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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 (M_age= 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.

Keywords

student success self-regulated learning substance use university students academic achievement

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 (M_age= 19.1 years; SD_age= 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.

Figure 1.
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.

Table 1.
Sociodemographic Characteristics of the Sample.

Variable Total (SD)

N 1247

Age 19.1 (1.44)

Missing 1.50%

Sex

Female 67.06%

Male 32.70%

Missing 0.24%

Year of study

First 56.64%

Second 43.36%

Missing 0.00%

Domestic students 85.47%

Missing 1.58%

Alcohol frequency 2.44 (1.07)

Missing 1.11%

Cannabis frequency 1.87 (1.15)

Missing 0.79%

Cannabis user 49.61%

Missing 2.29%

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.

Table 2.
Correlation matrix with mean and standard deviation of study variables.

Variable M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Alcohol 2.44 1.07 1.000 —

2. Cannabis 1.87 1.15 .50 1.000 —

SRL-Practices

3. Task understanding 3.75 0.64 −.08 −.07* 1.000 —

4. Goal management 3.49 0.85 −.07* −.06* .55 1.000 —

5. Time management 3.59 0.82 −.03 −.08 .41 .60 1.000 —

6. Adapting 3.34 0.74 −.02 .01 .41 .42 .35 1.000 —

7. Metacognitive monitoring 3.77 0.78 −.05 −.05 .46 .37 .28 .42 1.000 —

8. Academic social engagement 3.19 1.00 .19 .01 .22 .28 .21 .22 .15 1.000

Academic challenges —

9. Motivation challenges 3.11 0.99 .02 .11 −.16 −.20 −.17 .05 −.05 −.10 1.000 —

10. Initiating and sustaining engagement challenges 3.49 0.93 .12 .14 −.21 −.34 −.33 −.11 −.09 −.11 .52 1.000 —

11. Goal and time management challenges 3.13 1.01 .00 .09 −.16 −.22 −.27 −.02 −.05 −.10 .65 .64 1.000 —

12. Cognitive challenges 2.96 0.92 −.02 .06* −.18 −.16 −.12 .04 −.04 −.13 .63 .46 .61 1.000

13. Social and emotional challenges 3.24 0.92 .05 .12 −.11 −.19 −.17 .01 −.01 −.26 .56 .53 .53 .53 1.000

14. Metacognitive challenges 3.00 0.96 −.01 .09 −.16 −.23 −.22 .03 −.01 −.11 .66 .57 .74 .73 .60 1.000

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).

Table 3.
Alcohol use predicting SRL practices.

IVs = alcoholDVs = SRL practices Estimate SE z p(>|z|) Std.all R ²

Task understanding −0.07 0.04 −2.02 .04 −0.08 0.009

Goal management −0.06 0.01 −4.59 .00 −0.07 0.006

Time management −0.04 0.04 −0.95 .34 −0.04 0.004

Adapting −0.01 0.02 −0.79 .43 −0.02 0.005

Metacognitive monitoring −0.05 0.03 −1.34 .18 −0.05 0.004

Academic social engagement 0.16 0.03 5.25 .00 0.15 0.027

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.

Table 4.
Alcohol use predicting academic challenges.

IVs = alcohol useDVs = academic challenges Estimate SE z p(>|z|) Std.all R ²

Motivation challenges 0.03 0.04 0.74 .46 0.03 0.034

Initiating and sustaining engagement challenges 0.12 0.03 3.64 .00 0.12 0.020

Goal and time management challenges 0.03 0.02 1.35 .18 0.03 0.007

Cognitive challenges −0.02 0.05 −0.51 .61 −0.03 0.016

Social and emotional challenges 0.05 0.02 2.27 .02 0.05 0.017

Metacognitive challenges −0.01 0.02 −0.43 .67 −0.01 0.013

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).

Table 5.
Cannabis use predicting SRL practices.

IVs = cannabisDVs = SRL practices Estimate SE z p(>|z|) Std. all R ²

Task understanding 0.010

Cannabis use frequency −0.08 0.03 −2.35 .02 −0.09

Cannabis user/non-user 0.05 0.13 0.39 .70 0.025

Goal management 0.007

Cannabis use frequency −0.03 0.69 −0.46 .65 −0.04

Cannabis user/non-user −0.09 0.15 −0.56 .58 −0.04

Time management 0.014

Cannabis use frequency −0.07 0.07 −0.91 .36 −0.08

Cannabis user/non-user −0.07 0.21 −0.34 .73 −0.04

Adapting 0.005

Cannabis use frequency −0.00 0.03 −0.16 .87 −0.01

Cannabis user/non-user 0.02 0.11 0.21 .83 0.01

Metacognitive monitoring 0.006

Cannabis use frequency −0.80 0.01 −11.44 .00 −0.10

Cannabis user/non-user 0.12 0.04 3.11 .00 0.06

Academic social engagement 0.003

Cannabis use frequency −0.05 0.05 −0.97 .33 −0.06

Cannabis user/non-user 0.10 0.08 1.23 .22 0.05

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.

Table 6.
Cannabis use predicting academic challenges.

IVs = cannabis useDVs = academic challenges Estimate SE z p(>|z|) Std.all R ²

Motivation challenges 0.047

Cannabis use frequency 0.15 0.03 5.90 .00 0.17

Cannabis user/non-user −0.16 0.10 −1.56 .12 −0.08

Initiating and sustaining engagement challenges 0.024

Cannabis use frequency 0.08 0.03 2.52 .12 0.09

Cannabis user/non-user 0.13 0.04 3.12 .00 0.06

Goal and time management challenges 0.015

Cannabis use frequency 0.09 0.03 3.64 .00 0.10

Cannabis user/non-user −0.02 0.03 −0.58 .56 −0.01

Cognitive challenges 0.023

Cannabis use frequency 0.12 0.01 8.35 .00 0.12

Cannabis user/non-user −0.23 0.09 −2.66 .01 −0.11

Social and emotional challenges 0.027

Cannabis use frequency 0.09 0.04 2.20 .03 0.11

Cannabis user/non-user 0.02 0.10 0.25 .81 0.01

Metacognitive challenges 0.020

Cannabis use frequency 0.10 0.02 6.00 .00 0.11

Cannabis user/non-user −0.07 0.02 −3.06 .00 −0.03

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.

Variable	Total (SD)
N	1247
Age	19.1 (1.44)
Missing	1.50%
Sex
Female	67.06%
Male	32.70%
Missing	0.24%
Year of study
First	56.64%
Second	43.36%
Missing	0.00%
Domestic students	85.47%
Missing	1.58%
Alcohol frequency	2.44 (1.07)
Missing	1.11%
Cannabis frequency	1.87 (1.15)
Missing	0.79%
Cannabis user	49.61%
Missing	2.29%

Variable	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1. Alcohol	2.44	1.07	1.000	—
2. Cannabis	1.87	1.15	.50**	1.000	—
SRL-Practices
3. Task understanding	3.75	0.64	−.08**	−.07*	1.000	—
4. Goal management	3.49	0.85	−.07*	−.06*	.55**	1.000	—
5. Time management	3.59	0.82	−.03	−.08**	.41**	.60**	1.000	—
6. Adapting	3.34	0.74	−.02	.01	.41**	.42**	.35**	1.000	—
7. Metacognitive monitoring	3.77	0.78	−.05	−.05	.46**	.37**	.28**	.42**	1.000	—
8. Academic social engagement	3.19	1.00	.19**	.01	.22**	.28**	.21**	.22**	.15**	1.000
Academic challenges											—
9. Motivation challenges	3.11	0.99	.02	.11**	−.16**	−.20**	−.17**	.05	−.05	−.10**	1.000	—
10. Initiating and sustaining engagement challenges	3.49	0.93	.12**	.14**	−.21**	−.34**	−.33**	−.11**	−.09**	−.11**	.52**	1.000	—
11. Goal and time management challenges	3.13	1.01	.00	.09**	−.16**	−.22**	−.27**	−.02	−.05	−.10**	.65**	.64**	1.000	—
12. Cognitive challenges	2.96	0.92	−.02	.06*	−.18**	−.16**	−.12**	.04	−.04	−.13**	.63**	.46**	.61**	1.000
13. Social and emotional challenges	3.24	0.92	.05	.12**	−.11**	−.19**	−.17**	.01	−.01	−.26**	.56**	.53**	.53**	.53**	1.000
14. Metacognitive challenges	3.00	0.96	−.01	.09**	−.16**	−.23**	−.22**	.03	−.01	−.11**	.66**	.57**	.74**	.73**	.60**	1.000

IVs = alcoholDVs = SRL practices	Estimate	SE	z	p(>\|z\|)	Std.all	R ²
Task understanding	−0.07	0.04	−2.02	.04	−0.08	0.009
Goal management	−0.06	0.01	−4.59	.00	−0.07	0.006
Time management	−0.04	0.04	−0.95	.34	−0.04	0.004
Adapting	−0.01	0.02	−0.79	.43	−0.02	0.005
Metacognitive monitoring	−0.05	0.03	−1.34	.18	−0.05	0.004
Academic social engagement	0.16	0.03	5.25	.00	0.15	0.027

IVs = alcohol useDVs = academic challenges	Estimate	SE	z	p(>\|z\|)	Std.all	R ²
Motivation challenges	0.03	0.04	0.74	.46	0.03	0.034
Initiating and sustaining engagement challenges	0.12	0.03	3.64	.00	0.12	0.020
Goal and time management challenges	0.03	0.02	1.35	.18	0.03	0.007
Cognitive challenges	−0.02	0.05	−0.51	.61	−0.03	0.016
Social and emotional challenges	0.05	0.02	2.27	.02	0.05	0.017
Metacognitive challenges	−0.01	0.02	−0.43	.67	−0.01	0.013

IVs = cannabisDVs = SRL practices	Estimate	SE	z	p(>\|z\|)	Std. all	R ²
Task understanding						0.010
Cannabis use frequency	−0.08	0.03	−2.35	.02	−0.09
Cannabis user/non-user	0.05	0.13	0.39	.70	0.025
Goal management						0.007
Cannabis use frequency	−0.03	0.69	−0.46	.65	−0.04
Cannabis user/non-user	−0.09	0.15	−0.56	.58	−0.04
Time management						0.014
Cannabis use frequency	−0.07	0.07	−0.91	.36	−0.08
Cannabis user/non-user	−0.07	0.21	−0.34	.73	−0.04
Adapting						0.005
Cannabis use frequency	−0.00	0.03	−0.16	.87	−0.01
Cannabis user/non-user	0.02	0.11	0.21	.83	0.01
Metacognitive monitoring						0.006
Cannabis use frequency	−0.80	0.01	−11.44	.00	−0.10
Cannabis user/non-user	0.12	0.04	3.11	.00	0.06
Academic social engagement						0.003
Cannabis use frequency	−0.05	0.05	−0.97	.33	−0.06
Cannabis user/non-user	0.10	0.08	1.23	.22	0.05

IVs = cannabis useDVs = academic challenges	Estimate	SE	z	p(>\|z\|)	Std.all	R ²
Motivation challenges						0.047
Cannabis use frequency	0.15	0.03	5.90	.00	0.17
Cannabis user/non-user	−0.16	0.10	−1.56	.12	−0.08
Initiating and sustaining engagement challenges						0.024
Cannabis use frequency	0.08	0.03	2.52	.12	0.09
Cannabis user/non-user	0.13	0.04	3.12	.00	0.06
Goal and time management challenges						0.015
Cannabis use frequency	0.09	0.03	3.64	.00	0.10
Cannabis user/non-user	−0.02	0.03	−0.58	.56	−0.01
Cognitive challenges						0.023
Cannabis use frequency	0.12	0.01	8.35	.00	0.12
Cannabis user/non-user	−0.23	0.09	−2.66	.01	−0.11
Social and emotional challenges						0.027
Cannabis use frequency	0.09	0.04	2.20	.03	0.11
Cannabis user/non-user	0.02	0.10	0.25	.81	0.01
Metacognitive challenges						0.020
Cannabis use frequency	0.10	0.02	6.00	.00	0.11
Cannabis user/non-user	−0.07	0.02	−3.06	.00	−0.03

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).

ORCID iDs

Violet V. Cieslik

Paweena Sukhawathanakul

Author Biographies

Violet V. Cieslik is a PhD student in the Department of Psychology at the University of Victoria. Her research examines the development of creativity and executive functioning in preschoolers and the effects of external influences (e.g., screen-time, media, schooling type, and recreational involvement) on preschool development. She earned her MSc in Psychology from the University of Victoria.

Paweena Sukhawathanakul is an Assistant Professor in the Department of Psychology at the University of Victoria. Her research is centered on identifying factors that enhance or undermine the social and emotional development of children and adolescents. She earned her PhD from the University of Victoria.

Ramin Rostampour earned his PhD from the University of Victoria. His research broadly lies in researching the differential psychology of Self-Regulated Learning (SRL) from a unified, person-centred, and process-oriented perspective.

Meng Qi Wu is a PhD student in the Department of Educational Psychology and Leadership studies. Her research largely focuses on the relationship between culture, self-regulated learning (SRL), and metacognitive awareness in adolescents and emerging adults. She earned her MA from the University of Victoria

Allyson F. Hadwin is a Professor of Psychology at the University of Victoria. Her research examines psychological factors and instructional practices that contribute to student success and well-being through the lens of self-regulated learning (SRL). She aims to uncover the complex interplay between, cognitive, motivational, and socio-emotional factors that influence how learners individually and collaboratively progress toward their academic goals in traditional and digital learning environments. She earned her PhD from the Simon Fraser University.

Sherry H. Stewart is a Tier 1 Canada Research Chair in Addictions and Mental Health and a Professor in the Departments of Psychiatry, Psychology and Neuroscience, and Community Health and Epidemiology at Dalhousie University, as well as a licensed clinical psychologist in the province of Nova Scotia. She is a recently inducted Fellow in the Royal Society of Canada. She is well known for her research on psychological factors contributing to alcohol abuse, pathological gambling, and the comorbidity of emotional and addictive disorders. She earned her PhD from McGill University.

Kara Thompson is an Associate Professor in the Department of Psychology at St. Francis Xavier University. She is a prolific scholar, with close to 70 peer-reviewed publications and is widely recognized for her research contributions on alcohol policy, and substance use in young adulthood. She is a scientist on the Canadian Alcohol Policy Evaluation project and was a scientific expert for the development of Canada's new guidance on alcohol and health. She earned her PhD from the University of Victoria

Matthew T. Keough is an Assistant Professor in the Department of Psychology at York University. His research focuses on improving our understanding of the etiology and treatment of addictive behaviour, including both substance use and behavioural addictions (e.g., problem gambling). He earned his PhD from Concordia University.

Marvin Krank is a Professor Emeritus in the Department of Psychology at the University of British Columbia - Okanogan Campus. His research focuses on the development of substance abuse in adolescence; vulnerabilities related to social learning; cognitive models that differentiate implicit (unconscious) from explicit memories; relative role of automatic associations versus executive control; social experiences, dispositions, memories, and thoughts that predict transitions to substance use and to substance abuse. He earned his PhD from McMaster University.

Patricia J. Conrod is a Professor of Psychiatry at the Université de Montréal. Her research focuses on cognitive, personality, and biological risk factors for the development and maintenance of drug abuse, as well as the factors that mediate the co-occurrence of addictive behaviors with other mental disorders. Her experimental research investigates the factors that increase susceptibility to behavioral reinforcement from drugs of abuse. She earned her PhD in Clinical Psychology from McGill University.

Natalie Castellanos-Ryan is an Assistant Professor at the School of Psychoeducation, Université de Montréal. Her research focuses on how deficits in self-regulation (impulsivity), pubertal and cognitive development, in interaction with environmental factors, predict substance use problems and psychopathological co-morbidity in youth. She completed her PhD at the Institute of Psychiatry, King's College London.

Phil Winne is a Professor Emeritus in the Faculty of Education at Simon Fraser University. His research focuses on self-regulated learning, metacognition, study tactics and learning strategies, motivation, learning analytics, technologies for researching and promoting learning, research methods in the learning sciences. He earned his PhD at Stanford University.

References

Araque

Roldán

Salguero

(2009). Factors influencing university drop out rates. Computers and Education, 53(3), 563–574. https://doi.org/10.1016/j.compedu.2009.03.013

Arria

A. M.

Caldeira

K. M.

Bugbee

B. A.

Vincent

K. B.

O'Grady

K. E.

(2015). The academic consequences of marijuana use during college. Psychology of Addictive Behaviors, 29(3), 564–575. https://doi.org/10.1037/adb0000108

Arria

A. M.

Caldeira

K. M.

O’Grady

K. E.

Vincent

K. B.

Fitzelle

D. B.

Johnson

E. P.

Wish

E. D.

(2008). Drug exposure opportunities and use patterns among college students: Results of a longitudinal prospective cohort study. Substance Abuse, 29(4), 19–38. https://doi.org/10.1080/08897070802418451

Arria

A. M.

Caldeira

K. M.

Vincent

K. B.

Winick

E. R.

Baron

R. A.

O'Grady

K. E.

(2013). Discontinuous college enrollment: Associations with substance use and mental health. Psychiatric Services, 64(2), 165–172. https://doi.org/10.1176/appi.ps.201200106

Arria

A. M.

Garnier-Dykstra

L. M.

Caldeira

K. M.

Vincent

K. B.

Winick

E. R.

O’Grady

K. E.

(2013a). Drug use patterns and continuous enrollment in college: Results from a longitudinal study. Journal of Studies on Alcohol and Drugs, 74(1), 71–83. https://doi.org/10.15288/jsad.2013.74.71

Bartel

S. J.

Sherry

S. B.

Stewart

S. H.

(2023). Pandemic-related changes in alcohol and cannabis use: Comparing retrospective reports and prospective data. International Journal of Mental Health and Addiction, 21(4), 2077–2083. https://doi.org/10.1007/s11469-021-00708-7

Bolin

R. M.

Pate

McClintock

(2017). The impact of alcohol and marijuana use on academic achievement among college students. The Social Science Journal, 54(4), 430–437. https://doi.org/10.1016/j.soscij.2017.08.003

Buckner

J. D.

Ecker

A. H.

Cohen

A. S.

(2010). Mental health problems and interest in marijuana treatment among marijuana-using college students. Addictive Behaviors, 35(9), 826–833. https://doi.org/10.1016/j.addbeh.2010.04.001

Caldeira

K. M.

Arria

A. M.

O'Grady

K. E.

Vincent

K. B.

Wish

E. D.

(2008). The occurrence of cannabis use disorders and other cannabis-related problems among first-year college students. Addictive Behaviors, 33(3), 564–575. https://doi.org/10.1016/j.addbeh.2007.10.001

10.

Castellanos-Ryan

Morin

Rioux

London-Nadeau

Leblond

(2022). Academic, socioeconomic and interpersonal consequences of cannabis use: A narrative review. Drugs: Education, Prevention and Policy, 29(3), 199–217. https://doi.org/10.1080/09687637.2021.1906846

11.

Champion

K. E.

Debenham

Teesson

Stapinski

L. A.

Devine

Barrett

E. L.

Slade

Kelly

E. V.

Chapman

Smout

Lawler

Castellanos-Ryan

Conrod

P. J.

Newton

N. C.

(2024). Effect of a selective personality-targeted prevention program on 7-year illicit substance related outcomes: A secondary analysis of a cluster randomized controlled trial. Drug and Alcohol Dependence, 258, 111266–111266. https://doi.org/10.1016/j.drugalcdep.2024.111266

12.

Cho

S. B.

Llaneza

D. C.

Adkins

A. E.

Cooke

Kendler

K. S.

Clark

S. L.

Dick

D. M.

(2015). Patterns of substance use across the first year of college and associated risk factors. Frontiers in Psychiatry, 6, 152. https://doi.org/10.3389/fpsyt.2015.00152

13.

Conrod

P. J.

Castellanos

Strang

(2010). Brief, personality-targeted coping skills interventions and survival as a non–drug user over a 2-year period during adolescence. Archives of General Psychiatry, 67(1), 85–93. https://doi.org/10.1001/archgenpsychiatry.2009.173

14.

Conway

J. M.

DiPlacido

(2015). The indirect effect of alcohol use on GPA in first-semester college students: The mediating role of academic effort. Journal of College Student Retention: Research, Theory & Practice, 17(3), 303–318. https://doi.org/10.1177/1521025115575705

15.

Curran

H. V.

Brignell

Fletcher

Middleton

Henry

(2002). Cognitive and subjective dose-response effects of acute oral Δ⁹-tetrahydrocannabinol (THC) in infrequent cannabis users. Psychopharmacologia, 164(1), 61–70. https://doi.org/10.1007/s00213-002-1169-0

16.

Dahl

R. E.

(2004). Adolescent brain development: A period of vulnerabilities and opportunities. Keynote address. Annals of the New York Academy of Sciences, 1021(1), 1–22. https://doi.org/10.1196/annals.1308.001

17.

Dougherty

D. M.

Mathias

C. W.

Dawes

M. A.

Furr

R. M.

Charles

N. E.

Liguori

Shannon

E. E.

Acheson

(2013). Impulsivity, attention, memory, and decision-making among adolescent marijuana users. Psychopharmacology, 226(2), 307–319. https://doi.org/10.1007/s00213-012-2908-5

18.

Edalati

Collin-Vézina

Conrod

(2021). 1.6 Personality profiles associated with the risk of substance use and psychiatric symptoms in adolescents living in vulnerable contexts. Journal of the American Academy of Child and Adolescent Psychiatry, 60(10), S141–S141. https://doi.org/10.1016/j.jaac.2021.09.019

19.

Efklides

(2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6–25. https://doi.org/10.1080/00461520.2011.538645

20.

Gilbert

Bureau

J. S.

Diallo

Morin

A. J. S.

Guay

(2023). Undergraduate students’ motivational profiles before and during the COVID-19 pandemic: The role of educational climate and trait self-control. British Journal of Educational Psychology, 93(4), 1188–1206. https://doi.org/10.1111/bjep.12626

21.

Hadwin

A. F.

Rostampour

Bahena-Olivares

L. M.

(2021). Self-regulated learning profile and self–diagnostic tool (SRL-PSD-2021) [Poster presentation]. American Educational Research Association.

22.

Hadwin

A. F.

Sukhawathanakul

Rostampour

Bahena Olivares

(2022). Do self-regulated learning practices and intervention mitigate the impact of academic challenges and COVID distress on academic performance during online learning? Frontiers in Psychology, 13, 813529. https://doi.org/10.3389/fpsyg.2022.813529

23.

Hadwin Winne

P. H.

(2012). Promoting learning skills in undergraduate students. In Enhancing the quality of learning (pp. 201–227). Cambridge University Press. https://doi.org/10.1017/CBO9781139048224.013.

24.

Hayes

A. F.

Coutts

J. J.

(2020). Use omega rather than Cronbach's alpha for estimating reliability. Communication Methods and Measures, 14(1), 1–24. https://doi.org/10.1080/19312458.2020.1718629

25.

Health Canada. (2019). Canadian postsecondary education alcohol and drug use survey (CPADS). Government of Canada. https://health-infobase.canada.ca/src/data/alcohol/cpads/downloads/infographic_download.pdf

26.

Horwood

L. J.

Fergusson

D. M.

Hayatbakhsh

M. R.

Najman

J. M.

Coffey

Patton

G. C.

Silins

Hutchinson

D. M.

(2010). Cannabis use and educational achievement: Findings from three Australasian cohort studies. Drug and Alcohol Dependence, 110(3), 247–253. https://doi.org/10.1016/j.drugalcdep.2010.03.008

27.

L. T.

Bentler

P. M.

(1999). Cut off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

28.

Jia

Zhang

(2022). The role of metacognitive strategy monitoring and control in the relationship between creative mindsets and divergent thinking performance. Journal of Intelligence, 10(2), 35. https://doi.org/10.3390/jintelligence10020035

29.

Jones

K. A.

Blagrove

Parrott

A. C.

(2009). Cannabis and ecstasy/MDMA: Empirical measures of creativity in recreational users. Journal of Psychoactive Drugs, 41(4), 323–329. https://doi.org/10.1080/02791072.2009.10399769

30.

Kelly

E. V.

Newton

N. C.

Stapinski

L. A.

Conrod

P. J.

Barrett

E. L.

Champion

K. E.

Teesson

(2020). A novel approach to tackling bullying in schools: Personality-targeted intervention for adolescent victims and bullies in Australia. Journal of the American Academy of Child & Adolescent Psychiatry, 59(4), 508–518.e2. https://doi.org/10.1016/j.jaac.2019.04.010

31.

Kilian

O'Donnell

Potapova

López-Pelayo

Schulte

Miquel

Paniello Castillo

Schmidt

C. S.

Gual

Rehm

Manthey

(2022). Changes in alcohol use during the COVID-19 pandemic in Europe: A meta-analysis of observational studies. Drug and Alcohol Review, 41(4), 918–931. https://doi.org/10.1111/dar.13446

32.

King

Pickett

McNevin

S. H.

Bowie

C. R.

Rivera

Keown-Stoneman

Harkness

Cunningham

Milanovic

Saunders

K. E. A.

Goodday

Duffy

(2021). Mental health need of students at entry to university: Baseline findings from the U-flourish student well-being and academic success study. Early Intervention in Psychiatry, 15(2), 286–295. https://doi.org/10.1111/eip.12939

33.

Koivuniemi

Panadero

Malmberg

Järvelä

(2017). Desafíos de aprendizaje y habilidades de regulación en distintas situaciones de aprendizaje en estudiantes de educación superior [Higher education students’ learning challenges and regulatory skills in different learning situations]. Infancia y Aprendizaje, 40(1), 19–55. https://doi.org/10.1080/02103702.2016.1272874

34.

Kowal

M. A.

Hazekamp

Colzato

L. S.

van Steenbergen

van der Wee

N. J. A.

Durieux

Manai

Hommel

(2015). Cannabis and creativity: Highly potent cannabis impairs divergent thinking in regular cannabis users. Psychopharmacology, 232(6), 1123–1134. https://doi.org/10.1007/s00213-014-3749-1

35.

La Greca

A. M.

Harrison

H. M.

(2005). Adolescent peer relations, friendships, and romantic relationships: Do they predict social anxiety and depression? Journal of Clinical Child and Adolescent Psychology, 34(1), 49–61. https://doi.org/10.1207/s15374424jccp3401_5

36.

Lambe

L. J.

Hohn

L. A.

Sukhawathanakul

Conrod

P. J.

Hadwin

Keough

M. T.

Krank

Thompson

Stewart

S. H.

(2024). Personality traits and links between bullying, emotional distress, and substance use among emerging adults. International Journal of Mental Health and Addiction, 1–17. https://doi.org/10.1007/s11469-024-01401-1 .

37.

Lambe

L. J.

Yunus

F. M.

Moore

Keough

M. T.

Thompson

Krank

Conrod

Stewart

S. H.

(2023). Inhibited personality traits, internalizing symptoms, and drinking to cope during the COVID-19 pandemic among emerging adults. Cognitive Behaviour Therapy, 52(3), 198–212. https://doi.org/10.1080/16506073.2022.2152726

38.

Landry

Tremblay

Guyon

Bergeron

Brunelle

(2004). Screening chart for problem consumption of alcohol and drugs among adolescents [DEP-ADO]: Development and psychometric qualities. Drogues, Santé et Société, 3(1), 35–67. https://doi.org/10.7202/009489ar

39.

Lee

C. A.

Derefinko

K. J.

Davis

H. A.

Milich

Lynam

D. R.

(2017). Cross-lagged relations between motives and substance use: Can use strengthen your motivation over time? Drug and Alcohol Dependence, 178, 544–550. https://doi.org/10.1016/j.drugalcdep.2017.05.027

40.

Macleod

Oakes

Copello

Crome

Egger

Hickman

Oppenkowski

Stokes-Lampard

Smith

G. D.

(2004). Psychological and social sequelae of cannabis and other illicit drug use by young people: A systematic review of longitudinal, general population studies. The Lancet, 363(9421), 1579–1588. https://doi.org/https://doi-org.ezproxy.library.uvic.ca/10.1016/S0140-6736(04)16200-4

41.

Martinez

J. A.

Roth

M. G.

Johnson

D. N.

Jones

J. A.

(2015). How robustly does cannabis use associate to college grades? Findings from two cohorts. Journal of Drug Education, 45(1), 56–67. https://doi.org/10.1177/0047237915596606

42.

Meda

S. A.

Gueorguieva

R. V.

Pittman

Rosen

R. R.

Aslanzadeh

Tennen

Leen

Hawkins

Raskin

Wood

R. M.

Austad

C. S.

Dager

Fallahi

Pearlson

G. D.

(2017). Longitudinal influence of alcohol and marijuana use on academic performance in college students. PLoS ONE, 12(3), e0172213. https://doi.org/10.1371/journal.pone.0172213

43.

Morris

Baptist-Mohseni

Kronstein

N. B.

Murphy

C. B.

Yunus

Thibault

Livet

Mahmoud

Pétrin-Pomerleau

Krank

Thompson

Conrod

Stewart

S. H.

Keough

M. T.

(2022). Hazardous drinking mediates the relation between externalizing personality and reduced adherence to COVID-19 public health guidelines in university students. Emerging Adulthood, 11(3), 797–803. https://doi.org/10.1177/21676968221140449

44.

O’Leary-Barrett

Masse

Pihl

R. O.

Stewart

S. H.

Séguin

Conrod

P. J.

(2017). A cluster-randomised controlled trial evaluating the effects of delaying onset of adolescent substance abuse on cognitive development and addiction: The Co-Venture trial. Addiction, 112, 1871–1881. https://doi.org/10.1111/add.13876

45.

Páramo

M. F.

Cadaveira

Tinajero

Rodríguez

M. S.

(2020). Binge drinking, cannabis co-consumption and academic achievement in first year university students in Spain: Academic adjustment as a mediator. International Journal of Environmental Research and Public Health, 17(2), 542. https://doi.org/10.3390/ijerph17020542

46.

Patrick

M. E.

Schulenberg

J. E.

O’Malley

P. M.

(2016). High school substance use as a predictor of college attendance, completion, and dropout: A national multicohort longitudinal study. Youth and Society, 48(3), 425–447. https://doi.org/10.1177/0044118X13508961

47.

Pintrich

P. R.

(2000). The role of goal orientation in self-regulated learning. In Boekaerts

Zeidner

Pintrich

P. R.

(Eds.), Handbook of self-regulation (pp. 451–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3

48.

Piumatti

(2018). Motivation, health-related lifestyles, and depression among university students: A longitudinal analysis. Psychiatry Research, 260, 412–417. https://doi.org/10.1016/j.psychres.2017.12.009

49.

Romero

Master

Paunesku

Dweck

C. S.

Gross

J. J.

(2014). Academic and emotional functioning in middle school: The role of implicit theories. Emotion, 14(2), 227–234. https://psycnet.apa.org/doi/10.1037/a0035490

50.

Rosseel

(2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://www.jstatsoft.org/article/view/v048i02 https://doi.org/10.18637/jss.v048.i02

51.

Schafer

Feilding

Morgan

C. J. A.

Agathangelou

Freeman

T. P.

Valerie Curran

(2012). Investigating the interaction between schizotypy, divergent thinking and cannabis use. Consciousness and Cognition, 21(1), 292–298. https://doi.org/10.1016/j.concog.2011.11.009

52.

Scott

J. C.

Slomiak

S. T.

Jones

J. D.

Rosen

A. F. G.

Moore

T. M.

Gur

R. C.

(2018). Association of cannabis with cognitive functioning in adolescents and young adults: A systematic review and meta-analysis. JAMA Psychiatry, 75(6), 585–595. https://doi.org/10.1001/jamapsychiatry.2018.0335

53.

Sobell

M. B.

Sobell

L. C.

(2007). Substance use, health, and mental health. Clinical Psychology: Science and Practice, 14(1), 1–5. https://doi-org.ezproxy.library.uvic.ca/10.1111/j.1468-2850.2007.00056.x

54.

Stamatis

C. A.

Broos

H. C.

Hudiburgh

S. E.

Dale

S. K.

Timpano

K. R.

(2022). A longitudinal investigation of COVID-19 pandemic experiences and mental health among university students. British Journal of Clinical Psychology, 61(2), 385–404. https://doi.org/10.1111/bjc.12351

55.

Suerken

C. K.

Reboussin

B. A.

Egan

K. L.

Sutfin

E. L.

Wagoner

K. G.

Spangler

Wolfson

(2016). Marijuana use trajectories and academic outcomes among college students. Drug and Alcohol Dependence, 162, 137–145. https://doi.org/10.1016/j.drugalcdep.2016.02.041

56.

Sussman

Arnett

J. J.

(2014). Emerging adulthood: Developmental period facilitative of the addictions. Evaluation & the Health Professions, 37(2), 147–155. https://doi.org/10.1177/0163278714521812

57.

Thompson

Leadbeater

Ames

Merrin

G. J.

(2019). Associations between marijuana use trajectories and educational and occupational success in young adulthood. Prevention Science, 20(2), 257–269. https://doi.org/10.1007/s11121-018-0904-7

58.

Tucker

J. S.

Ellickson

P. L.

Orlando

Martino

S. C.

Klein

D. J.

(2005). Substance use trajectories from early adolescence to emerging adulthood: A comparison of smoking, binge drinking, and marijuana use. Journal of Drug Issues, 35(2), 307–332. https://doi.org/10.1177/002204260503500205

59.

Winne

P. H.

Hadwin

A. F.

(1998). Studying as self-regulated learning. In Hacker

Dunlosky

Graesser

(Eds.), Metacognition in educational theory and practice (pp. 277–304). Lawrence Erlbaum.

60.

Winne

P. H.

Hadwin

A. F.

(2008). The weave of motivation and self-regulated learning. In Schunk

D. H.

Zimmerman

B. J.

(Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297–314). Lawrence Erlbaum Associates.

61.

M. Q.

Cieslik

V. V.

Askari

Hadwin

A. F.

Hood

(2024). Measuring the complexity of self-regulated learning and academic challenges for adolescents in Canada. Journal of Psychoeducational Assessment, 42(3), 293–307. https://doi.org/10.1177/07342829231221851

62.

Yunus

F. M.

Standage

Walsh

Lockhart

Thompson

Keough

Krank

Hadwin

Conrod

P. J.

Stewart

S. H.

(2024). High peak drinking levels mediate the relation between impulsive personality and injury risk in emerging adults. Injury Epidemiology, 11(1), 5–5. https://doi.org/10.1186/s40621-024-00487-4

63.

Zhang

Guan

Ahmed

M. Z.

Jobe

M. C.

Ahmed

(2022). The association between university students’ achievement goal orientation and academic engagement: Examining the mediating role of perceived school climate and academic self-efficacy. Sustainability (Basel, Switzerland), 14(10), 6304. https://doi.org/10.3390/su14106304

64.

Zimmermann

Iwanski

(2014). Emotion regulation from early adolescence to emerging adulthood and middle adulthood: Age differences, gender differences, and emotion-specific developmental variations. International Journal of Behavioral Development, 38(2), 182–194. https://doi.org/10.1177/0165025413515405

Exploring Associations Between Substance Use,Self-Regulated Learning Practices,and Academic Challenges at Five Canadian Universities

Abstract

Keywords

Materials and Methods

Design

Participants

Procedure

Measures

Alcohol and Cannabis Use

SRL Practices

Academic Challenges

Analytic Approach

Results

Descriptive Analyses

Bivariate Analysis

Alcohol Use and Academic Outcomes

Cannabis and Academic Outcomes

Discussion

Limitations and Future Directions

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iDs

Author Biographies

References