M.J.; Kandasamy, K.; Yang, L. Academic Motivation of Canadian Undergraduate Students: Mental Health, Sociodemographic and COVID Predictors. Psychiatry Int. 2026, 7, 151. https://doi.org/10.3390/psychiatryint7040151

AMA Style

Marshall MJ, Kandasamy K, Yang L. Academic Motivation of Canadian Undergraduate Students: Mental Health, Sociodemographic and COVID Predictors. Psychiatry International. 2026; 7(4):151. https://doi.org/10.3390/psychiatryint7040151

Chicago/Turabian Style

Marshall, Max J., Kesaan Kandasamy, and Lixia Yang. 2026. "Academic Motivation of Canadian Undergraduate Students: Mental Health, Sociodemographic and COVID Predictors" Psychiatry International 7, no. 4: 151. https://doi.org/10.3390/psychiatryint7040151

APA Style

Marshall, M. J., Kandasamy, K., & Yang, L. (2026). Academic Motivation of Canadian Undergraduate Students: Mental Health, Sociodemographic and COVID Predictors. Psychiatry International, 7(4), 151. https://doi.org/10.3390/psychiatryint7040151

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