Abstract
Self-regulated learning (SRL) practices and academic challenges are crucial factors in student success and academic performance. Frequent cannabis and alcohol use can negatively affect emerging adults’ cognitive abilities which in turn diminishes their academic performance. Research has not yet fully explored how substance use effects university student engagement with SRL practices (e.g., task understanding) and academic challenges (e.g., motivational challenges). Participants (N = 1,247) were first- and second-year undergraduate students at five Canadian Universities (Mage = 19.1 years). Cannabis use negatively associated with reports of task understanding and metacognitive monitoring and positively associated with five academic challenges: goal and time management, cognitive, social-emotional, metacognitive, and motivational challenges. Alcohol use negatively associated with reports of goal and time management, and task understanding, and positively associated with academic social engagement practices, social-emotional and initiating-sustaining engagement challenges. Findings enhance understanding about how specific substances affect emerging adults on academic success precursors early in their transition to university.
Emerging adulthood is a crucial developmental stage characterized by significant growth and cognitive change (Dahl, 2004). For instance, this period is crucial for the establishment of self-regulation skills (Zimmermann & Iwanski, 2014), including self-regulated learning (SRL) that are essential for academic success (Hadwin & Winne, 2012). However, this period also presents various challenges across neurobiological (Scott et al., 2018), social (La Greca & Harrison, 2005), and academic (Romero et al., 2014) domains. These challenges can hinder the development of effective self-regulation strategies, ultimately increasing the likelihood of emerging adults engaging in risky behaviors, such as substance use (Sussman & Arnett, 2014). During 2019–2020, 33% and 77% of 17- to 25-year-old university students reported using cannabis and alcohol in the past month, respectively (Health Canada, 2019). Extant research has illustrated the effects of substance use on both academic (Horwood et al., 2010) and cognitive outcomes (Scott et al., 2018).
Speaking to cognitive outcomes, Dougherty et al. (2013) and Sussman and Arnett (2014) found substance use in emerging adulthood was associated with cognitive deficits (e.g., attention, decision-making, impulsivity, memory) and self-regulation, respectively. In terms of academic success and substance use, most research focuses on institutional outcomes, such as institutional Grade Point Average (GPA) (Buckner et al., 2010; Meda et al., 2017; Páramo et al., 2020) and frequently missing class (Arria et al., 2013). Consequently, there is a lack of understanding regarding the unique relationships between substance use and emerging adults’ SRL practices and academic challenges. Hence, this study aims to fill this gap by exploring how alcohol and cannabis use influences emerging adults’ SRL practices and the academic challenges they face during their first semester at university. By examining these relationships, we seek to provide a deeper understanding of the mechanisms through which substance use impacts academic success beyond traditional institutional outcomes.
Traditionally, studies examining the relationship between academic success and frequent alcohol or cannabis consumption among university students have focused on institutional outcomes, such as self-reported GPA (Bolin et al., 2017; Martinez et al., 2015), institutional GPA (Meda et al., 2017; Páramo et al., 2020), enrollment status (Arria et al., 2013a), class absences (Arria et al., 2013, 2015; Caldeira et al., 2008), dropout/retention rates (Suerken et al., 2016), and degree attainment (Thompson et al., 2019; Tucker et al., 2005). While these measures capture important academic milestones, they overlook the processes that contribute to long-term academic success, specifically SRL practices and academic challenges.
Existing research consistently links frequent alcohol use to poorer academic performance, including increased class absences (Arria et al., 2013; Bolin et al., 2017) and lower GPA (Conway & DiPlacido, 2015; Meda et al., 2017). Similarly, frequent cannabis users demonstrate poorer academic outcomes as measured by both self-reported and institutional GPA, cross-sectionally and longitudinally. For example, Meda et al. (2017) and Páramo et al. (2020) found that frequent cannabis use was associated with lower institutional GPA and self-reported GPA, respectively. Even though these findings highlight the consequences of frequent alcohol and cannabis use on institutional outcomes, they overlook the underlying mechanisms, such as SRL practices and academic challenges, which are crucial for achieving academic success. Therefore, this study goes beyond traditional institutional outcomes (e.g., GPA) to explore how alcohol and cannabis use affects students’ SRL practices and academic challenges during their first semester of university.
SRL is conceptualized as an active and constructive process that allows learners to metacognitively monitor, evaluate, and regulate their (meta)cognition, behaviors, motivation, and emotions (Efklides, 2011; Pintrich, 2000; Winne & Hadwin, 1998, 2008; Zimmermann & Iwanski, 2014). Winne and Hadwin's (1998) SRL model comprises four loosely sequenced recursive phases of (1) task understanding, (2) goal setting and planning, (3) task enactment, and (4) small- and large-scale adaptation. In the first phase, students gather information from their internal and external environments to construct their own perceptions of academic tasks. In the second phase, students set goals and construct plans using relevant strategies to achieve those goals. In the third phase, students engage in various strategies to complete tasks. Finally, in the fourth phase, students adapt and modify their existing strategies for future tasks (Winne & Hadwin, 2008). Engagement in these phases is referred to as “SRL practices,” while difficulties encountered during these phases are termed “academic challenges.” Both SRL practices and academic challenges play a critical role in academic success (Hadwin & Winne, 2012).
Regarding the former, SRL practices help students to regulate their (meta)cognition, emotion, motivation, and behaviors during the learning processes and to achieve academic success (Hadwin et al., 2022; Pintrich, 2000). For example, SRL strategies enable students to set academic goals, monitor their progress, and adjust their learning strategies when faced with difficulties. A recent study by Zhang et al. (2022) illustrated the importance of SRL practices in academic success, showing that university students who concentrate on mastering their own learning demonstrate higher levels of academic success. As for the latter, academic challenges pertain to both academic and socioemotional obstacles that students encounter, which can hinder learning and overall academic success (Koivuniemi et al., 2017). For instance, motivational challenges—such as feeling unmotivated or disengaged from course material—can negatively impact students’ academic progress. A student might understand how to manage their time, but if they lack motivation to attend classes or complete assignments, this challenge can still prevent them from achieving academic success. However, academic challenges can also serve as opportunities for students to engage with SRL practices and take control of their (meta)cognition, motivation, behavior, and emotions (Hadwin et al., 2022; Wu et al., 2024).
In the present study, we measure both SRL practices and academic challenges with multiple components. SRL practices are operationalized as (1) task understanding, (2) metacognitive monitoring, (3) goal management, (4) time management, (5) adapting, and (6) academic social engagement; whereas, academic challenges are operationalized as (1) goal and time management, (2) cognitive, (3) social-emotional, (4) metacognitive, (5) motivational, and (6) initiating-sustaining engagement challenges.
Given the prevalence of alcohol and cannabis use among university students, it is crucial to examine how these substances may affect the processes underlying academic success, specifically SRL practices and academic challenges. Extant research shows that alcohol and cannabis use places university students at higher risk for cognitive (Dougherty et al., 2013; Scott et al., 2018) and academic (Arria et al., 2013; Buckner et al., 2010; Meda et al., 2017; Páramo et al., 2020) deficits. For example, a recent meta-analysis by Scott et al. (2018) found a small but significant negative effect of heavy cannabis use on adolescent and young adult cognitive functioning (e.g., learning, executive function, processing speed, and attention), while Arria et al. (2013) and Meda et al. (2017) found links between consumption of alcohol in university students and various academic outcomes, such as frequent class absences and lower, respectively. Therefore, this study builds on previous research focused on institutional outcomes like GPA (Thompson et al., 2019; Tucker et al., 2005) by incorporating student reports of SRL practices and challenges, based on SRL theories (Efklides, 2011; Pintrich, 2000; Winne & Hadwin, 1998, 2008). We hypothesize that these deficits may lead to reduced engagement in SRL practices (e.g., completing academic tasks, managing time, and selecting learning strategies) and increased academic challenges (e.g., difficulties with studying, managing assignments, and attending class).
In the present study, we analyzed a large sample of first- and second-year undergraduate students from five Canadian universities (N = 1,247) to explore the extent to which self-report alcohol or cannabis use (exogenous variables) were associated with SRL practices or academic challenges (endogenous variables). This was the first study of its kind. Thus, neither the study nor the analysis plan was pre-registered on a publicly available platform and the results should be considered exploratory.
Materials and Methods
Design
This study used an online cross-sectional design. The independent variables included alcohol and cannabis use. The dependent variables included six SRL practices (i.e., task understanding, metacognitive monitoring, goal management, time management, adapting, and academic social engagement) and six academic challenges (i.e., goal and time management, cognitive, social-emotional, metacognitive, motivational, and initiating-sustaining engagement challenges). Potential confounders that were statistically controlled included: sex at birth (male, female, other) and year of study (first- or second year).
Participants
Sample consisted of first- or second-year undergraduate students, between the ages of 18 and 25 years old. Students were sampled from five Canadian universities. The final sample included 1,247 participants (Mage = 19.1 years; SDage = 1.44). Demographic data collected were age, sex at birth, year of study, and international student status.
Procedure
During 2021–2022, a cross-sectional, self-report survey was conducted across five Canadian universities, as part of a randomized controlled trial for the UniVenture Program. The data collection sites were chosen to represent a broad spectrum of Canadian universities, ensuring a mix of geographical locations (east to west), settings (rural and urban), and sizes (large and small). The UniVenture Project is based on the PreVenture program, which targets personality traits for the prevention and early intervention of substance use (Conrod et al., 2010; Kelly et al., 2020). Ethical approval was obtained from each participating institution.
The survey was administered online, with three universities recruiting participants via direct email to all first- and second-year undergraduate students. The other two universities utilized social media advertisements, emails to various university-affiliated student groups, and SONA, a platform for students to participate in research studies. Since one of the universities had a predominantly French-speaking population, French versions of the questionnaires were provided at that site. Data quality was maintained through the inclusion of screening questions. After completing the survey, participants were given access to mental health resources, such as information about student wellness services available on campus.
Measures
Alcohol and Cannabis Use
Two items from the 20-item Co-Venture Drug Use Battery (O’Leary-Barrett et al., 2017) were used to measure university students’ alcohol and cannabis use (the predictors). This Battery was chosen instead of other measures, such as the Alcohol Use Disorders Identification Test (AUDIT), as it is a short form version of the Detection of Alcohol and Drug Problems in Adolescents (DEP-ADO) that has previously psychometrically validated in adolescents and emerging adult populations (Landry et al., 2004). Participants responded on a 10-point Likert scale: 1 = never, 2 = used in my lifetime, but not in the past 12 months, 3 = used in the past 12 months, but not in the past 3 months, 4 = used in the past 3 months, 5 = monthly or less, 6 = 2–3 times a month, 7 = 2–3 times a week, 8 = 4 times a week or more, 9 = 4 times or more a day, and 10 = I prefer not to say (scored as missing). Higher scores on both the alcohol and cannabis items indicate more frequent substance use.
SRL Practices
The SRL-Practices Scale (SRL-P; Hadwin et al., 2021) was used to measure students’ perceptions about their engagement in practices that foster SRL. Hadwin et al. (2022) found good internal consistency of the measure in university student populations. To estimate the reliability of each subscale, McDonald's Omega (ω) was calculated (Hayes & Coutts, 2020). The SRL-P comprises 25 items yielding six subscales related to (1) task understanding (e.g., “Asked myself if I know what is important to learn”; five items; ω = .77), (2) goal management (e.g., “Set goals for my work”; five items; ω = .88), (3) time management (e.g., “Created a timeline or schedule; three items; ω = .60), (4) metacognitive monitoring (e.g., “Asked myself if I am understanding the material”; three items; ω = .82), (5) adapting (e.g., “Modified my plans for the task”; six items; ω = .83), and (6) academic social engagement (e.g., “Got to know people in the class”; three items; ω = .76). Participants rated their level of agreement with each statement on a 5-point Likert scale ranging from (1) strongly disagree to (5) strongly agree. Higher scores indicate higher reports of engagement with SRL practices. The internal consistencies of the six subscales were acceptable to good according to Hayes and Coutts’s (2020) cut-offs (i.e., ω ≥ .60 is “acceptable”; ω ≥ .83 is “good”).
Academic Challenges
The SRL-Challenges Scale (SRL-C; Hadwin et al., 2021) was used to assess students’ academic challenges. It has good psychometric properties when used in university student populations (Hadwin et al., 2022). The reliability of each subscale was assessed using Hayes and Coutts’s (2020) McDonald's Omega (ω) cut-offs (i.e., ω ≥ .60 is “acceptable”; ω ≥ .83 is “good”). The scale comprises 27 items with six subscales related to (1) motivation challenges (e.g., “Persisting when things got tough”; three items; ω = .78), (2) metacognitive challenges (e.g., “Knowing how or when to fix strategies/study skills”; eight items; ω = .93), (3) cognitive challenges (e.g., “Remembering things I learned; six items; ω = .89), (4) initiating and sustaining engagement challenges (e.g., “Wanting to do my schoolwork”; four items; ω = .82), (5) socio-emotional challenges (e.g., “Taking care of my mental health and well-being”; six items; ω = .84), and (6) goal and time management challenges (e.g., “Keeping my commitments or goals”; four items; ω = .83). Overall, internal consistencies of the six subscales were acceptable to good (Hayes & Coutts, 2020). Responses were reported on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree. Higher scores indicate greater levels of students reported struggles in managing aspects of studying.
Analytic Approach
Descriptive statistics (means, standard deviations) and bivariate correlations were calculated and explored. Due to the small number of missing data, pair-wise deleting methods used are unlikely to bias the results.
Structural equation modeling (SEM) was used to determine the associations of alcohol and cannabis use with SRL practices and academic challenges. Four SEM path models were carried out using the lavaan package in R (Rosseel, 2012) with the robust maximum likelihood estimator (i.e., MLM) with standard errors and a means-adjusted chi-square test statistic as it is the optimal approach, robust to non-normal data. Models 1 and 2 reflect the effects of alcohol use frequency on student reports of SRL practices and academic challenges, respectively; whereas, Models 3 and 4 reflect cannabis use frequency on SRL practices and academic challenges, respectively (Figure 1). Four recommendations for assessing model parameters and goodness of fit were selected (Hu & Bentler, 1999): (a) comparative fit index (CFI) values ≥ 0.90, (b) the Tucker–Lewis index (TLI) ≥ 0.90, (c) the standardized root-mean-square residual (SRMR) ≤ 0.08, and (d) the 90% confidence interval (CI) accompanying root-mean-square error of approximation (RMSEA) ≤ 0.08. Models within these parameters were considered to have adequate model fit. Within each model, variations across five data collection sites, year of study, and sex at birth (i.e., male or female) were controlled. All models had adequate model fit (see Tables 3–6). We accounted for the large number of cannabis non-users in our sample by entering cannabis use as both a binary (i.e., no use vs. used at least once) and continuous variable (i.e., frequency of use). This allowed us to evaluate the distinct qualitative effects of being a cannabis user/non-user as well as the quantitative effect of frequency of cannabis use. We used this approach for cannabis to ensure that our estimates of the effects of cannabis use frequency on SRL practices and academic challenges were not biased by students who had never used cannabis.
Four SEM path models of alcohol and cannabis use on six SRL practices and six academic challenges.
Results
Descriptive Analyses
Table 1 presents sociodemographic characteristics for all study variables. Table 2 presents descriptive statistics for the study variables including the mean, standard deviation, and correlation matrix. The skewness for all variables was found to be between −.85 and .30, and kurtosis variables ranged between −.80 and 1.18, indicating a normal distribution of the data.
Sociodemographic Characteristics of the Sample.
Note. Data are presented as mean (SD) for continuous measures and % for categorical measures. The frequency of alcohol and cannabis use among university students was measured using two questions with a 10-point Likert scale ranging from 1 (never) to 9 (4 times or more a day) with higher scores indicating greater frequency of substance use.
Correlation matrix with mean and standard deviation of study variables.
Note. Significant correlations are displayed as *p < .05. **p < .01.
Bivariate Analysis
As shown in Table 2, alcohol use frequency had a statistically significant negative correlation with two practices (i.e., task understanding, goal management), statistically significant positive correlation with one practice (i.e., academic social engagement), and statistically significant positive correlation with one challenge (i.e., initiating-sustaining engagement). In contrast, cannabis use frequency had a statistically significant negative correlation with three of the six practices (i.e., task understanding, goal management, time management), and statistically significant positive correlation with five of the six challenges (i.e., goal and time management, initiating and sustaining engagement, metacognitive, motivation, social and emotional).
Alcohol Use and Academic Outcomes
Alcohol use frequency was negatively associated with the SRL practices of goal and time management (β = −0.06, p < .001) and task understanding (β = −0.07, p < .04) but, positively associated with students’ reports of academic social engagement (β = 0.16, p < .001; Table 3). In addition, alcohol use was positively associated with students reports of social-emotional (β = 0.05, p = .02) and initiating-sustaining engagement (β = 0.12, p < .001) challenges (Table 4).
Alcohol use predicting SRL practices.
Note. Estimates are structural regression path coefficients extracted from SEM models where alcohol use predicted 6 Academic Practices. Fit indices: χ2(375, N = 1170) = 833.390, CFI = 0.927, TLI = 0.914, RMSEA 90% CI = [0.035, 0.040], SRMR = 0.045, estimator = MLM. Students’ year of study, sex at birth, as well as variation across five data collection sites where were considered but are not shown here. IVs = independent variables; DVs = dependent variables.
Alcohol use predicting academic challenges.
Note. Estimates are structural regression path coefficients extracted from SEM models where alcohol use predicted six academic challenge areas. Fit indices: χ2(405, N = 1199) = 1565.994, CFI = 0.926, TLI = 0.915, RMSEA 90% CI = [0.051, 0.056], SRMR = 0.045, estimator = MLM. Students’ year of study, sex at birth, as well as variation across five data collection sites where were considered but are not shown here. IVs = independent variables; DVs = dependent variables.
Cannabis and Academic Outcomes
Results show that cannabis use frequency was negatively associated with lower reports of two academic SRL practices: task understanding (β = −0.08, p = 0.02) and metacognitive monitoring (β = −0.80, p < .001; Table 5). Cannabis use frequency was also positively associated with higher reports of all academic challenges (β = 0.09–0.15, p = .00–.03) except for initiating and sustaining engagement challenges (β = 0.05, p = .12; Table 6). In contrast, results showed that having tried cannabis at least once (binary variable) was positively associated with SRL practices of metacognitive monitoring (β = 0.12, p < .001; Table 5) practices and initiating and sustaining engagement (β = 0.13, p = 0.00) challenges, but negatively associated with students reports of cognitive (β = −0.23, p = 0.01) and metacognitive (β = −0.07, p < .001) challenges (see Table 6).
Cannabis use predicting SRL practices.
Note. Estimates are structural regression path coefficients extracted from SEM models where cannabis use predicted six academic practices. Fit indices 1: χ2(326, N = 1173) = 820.296, CFI = 0.936, TLI = 0.924, RMSEA 90% CI = [0.033, 0.038], SRMR = 0.043, estimator = MLM. Students’ year of study, sex at birth, as well as variation across five data collection sites where were considered but are not shown here. Cannabis use was entered into the model as a binary variable (no use/used at least once) and as a continuous (how often?). Using both predictors in the model allowed for measuring the qualitative distinct effect of being a user/non-user separate from the quantitative effect of the frequency of use. IVs = independent variables; DVs = dependent variables.
Cannabis use predicting academic challenges.
Note. Estimates are structural regression path coefficients extracted from SEM models where cannabis use predicted six academic challenge areas. Fit indices: χ2(372, N = 1202) = 1592.931, CFI = 0.927, TLI = 0.916, RMSEA 90% CI = [0.050, 0.054], SRMR = 0.043, estimator = MLM. Students’ year of study, sex at birth, as well as variation across five data collection sites where were considered but are not shown here. Cannabis use was entered into the model as a binary variable (no use/used at least once) and as a continuous (how often?). Using both predictors in the model allowed for measuring the qualitative distinct effect of being a user/non-user separate from the quantitative effect of the frequency of use. IVs = independent variables; DVs = dependent variables.
Discussion
The early weeks of the academic semester are crucial for university students to develop routines and academic objectives (Hadwin & Winne, 2012); however, limited research has focused on how frequent alcohol and cannabis use can disrupt these learning process. In the current study, we examined associations of alcohol and cannabis use and student reports of SRL practices and academic challenges across five universities. This is the first study to look at these associations in first few weeks of the academic semester using the SRL framework (e.g., Efklides, 2011; Pintrich, 2000; Winne & Hadwin, 1998, 2008).
In terms of alcohol use and SRL practices, results showed negative associations between the SRL practices of task understanding and goal management and positive associations with academic social engagement. In regard to the former, results illustrated that alcohol use in the initial weeks of the academic semester can hinder students understanding of course material (i.e., coursework, assignments, and readings) as well as setting and managing their academic goals. This is concerning, since falling behind in course work has been associated with higher university dropout rates and lower academic performance (Araque et al., 2009). That said, results for the latter demonstrate positive associations between alcohol use and student reports of academic social engagement (e.g., behaviors such as getting to know and helping other students). These associations were expected, as research shows that the transition to university and the formation of peer relationships during the first year often coincides with experimentation of alcohol use among university students (Cho et al., 2015; Lee et al., 2017). These results point to the possibility that alcohol could provide short-term social engagement benefits for first- and second-year students in the first couple of months of the academic semester, but also contribute to longer-term adverse academic consequences with more frequent alcohol use.
Speaking to alcohol use and academic challenges, results showed higher reported levels of social and emotional challenges (e.g., feelings of loneliness, connectedness, and mental health) as well as difficulty initiating and persisting challenges with studying. Findings are consistent with previous studies that show links between higher alcohol consumption, student stress, and poorer mental health and well-being while in university (King et al., 2021; Piumatti, 2018). One possible explanation for these relationships could be due to the increased demands put on students in the first few weeks of the semester. For instance, students who are unable to easily start and persist with studying or those who have lower mental health and well-being could have a higher tendency toward using alcohol as a coping mechanism; however, longitudinal research is needed to understand these bidirectional relationships. Collectively, these results provide preliminary evidence for the nuanced relationships between alcohol use and SRL practices and academic challenges in the first few weeks of the academic semester.
Cannabis use results showed negative associations between students’ reports of two SRL practices (i.e., task understanding and metacognitive monitoring) and five of the six academic challenges (i.e., motivation, goal and time management, cognitive, social and emotional, and metacognitive). Speaking first to SRL practices, results are in line with previous research (Castellanos-Ryan et al., 2022; Scott et al., 2018) and suggest that cannabis use may hinder students’ abilities to comprehend academic tasks, apply study techniques, and effectively monitor, plan, and adapt their learning processes. These associations are particularly concerning, as difficulty implementing and adapting effective study techniques have been previously shown as strong predictors of university dropout (Patrick et al., 2016).
Regarding cannabis use and academic challenges, the present findings suggest that frequent cannabis use may intensify many of the already present academic challenges that students face in the first few months of the academic semester. This is in line with SRL research that shows time management, cognitive strategies, and concentration are the most frequently reported challenges students face in the first few months in university (Koivuniemi et al., 2017). Therefore, as cannabis use could intensify the students’ academic challenges, it is evident that intervention efforts aimed at limiting the frequency of cannabis use in university could have the benefit of also decreasing students’ academic challenges.
Lastly, results relating to no use or cannabis use also showed that students who had used cannabis at least once reported significantly more metacognitive monitoring practices, significantly less cognitive and metacognitive challenges, but significantly more initiating and sustaining engagement challenges. This could be due to individual differences in metacognitive abilities and the number of metacognitive practices that students employ. Results show evidence for enhanced metacognition acting as protective factor for students use or overuse of cannabis. In addition, since metacognition has been previously associated with divergent thinking (Jia et al., 2022), these associations could be explained by studies that show associations between small doses of cannabis and divergent thinking. For example, results of research testing the influence of acute cannabis intoxication on creativity test performance suggest that low doses of Δ9-tetrahydrocannabinol (THC) may enhance divergent thinking relative to high doses or a placebo (Curran et al., 2002, Jones et al., 2009, Kowal et al., 2015, Schafer et al., 2012). Nevertheless, more research needs to be done to understand the nuanced relationships between using cannabis at least once and metacognition in undergraduate populations.
Limitations and Future Directions
While a considerable strength of the study is the large sample size representing students across multiple post-secondary institutions, there are limitations that should be considered. First, as all variables in the study were measured using self-reported data, results may include biased responses and may not fully reflect the true effects of substance use on the academic challenges students face and SRL practices they employ. However, it should be noted, that previous studies have examined the reliability of self-reported substance use patterns on health-related consequences (Lambe et al., 2023; Morris et al., 2022) which provides support for the utility of self-reported substance use patterns (Sobell & Sobell, 2007). Second, the cross-sectional nature of the study limits interpretation about the directionality of the relations of substance use and university student success. Longitudinal analyses would allow for the identification of within- and between-person differences in substance use and academic success outcomes over time. Third, a general caution when interpreting results of this study is that the SRL practices and academic challenges were made up of latent SRL factors. Therefore, we caution readers when conceptualizing these results, to remember that the latent SRL variables of this study reflect a partialed variables. Fourth, most of the effect sizes were relatively small, warranting caution in interpretation and attempts to replicate findings.
It is also important to recognize the limits to the generalizability of the current study. At the time of data collection, Canada was experiencing the second wave of the COVID-19 pandemic during which multiple provinces issued stay-at-home warnings and university classes were held online to try to limit the spread of the virus. Additionally, the study also coincided with the first roll-out of COVID-19 vaccinations. Hence, this period does not represent the typical student's introduction to post-secondary education due to ongoing public health restrictions and additional pandemic-related stressors this cohort of students would have faced. For example, students may have experienced changes in (1) mental health challenges (Stamatis et al., 2022), (2) substance use (Bartel et al., 2023; Kilian et al., 2022), and (3) challenges initiating and sustaining engagement in an online learning environment during COVID (Gilbert et al., 2023). Thus, it is important to replicate these findings during non-pandemic times to ensure they hold outside of the context of those restrictions.
In addition, while this study did not examine the co-use of alcohol and cannabis, we recognize that this is a critical pattern to investigate. Specifically, as previous research has shown co-use to effect institutionalized academic outcomes (Arria et al., 2008), it is possible that co-use could have a unique effect on SRL practices and academic challenges that emerging adults encounter in the first and second year of university. Future research should investigate co-use patterns to gain a more comprehensive understanding of how alcohol and cannabis co-use relates to SRL practices and academic challenges.
Finally, it is possible that the observed positive relationships between cannabis use and SRL could be influenced by underlying traits, or predispositions, that affect both substance use and SRL practices. Previous research has explored relationships between specific personality types at risk for cannabis (e.g., Champion et al., 2024; Edalati et al., 2021) and alcohol (e.g., Lambe et al., 2023, 2024; Yunus et al., 2024) use; however, current research has yet explored associations in relation to SRL practices and academic challenges. Therefore, future studies should explore associations between specific personality types at risk for cannabis and alcohol and emerging adults’ SRL practices and academic challenges.
Conclusion
Substance use in university student populations continues to be a challenging issue that has been shown to impact students’ academic functioning (Macleod et al., 2004). Our novel findings showed that substance use was associated with lower reported levels of SRL practices and greater academic challenges. While cannabis use was associated with a wider range of reported academic challenges compared to alcohol use, both substances negatively predicted a similar number of reported academic practices. The implications of this research are far-reaching as they illustrate a need for interventions to help students establish adaptive SRL practices, particularly among those who are frequently engaging in substance use in the first few weeks of the academic semester.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Canadian Institutes of Health Research, Social Sciences and Humanities Research Council of Canada (grant number CA7-170130, 895-2019-1021).
