The Relationships Between Students’ Expectations,Approaches to Learning,Academic Performance,and Wellbeing in an Online Undergraduate Psychology Program

Abstract

Background

Online undergraduate programs are increasingly popular, yet students often enter with inaccurate expectations about academic requirements.

Objective

This study aimed to determine whether mismatches between students’ initial and current expectations are associated with their approaches to learning, academic achievement, and wellbeing.

Method

We measured 113 online undergraduate students’ expectations of their undergraduate studies, learning approaches, university-related stress, anxiety, burnout, and academic achievement in an online survey.

Results

When current expectations fell short of initial expectations (a negative mismatch in expectations), students reported greater study-related stress, anxiety, cynicism, and adopted surface learning approaches. Students reported greater academic efficacy, higher GPA, more study time, and more deep and strategic learning approaches when their expectations were exceeded (a positive mismatch). Further, students’ learning approaches mediated the relationship between mismatched expectations and wellbeing.

Conclusion

This study confirmed that when online undergraduate students experience a mismatch between their expectations of higher education and their actual experiences, this mismatch is associated with their approach to learning, wellbeing, and academic performance.

Teaching Implications

Higher education institutions should set realistic expectations for new online students and encourage the adoption of a strategic approach to learning, thereby enhancing student wellbeing and academic success.

Keywords

approaches to learning expectations wellbeing burnout GPA

The rapid rise of online learning has transformed higher education over the past two decades. Since 2000, global online enrollments have surged by 900% (Oxford Learning College, 2024). In the United States, the share of undergraduates taking at least one online course tripled from 15.6% to 43.1% between 2004 and 2016 (Snyder et al., 2018). By 2019, 79% of colleges offered online programs, driving further growth (Shankar et al., 2023). This trend is projected to create a $336.98 billion industry by 2026 (Syngene Research, 2019). Beyond flexibility and accessibility, online learning reshapes how students engage with learning. While some thrive, others struggle with the increased autonomy, reduced instructor guidance, and greater need for self-regulation that online education often requires (Mingoia et al., 2025; Yan & Pourdavood, 2024). These differences highlight the need to understand how students adapt, especially when their academic experience differs from their expectations.

Expectation Mismatches in Online Education

Research has extensively documented expectation mismatches—instances where students’ anticipated learning experiences differ from what they actually encounter—in higher education (for a review, see Tomlinson et al., 2023) and have become increasingly salient in online learning, particularly after COVID-19 (Garip et al., 2020; Lobos et al., 2022; Reid et al., 2024; Yalçın & Dennen, 2023). Many students begin online programs expecting a combination of flexibility, accessible learning materials, regular feedback, and structured support. In reality, however, online students face greater autonomy, fewer structured activities, delayed instructor responses, and limited interaction with both faculty and peers, conditions that can lead to frustration and disengagement (Ang et al., 2019; Maloshonok & Terentev, 2017). A common challenge in online learning is instructor availability: students anticipate frequent feedback and guidance but often experience delayed responses and fewer opportunities for clarification (Garip et al., 2020; Reid et al., 2024; Yan & Pourdavood, 2024). Similarly, peer interaction is often more limited than anticipated, contributing to isolation and reduced engagement (Lobos et al., 2022; Yalçın & Dennen, 2023). Without informal opportunities—such as casual conversations with peers or spontaneous interactions with instructors—to recalibrate expectations, students may struggle with self-directed learning, increasing the risk of disengagement or reliance on ineffective study strategies (Reid et al., 2024; Yan & Pourdavood, 2024).

Psychology as a Case Study for Expectation Mismatches

While expectation mismatches affect students across disciplines, psychology students represent a particularly instructive case for examining these phenomena due to widespread misconceptions about the field itself. As one of the most popular undergraduate disciplines worldwide (Murphy, 2024; The Condition of Education, 2024), psychology attracts students whose fundamental assumptions about the discipline often diverge dramatically from its academic reality. Specifically, psychology students often anticipate curricula centered on self-reflection, therapeutic skills, or clinical training, yet they frequently encounter programs grounded in research methods, statistics, and scientific theory (Collisson et al., 2023; Rowley et al., 2008; Winstone & Bretton, 2013). This misalignment extends to career expectations, with many students assuming undergraduate degrees provide direct pathways to clinical practice. In reality, postgraduate clinical training remains highly competitive, with approximately 80% of students unable to progress due to limited program capacity (Australian Psychological Society, 2022; Cranney et al., 2022). These structural barriers contribute to stress surrounding career progression (Cruwys et al., 2015) and may amplify disillusionment with the discipline (Rowley et al., 2008).

Unsurprisingly, psychology students report higher psychological distress and lower wellbeing compared to population norms, particularly as they progress through their studies. While students often attribute this distress to workload, evidence suggests it may also reflect persistent expectation—reality mismatches, particularly when anticipated support, structure, or course content fails to align with reality (Rowley et al., 2008; Winstone & Bretton, 2013). Many students begin their studies with optimism but report declining academic self-efficacy as they encounter unexpected academic demands or unclear pathways to their career goals. Among first-year psychology students, lower self-efficacy and feelings of disconnection have been linked to reduced wellbeing and engagement, particularly during transitional periods when students’ expectations go unmet or their coping strategies are insufficient (Denovan & Macaskill, 2017). These challenges may be particularly acute in online learning environments. As noted above, online education typically offers more limited real-time feedback and less structured support compared to traditional settings, potentially hampering students’ ability to recalibrate expectations and regulate their learning effectively (Garip et al., 2020; Lobos et al., 2022; Yalçın & Dennen, 2023; Yan & Pourdavood, 2024). The reduced instructor interaction and fewer informal opportunities characteristic of online learning may leave psychology students facing a greater risk of persistent mismatches between their expectations and the realities of the course.

This vulnerability is significant because the gap between expectations and reality may influence how students approach their learning and maintain motivation (Biggs, 2001; Fryer, 2017). While research in traditional educational settings demonstrates varied student responses to academic challenges—with some adapting their strategies while others become overwhelmed (Asikainen et al., 2020; Asikainen & Gijbels, 2017)—the specific manifestations of these patterns among online psychology students remain underexplored. Students’ approaches to learning (SAL) offer a useful lens for understanding these varied responses.

SAL: A Lens for Understanding Expectation Mismatches

Students’ approaches to learning can be categorized into three types: deep, surface, and strategic (Entwistle et al., 2000). A deep approach involves active engagement and the development of a comprehensive understanding by applying critical thinking and seeking connections with the subject matter (Asikainen & Gijbels, 2017). A surface approach relies on rote memorization and minimal effort, often driven by fear of failure rather than a desire to understand (Vanthournout et al., 2013). The strategic approach, sometimes called organized studying, focuses on structured learning, time management, and goal-oriented effort to maximize academic performance (Diseth, 2007; Entwistle & McCune, 2004).

Research demonstrates that students’ learning approaches influence both academic success and wellbeing. Deep and strategic learning are linked to higher achievement, with strategic learners benefiting from organization and effective time management (Asikainen et al., 2020; Richardson et al., 2012). In contrast, surface learning is associated with lower performance and greater study-related burnout, including exhaustion, cynicism, and feelings of inadequacy (Asikainen et al., 2020; Richardson et al., 2012). Deep and strategic learners tend to experience lower burnout, likely due to greater engagement and self-regulation (Asikainen et al., 2020; Räihä et al., 2024).

Because surface learning is linked to lower academic performance and higher stress, anxiety, and burnout (Asikainen et al., 2020; Richardson et al., 2012), we measured these to examine how mismatches in expectations may impact both students’ psychological outcomes and academic functioning. Although we use the term wellbeing outcomes throughout, our focus is specifically on negative indicators of wellbeing, such as stress, anxiety, and burnout, which research indicates may meaningfully impact overall student wellbeing (Dias Lopes et al., 2020; Slimmen et al., 2022). Together, these outcomes allow us to examine how mismatched expectations affect both academic functioning and psychological wellbeing.

The Relationship Between Student Expectations and Outcomes Through Approaches to Learning

To understand how unmet expectations influence both academic achievement and psychological wellbeing, we draw upon Biggs’ (2001) Presage–Process–Product (3P) model and perceived control theory (Fryer, 2017; Skinner, 1995). The 3P model proposes that Presage factors, such as autonomous motivation, prior achievement, and instructional design, shape the Process of learning through students’ approaches to study (deep, surface, and strategic), which then determines the Product: academic performance and psychological wellbeing (Wang et al., 2013).

Empirical evidence supports each stage of this theoretical sequence. Students with higher autonomous motivation are more likely to adopt deep learning strategies, even under heavy workloads (Kyndt et al., 2011), while collaborative activities foster metacognitive awareness and deep processing (Beccaria et al., 2014). Valadas et al. (2016) traced the full pathway, showing how prior achievement, study time, and course satisfaction led to strategic approaches, which improved exam scores. Additional research establishes connections between learning approaches and wellbeing outcomes: surface learners consistently report elevated stress levels and diminished life satisfaction, whereas deep and strategic learners demonstrate greater academic engagement and enhanced wellbeing (Asikainen et al., 2014; Diseth, 2007). Collectively, these findings demonstrate that presage conditions systematically influence learning approaches, which in turn shape both academic and wellbeing outcomes.

Despite this empirical support, the 3P model leaves an important question unanswered: Why do certain conditions lead students toward one learning approach rather than another? Fryer (2017) bridged this gap by incorporating the theory of perceived control. In his extended model, students’ beliefs about their ability to influence their learning environment became the key mechanism: those who feel they have control engage deeply or strategically, while those who feel powerless resort to surface approaches. Although researchers have not fully tested this integration, related research supports its logic, as studies consistently link higher perceived control and self-efficacy to deeper learning, and lower control to surface strategies (e.g., Prat-Sala & Redford, 2010).

We extend this reasoning in the current study by proposing that expectation mismatches—when coursework proves more demanding than anticipated—threaten students’ perceived control. When students encounter unanticipated academic difficulties, their sense of agency may be compromised, leading to an increased reliance on surface learning strategies. Conversely, when academic demands align with expectations, students likely maintain perceived control, thereby supporting deeper or strategic engagement. Guided by this extended theoretical model, we hypothesized that learning approaches mediate the relationship between expectation mismatches and both academic and wellbeing outcomes—specifically, that mismatches shape students’ learning approaches, thereby affecting academic and wellbeing outcomes. While previous research has examined how general course satisfaction relates to learning approaches (Asikainen et al., 2014; Diseth, 2007; Hua et al., 2024), research has yet to explore the specific role of expectation mismatches in this process—a gap the present study sought to address.

The Current Study

Despite growing interest in online education and student learning approaches, several gaps remain. First, although prior research has examined how presage variables, such as prior academic performance and course evaluations, shape learning approaches and academic outcomes (e.g., Asikainen et al., 2014; Diseth, 2007; Hua et al., 2024), research has paid less attention to the specific role of mismatched expectations as a predictor. In particular, few studies have examined individual-level changes in expectations, instead relying on group-level changes that may obscure meaningful differences in individual student trajectories (Asikainen & Gijbels, 2017; Cronbach & Furby, 1970; Hauser-Cram & Krauss, 1991). A residual change score approach offers a more sensitive way to assess mismatches by quantifying how much students’ actual experiences deviate from their initial expectations, categorizing them as unmet (negative scores), met (scores close to zero), or exceeded (positive scores) (Hauser-Cram & Krauss, 1991).

Second, research has extensively studied learning approaches in traditional classroom settings; however, their role in online education remains underexplored. Few studies have examined how online students adapt their engagement strategies in response to unique challenges such as increased autonomy and reduced external accountability (Asikainen & Gijbels, 2017; Beenen & Arbaugh, 2019). Understanding how students navigate mismatched expectations in these settings is particularly important given the expansion of online education.

Finally, Biggs’ 3P model suggests that students’ expectations, learning strategies, and academic outcomes are interconnected. However, few empirical studies have tested these relationships in online learning, particularly the potential mediating role of learning approaches between mismatched expectations and student outcomes. Clarifying these links can inform pedagogical strategies to support student adaptation and success. To explore these relationships, the present study examined the following research questions:
Do individual differences in the mismatch of expectations among undergraduate students correlate with their approaches to learning (surface, strategic & deep)?

Are approaches to learning (surface, strategic, and deep) correlated with academic achievement and wellbeing outcomes?

Do student approaches to learning mediate the relationship between a mismatch in expectations and academic achievement and/or wellbeing outcomes?

Method

Research Design

This study used a cross-sectional survey design, collecting data from 113 students enrolled in courses offered in the University of South Australia's fully online (UniSA Online) Bachelor of Psychology and Bachelor of Psychological Science and Sociology programs. In these programs, every course in the 3-year curriculum is delivered asynchronously online, with no on-campus requirements. Following ethics approval from our institution (Protocol: 205621), we advertised the study on program discussion forums and via e-mail in selected first-, second-, and third-year courses. Recruitment at a single time-point yielded a sample that exceeded the GPower3 estimate of 77 participants needed to detect a medium effect size (0.5) with 0.80 power (Faul et al., 2009). Each student could participate only once. On completion, participants could enter a draw to win one of five $100 prepaid MasterCards.

Measures

Participants

We asked participants to report their age, gender, degree commencement year, and the number of courses they had completed to date in their program. They also indicated their enrollment load (full- or part-time), whether they were a domestic or international student, and whether they were the first in their immediate family (i.e., parents, guardians, or siblings) to attend university. Additional items captured prior to higher education study in psychology and/or other disciplines, as well as weekly hours spent studying, in paid employment, and unpaid childcare or other caring roles.

Most of the sample identified as female (n = 90, 79.6%). Participants were primarily enrolled in the Bachelor of Psychology degree (n = 84, 74.3%) or the Bachelor of Psychological Science and Sociology degree (n = 20, 17.7%). Participants ranged in age from 18 to 71 years (M = 38.0, SD = 10.4), and all participants except one were domestic students. While we did not collect data on participants’ racial or ethnic backgrounds, country of birth information within these programs indicates the following distribution: Oceania (67.85%), Europe (6.74%), Asia (6.03%), Africa (3.62%), Middle East (1.28%), North America (0.78%), and Central and South America (0.71%). See Table 1 for more demographic information.

Table 1.
Demographics of Sample.

N %

Gender

Female 90 79.6

Male 18 15.9

Nonbinary 4 3.5

Prefer not to state 1 .9

Age (in years)

18–20 4 3.5

21–25 11 9.7

26–30 13 11.5

31–35 20 17.7

36–40 18 16.0

41–45 15 13.3

46–50 16 14.2

51+ 15 13.3

Not stated 1 .9

Enrollment information

Studying part-time 70 61.9

Studying full-time 43 38.1

Domestic student 112 99.1

International student 1 .9

Degree enrolled in

Bachelor of Psychology 84 74.3

Bachelor of Psychological Science and Sociology 20 17.7

Bachelor of Health Science 2 1.8

Double Degree Psychology and Law 2 1.8

Bachelor of Criminal Justice 2 1.8

Bachelor of Occupational Therapy 1 .9

Not reported 2 1.9

Expectations of Undergraduate Studies

We measured expectations using the 13-item scale developed by Rowley et al. (2008), which assessed preparedness for the study, generic study issues, subject-specific study issues, personal goals, and worries. Participants responded on a six-point Likert scale (1 = strongly disagree* to 6 = strongly agree). An example item is “I expect to carry out a large amount of independent reading.” Participants responded to the items in two scenarios, one being their initial expectations before commencing the degree (α = .72), and the second being their current expectations (α = .79). Reliability for the mismatch residuals, calculated using Malgady and Colon-Malgady's (1991) formula, was good (α = .72).

SAL

We measured SAL with the 18-item short version of the Approaches and Study Skills Inventory for Students (ASSIST) scale (Entwistle & Ramsden, 2015), which includes subscales for deep, surface, and strategic approaches to learning. They responded on a five-point Likert scale (1 = disagree to 5 = agree), with six items for each of the three subscales and a higher score representing greater engagement in that approach to learning. An example of the deep approach is: “When I’m reading an article or book, I try to find out for myself exactly what the author means.” Internal consistency for scores in the present study was good: deep α = .64, surface α = .79, and strategic α = .86.

University-Related Stress

We measured stress with the 21-item University Stress Scale (Stallman & Hurst, 2016), which assesses the cognitive appraisals related to common environmental stressors experienced by higher education students. Participants responded on a four-point Likert scale (1 = not at all to 4 = constantly) to indicate which of the 21 stressors had caused them stress over the past month. We calculated a mean score, with higher scores indicating greater stress. Example stressors include academic/coursework demands, study/life balance, and the online university environment. Stallman and Hurst (2016) found scores had good internal consistency among Australian university students (α = .83) and good convergent validity with the stress scale of the Depression Anxiety Stress Scales (DASS; r = .47). Internal consistency for scores in the present study was α = .81.

University-Related Anxiety

We measured anxiety using a six-item scale developed for this study to assess anxiety specific to online tertiary education. Existing academic anxiety scales were unsuitable, as many were designed for younger populations (e.g., Academic Anxiety Scale for Children, Singh & Sengupta, 2013), focused on general anxiety rather than higher education demands (Adult Manifest Anxiety Scale—College Version [AMAS-C], Reynolds et al., 2003), or emphasized test and classroom anxiety (Academic Anxiety Scale, Cassady et al., 2019; Gogol et al., 2014). Many were also too lengthy (e.g., AMAS-C: 49 items) for practical use. No existing scale adequately captures the modern challenges of tertiary education, such as navigating digital platforms, managing time in flexible environments, and engaging in online classrooms. To address this, we developed a concise, targeted measure assessing academic anxiety in digital learning contexts.

Participants rated their anxiety on a six-point Likert scale (1 = not at all anxious to 6 = extremely anxious), with higher scores indicating greater anxiety. An example item is: “How anxious do you feel about your ability to succeed academically in online learning?” The measure demonstrated good reliability (α = .85). We used scores in subsequent analyses to explore relationships with student outcomes.

University-Related Burnout

We measured burnout with the 16-item Maslach Burnout Inventory-Student Survey (MBI-SS; Shi et al., 2018). Kim et al. (2018) found the MBI-SS was the most commonly used measure of student burnout as per their meta-analytical review, and it is a validated measure of emotional exhaustion, cynicism, and academic efficacy in university students (Shi et al., 2018). Participants responded on a seven-point frequency rating scale (0 = rarely to 6 = always), with higher scores indicating greater burnout. An example item is “I feel emotionally drained by my studies.” Internal consistency for scores in the present study were exhaustion α = .89, cynicism α = .89, and academic efficacy α = .82.

Academic Achievement

We measured academic achievement using a single item that asked participants to self-report their grade point average (GPA). We calculated GPA as the sum of grade points multiplied by course unit values, which we then divided by the sum of course unit values. GPA ranges from 1 to 7, with higher scores indicating greater academic achievement.

Data Analyses

We exported data from Qualtrics to SPSS version 29 for cleaning and screening. After testing statistical assumptions, we conducted the following analyses to address the research questions. To quantify expectation mismatches at the individual level, we used a linear regression model, deriving unstandardized residuals where initial expectations predicted current expectations. Positive residual values indicated that students’ current expectations exceeded their initial expectations (positive mismatch), while negative residuals indicated that initial expectations were unmet (negative mismatch).

For research questions 1 and 2, we used Pearson correlations to estimate the association between expectation mismatches, students’ learning approaches (deep, surface, and strategic), wellbeing (i.e., anxiety, stress, and burnout), academic achievement (i.e., GPA), and expectations of undergraduate psychology studies (i.e., preparedness, generic and subject specific study issues, personal goals, and worries). For research question 3, we conducted mediation analyses using model 4 of the PROCESS macro (Hayes, 2013) with 5000 bootstrapped samples in SPSS (version 29). Separate models tested whether learning approaches mediated the relationship between expectation mismatches and academic achievement and wellbeing outcomes.

We detected outliers using the z-score method (threshold: 3 SD from the mean; Osborne & Overbay, 2004). Two outliers were identified on cynicism (MBI-SS) and one on deep approach to learning (ASSIST), which were winsorized by replacing them with values 1% higher/lower than the closest nonextreme value (Tabachnick & Fidell, 2007). One GPA outlier was excluded from analyses involving GPA. Table 2 provides the descriptive statistics for the sample.

Table 2.
Descriptive Statistics for 113 Online, Undergraduate Students.

Measure N Min Max M (SD) Skewness (SE) Kurtosis (SE)

Expectations

Initial 113 32 76 55.22 (8.04) −.06 (.23) .26 (.45)

Current 113 31 78 56.75 (9.12) −.11 (.23) −.09 (.45)

Mismatch 113 −17.03 14.74 0.00 (6.07) −.18 (.23) .31 (.45)

Burnout

Exhaustion 113 1.00 7.00 3.08 (1.34) .61 (.23) −.06 (.45)

Cynicism 113 1.00 5.35 2.12 (1.16) 1.00 (.23) .25 (.45)

Efficacy 113 2.67 7.00 5.05 (.93) −.19 (.23) −.34 (.45)

SAL

Deep 113 2.64 5.00 3.87 (.51) −.25 (.23) −.22 (.45)

Strategic 113 1.00 5.00 3.73 (.88) −.58 (.23) −.17 (.45)

Surface 113 1.00 4.50 2.67 (.77) −.08 (.23) −.86 (.45)

Wellbeing

Stress 113 1.19 3.19 2.00 (.41) .36 (.23) −.40 (.45)

Anxiety 113 1.00 4.83 2.67 (1.00) .39 (.23) −.92 (.45)

GPA 96 2.64 7.00 5.64 (.92) −.70 (.25) −.08 (.49)

Note. The mismatch variable has a mean of 0, as these data are based on the residual values.

GPA=grade point average; SAL=students' approaches to learning; SE=standard error.

All distributions met parametric analysis assumptions (skew < |2.0 and kurtosis < |9.0; Bishara & Hittner, 2012; Edgell & Noon, 1984). Variables showed considerable variability (i.e., large standard deviations, Table 2), with no floor or ceiling effects, indicating that our measures effectively captured the variation among participants.

Results

Associations Between Expectations, SAL, Academic Achievement, and Wellbeing

Research question 1 examined whether expectation mismatches were associated with learning approaches. Results showed that students’ unmet expectations were significantly linked to greater surface learning, while exceeded expectations were associated with more deep and strategic learning (see Table 3).

Table 3.
Intercorrelations Between All Variables.

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1. Initial expectations –

2. Current expectations .75 –

3. Mismatch in expectations .00 .67 –

4. Deep approach .35 .43 .25 –

5. Strategic approach .41 .58 .42 .35 –

6. Surface approach −.35 −.48 −.33 −.27 −.42 –

7. Stress −.02 −.15 −.20* −.09 −.29 .45 –

8. Anxiety −.22* −.30** −.21* −.16 −.30 .70 .47 –

9. Burnout—exhaustion −.33 −.34** −.14 −.24* −.36 .53 .60 .52 –

10. Burnout—cynicism −.28 −.47 −.39 −.29 −.41 .52 .22* .37 .36 –

11. Burnout—academic-efficacy .39 .61 .48 .38 .64 −.53 −.26 −.48 −.28 −.48 –

12. Age .27** .15 −.08 .16 .23* −.09 −.18 −.04 −.24* .09 .14 –

13. Gender −.04 .11 .21* .04 .07 .06 .03 −.01 .04 −.17 .03 −.17 –

14. GPA^a −.09 .12 .27** .18 .20* −.24* −.11 −.21* −.03 −.15 .23* .09 −.01 –

15. Study hours .13 .29 .29 .05 .39 −.15 .03 −.13 .04 −.25 .31** .08 −.01 .15 –

16. Work hours .07 −.02 −.10 −.02 −.11 .12 .06 .15 .05 .00 −.17 .06 −.09 −.20* −.34** –

17. Childcare hours .20* .14 −.02 .14 .13 −.10 .27** −.07 .05 −.08 .18 .14 .00 .00 .06 −.23* –

18. Prior psych study .20* .09 −.08 .10 .03 .03 .01 −.06 .03 .05 −.02 .14 .00 −.11 −.07 .18 .03 –

19. Prior other study .07 .02 −.05 .00 −.12 .16 .10 .15 .02 .05 −.14 .08 .07 .16 −.12 .17 −.03 −.04 –

Note. Correlation is significant at the .01 level (2-tailed).

•Correlation is significant at the .05 level (2-tailed).

GPA=grade point average.

Research question 2 explored whether learning approaches correlated with academic achievement and wellbeing. Findings indicated that a surface approach was significantly associated with higher stress, anxiety, exhaustion, and cynicism, as well as lower academic efficacy and GPA. In contrast, a strategic approach correlated with lower stress, anxiety, exhaustion, and cynicism, alongside higher academic efficacy and GPA. A deep approach was negatively associated with exhaustion and cynicism and positively linked to academic efficacy (see Table 3 for effect sizes).

Although not a primary focus, we also found a significant association between gender and expectation mismatches. People who identified as male were more likely to have unmet expectations than people who identified as female. Additionally, older individuals were more likely to adopt strategic learning methods, and this approach was associated with a greater number of hours spent studying per week.

Mediation Pathways

Research question 3 sought to determine whether students’ approach to learning mediated the relationship between mismatches in students’ expectations and their academic and wellbeing outcomes. Our analysis revealed that surface and strategic learning approaches were significant mediators between mismatches in students’ expectations and wellbeing outcomes (see Table 4 for all effects).

Table 4.
Pathway Analysis Statistics for Relationships Between a Mismatch in Student Expectations and Stress, Anxiety, Burnout, and GPA Through Approaches to Learning.

Path Surface Strategic Deep

a: (Mismatch → SAL) β = −.33 β = .42** β = .25*

Stress

b: (SAL → Stress) β = .44** β = −.25* β = −.04

c: Total (Mismatch → Stress) β = −.20* β = −.20* β = −.20*

c’: Direct (Mismatch → Stress) β = −.05 β = −.20 β = −.19*

ab: Indirect (Mismatch → SAL → Stress) β = −.14 (.05)†, 95% CI [−.27, −.05] β = −.10 (.04)†, 95% CI [−.19, −.03] β = −.01 (.02), 95% CI [−.06, .04]

Anxiety

b: (SAL → Anxiety) β = .70** β = −.25* β = −.12

c: Total (Mismatch → Anxiety) β = −.21* β = −.21* β = −.21*

c’: Direct (Mismatch → Anxiety) β = .02 β = −.11 β = −.19

ab: Indirect (Mismatch → SAL → Anxiety) β = −.04 (.01)†, 95% CI [−.35, −.10] β = −.10 (.05)†, 95% CI [−.21, −.01] β = −.03 (.03), 95% CI [−.09, .02]

Burnout—exhaustion

b: (SAL → Exhaustion) β = .55 β = −37 β = −.56 (.25)*

c: Total (Mismatch → Exhaustion) β = −.13 β = −.13 β = −.13

c’: Direct (Mismatch → Exhaustion) β = .05 β = .02 β = −.08

ab: Indirect (Mismatch → SAL → Exhaustion) β = −.18 (.06)†, 95% CI [−.31, −.07] β = −.15 (.04)†, 95% CI [−.25, −.08] β = −.05 (.03), 95% CI [−.12, −.00]

Burnout—cynicism

b: (SAL → Cynicism) β = .44 β = −.30 β = −.20*

c: Total (Mismatch → Cynicism) β = −.39 β = −.39 β = −.39

c’: Direct (Mismatch → Cynicism) β = −.24 β = −.26 β = −.34

ab: Indirect (Mismatch → SAL → Cynicism) β = −.15 (.05)†, 95% CI [−.25, −.06] β = −.12 (.04)†, 95% CI [−.21, −.05] β = −.05 (.03), 95% CI [−.12, .00]

Burnout—efficacy

b: (SAL → Self-efficacy) β = −.41 β = .53 β = .28

c: Total (Mismatch → Efficacy) β = .48 β = .48 β = .48

c’: Direct (Mismatch → Efficacy) β = .35 β = .26 β = .41

ab: Indirect (Mismatch → SAL → Efficacy) β = .14 (.04)†, 95% CI [.05, .22] β = .22 (.05)†, 95% CI [.12, .31] β = .07 (.03)†, 95% CI [.01, .14]

GPA

b: (SAL → GPA) β = −.17 (.14) β = .10 β = .12

c: Total (Mismatch → GPA) β = .27 β = .27 β = .27

c’: Direct (Mismatch → GPA) β = .22 β = .23 β = .24*

ab: Indirect (Mismatch → SAL → GPA) β = .06 (.04), 95% CI [−.02, .13] β = .04 (.04), 95% CI [−.03, .11] β = .03 (.03), 95% CI [−.03, .09]

Note. Standardized coefficients are reported because they are scale-free and comparable across variables. Total effect (c): the total impact of mismatch on the outcome without considering the mediating effect of SAL; Direct effect (c’): the impact of mismatch on the outcome after accounting for the mediating effect of SAL; Indirect effect (ab): the mediating effect of SAL on the relationship between mismatch and the outcome. CI=confidence interval; GPA=grade point average; SAL=students' approaches to learning.

^†AB path significance determined by the exclusion of 0 in the 95% CI.

Stress and Anxiety

For stress, surface learning approaches exacerbated the impact of negative mismatches, significantly increasing stress levels (indirect effect: β = −.14 (.05), 95% confidence interval [CI] [−.27, −.05]). Conversely, strategic approaches ameliorated stress where positive mismatches occurred (indirect effect: β = −.10 (.04), 95% CI [−.19, −.03]). Deep learning approaches did not show a significant mediating effect on stress (indirect effect: β = −.01 (.02), 95% CI [−.06, .04]). Similarly, anxiety patterns reflected those of stress, with surface approaches increasing anxiety under negative mismatch conditions (indirect effect: β = −.04 (.01), 95% CI [−.35, −.10]) and strategic approaches reducing anxiety under positive mismatches (indirect effect: β = −.10 (.05), 95% CI [−.21, −.01]). Again, deep approaches did not significantly mediate anxiety.

Burnout—Exhaustion, Cynicism, and Academic Efficacy

Surface approaches significantly mediated increases in exhaustion and cynicism related to negative mismatches (indirect effects: β = −.18 (.06), 95% CI [−.31, −.07] for exhaustion; β = −.15 (.05), 95% CI [−.25, −.06] for cynicism). Strategic approaches showed a protective effect, particularly evident in enhancing academic efficacy (indirect effect: β = .22 (.05), 95% CI [.12, .31]). Deep learning approaches did not have significant mediating effects on any burnout dimensions.

GPA

Neither surface, strategic, nor deep learning approaches significantly mediated the relationship between mismatches and GPA. The indirect effects were small and not statistically significant (surface: β = .06 (.04), 95% CI [−.02, .13]; strategic: β = .04 (.04), 95% CI [−.03, .11]; deep: β = .03 (.03), 95% CI [−.03, .09]).

Discussion

Student expectations influence academic engagement and performance (Tomlinson et al., 2023), and mismatches between expectations and experiences can lead to stress, disengagement, and lower achievement (Asikainen et al., 2020; Winstone & Hulme, 2019). While most research has focused on in-person learning, little is known about how expectation mismatches affect the academic and wellbeing outcomes of online students. This study examined the role of learning approaches in mediating these relationships in an online undergraduate psychology program.

We found that students’ unmet expectations were linked to higher stress, anxiety, and burnout, while exceeded expectations correlated with better well-being and higher GPAs. Learning approaches mediated the relationship between expectation mismatches and wellbeing but did not significantly mediate academic outcomes. Notably, strategic learning emerged as a potential protective factor, with students who had exceeded expectations engaging more in strategic learning, which in turn was associated with lower stress and greater academic efficacy. These findings extend prior work on student adjustment and align with the 3P model of student learning (Biggs, 2001), which suggests that presage factors (e.g., expectation mismatches) influence learning processes and subsequent outcomes.

A Mismatch in Expectations and Outcomes

Consistent with prior research (Asikainen et al., 2020; Tomlinson et al., 2023; Winstone & Hulme, 2019), our study confirms that mismatches in expectations are a common experience among online students and are associated with both wellbeing and academic performance. We found negative mismatches—where students’ experiences fell short of their expectations—were linked to increased stress, anxiety, and burnout (both exhaustion-related and cynicism-related). These students also reported lower GPAs, illustrating the consequences of unmet expectations on academic outcomes, which is particularly concerning given that GPA influences postgraduate opportunities. In contrast, students who experienced positive mismatches—where their academic experiences exceeded initial expectations—reported higher academic efficacy, greater study engagement, and improved GPAs. Students with exceeded expectations also reported studying more hours per week, suggesting that positive expectation mismatches may foster greater academic motivation and engagement.

Our findings also revealed gender differences in expectation mismatches, with men more likely than women to report unmet expectations. Prior research suggests that men are less likely to seek social support or engage in verbal coping strategies when facing academic challenges, instead relying more on avoidance and withdrawal (García-Jiménez et al., 2024). Although our findings did not indicate significant gender differences in stress levels, men exhibited slightly higher levels of cynicism. This trend suggests that unmet expectations may contribute to feelings of detachment from studies, particularly in online learning environments where opportunities for informal social interaction are limited. Future research could explore how gendered coping styles influence academic adjustment and retention in online psychology programs.

The Mediating Role of Approaches to Learning for Wellbeing Outcomes

Our findings align with the 3P model of student learning (Biggs, 2001), which suggests that presage factors (e.g., expectation mismatches) influence learning processes, which in turn shape student outcomes. Strategic learning buffered the relationship between expectation mismatches and wellbeing, with structured, goal-oriented study strategies linked to greater academic efficacy and lower stress. Students who engaged in structured, goal-oriented study strategies reported greater academic efficacy and lower stress, consistent with Skinner's perceived control theory (1995). This theory suggests that students’ beliefs about their ability to manage their learning environment influence their engagement strategies and wellbeing. When students feel capable of adapting to academic challenges, they are more likely to adopt structured, goal-oriented behaviors (i.e., strategic learning), which reinforces their sense of control and reduces stress.

Conversely, students who relied on surface learning strategies—which are often used as coping mechanisms when students feel overwhelmed (Fryer, 2017; Skinner, 1995)—reported heightened stress, anxiety, and burnout. This suggests that surface learners may lack the self-regulatory skills necessary to navigate discrepancies between expectations and reality, making them more vulnerable to academic stress and disengagement.

While deep learning was linked to some wellbeing benefits, such as lower burnout and greater academic efficacy, it did not significantly mediate the relationship between expectation mismatches and wellbeing. In online settings, strategic learning—focused on time management, self-discipline, and structured study strategies—may be more adaptive, as students must independently regulate their learning. Given that many online students balance academic, work, and family commitments, strategic approaches may be more practical than deep approaches, which prioritize intellectual curiosity but require sustained engagement with content (Räihä et al., 2024).

It is also possible that students who use deep learning approaches are already predisposed to higher baseline motivation (Kyndt et al., 2011). For example, Asikainen and Gijbels (2017) found that students who adopt deep learning often have stable individual characteristics such as motivation. Higher baseline motivation may limit deep learning approaches as a mediator because deep learners may be able to remain motivated regardless of a mismatch in expectations. Additionally, Marton and Säljö (1976) suggest that deep learning approaches are driven by the motivation to understand meaning and integrate knowledge, rather than achieve immediate performance outcomes. Therefore, the benefits of deep learning may unfold over longer timeframes and may not align closely with the short-term measures of stress, anxiety, or burnout used in this study.

Approaches to Learning and Academic Performance

Although strategic and surface learning mediated the effects of expectation mismatches on wellbeing, they did not significantly mediate the effects on GPA outcomes. Although GPA was associated with expectation mismatches, learning approaches did not mediate these relationships, suggesting that other factors—such as motivation, self-selection biases, and discipline-specific demands—may play a stronger role in academic performance (Asikainen et al., 2020). Psychology students, particularly those intending to enter competitive postgraduate programs, may persist despite mismatches in expectations, adjusting their study strategies as needed.

Additionally, our sample only included students who remained enrolled, meaning that we did not capture students who withdrew due to severe mismatches in our data. Prior research suggests that expectation mismatches contribute to student attrition (Hyllegard et al., 2008). Future studies should examine whether students who leave psychology programs due to unmet expectations exhibit different learning patterns and academic trajectories.

Finally, the lack of mediation effects for GPA may reflect limited variability in the self-reported data. GPA scores were relatively high and clustered (Table 2), which may reduce statistical power. Additionally, students self-reported their GPA, which may introduce bias or error if students overestimate or round up their grades. Future research using objective academic records may more accurately capture links between learning approaches and achievement.

Implications for Psychology Education

Our findings highlight several key implications for psychology education, particularly in online settings. Addressing mismatches between students’ expectations and the realities of psychology programs is essential for fostering wellbeing and academic success. Transparent communication about program demands, career pathways, and postgraduate requirements could help mitigate unrealistic expectations and improve student retention.

Promoting strategic learning approaches should also be a priority. These approaches not only enhance academic performance but also support student well-being by reducing stress and fostering academic efficacy (Beenen & Arbaugh, 2019). Psychology educators can integrate structured time management exercises, self-regulation training, and mentoring programs into first-year courses to help students develop effective study habits.

Our findings further challenge the assumption that students naturally transition from surface to deep learning over time (e.g., Lake & Boyd, 2015). Evidence remains inconsistent on whether students develop deeper learning approaches as they progress (for reviews, see Asikainen & Gijbels, 2017; Baeten et al., 2010). Instead, our results suggest that strategic learning serves as a valuable intermediate step, particularly for online students balancing multiple responsibilities. Higher education providers should explicitly teach strategic learning techniques rather than assuming students will adopt them independently.

Given that strategic learning was associated with better wellbeing outcomes, psychology programs—particularly in online settings—should prioritize interventions that promote self-regulation and psychological flexibility. Providing clearer pre-enrollment guidance on coursework demands could also help prevent expectation mismatches. Research suggests that structured self-reflection exercises, time management training, and mentoring programs may improve student adjustment (Räihä et al., 2024). One potential avenue for supporting the development of strategic learning is fostering psychological flexibility—the ability to adapt to challenges and persist despite setbacks. Recent research suggests that interventions based on Acceptance and Commitment Therapy (Hayes et al., 2006), which focuses on self-regulation and resilience, can enhance psychological flexibility and, in turn, promote strategic learning behaviors. Researchers have found these interventions reduce burnout in online education (e.g., Räihä et al., 2024). Hence, students may benefit from structured interventions that promote self-reflection, time management, and proactive coping strategies, helping them navigate expectation mismatches more effectively when they arise.

Another important consideration is the development of an “online learner identity,” which refers to students’ ability to adapt to the self-directed nature of online education and take ownership of their learning (Garip et al., 2020). Students who struggle to establish this identity may face challenges with motivation, time management, and self-regulation, increasing their reliance on surface learning approaches when their expectations do not align with reality. Instructors can support this process by integrating structured time management exercises, goal-setting activities, and self-reflection prompts into course design. Strengthening students’ capacity for self-directed learning may be a key factor in fostering strategic learning approaches in online psychology programs.

In addition to promoting strategic learning approaches, course design itself can play a role in reducing expectation mismatches. A systematic review by Álvarez et al. (2022) found that incorporating scaffolding techniques, such as metacognitive prompts, into dashboard-based tools can improve students’ self-regulated learning by encouraging students to independently identify gaps in their learning. Moreover, interactive content (e.g., quizzes and progress checklists) allows students to more effectively self-regulate their learning by allowing them to independently track their progress, adjust their study strategies, and identify misunderstandings in real time (Nadeem & Al Falig, 2020).

Strengths and Limitations

This study addressed key gaps in research on the online undergraduate student experience. A major strength was its focus on fully online students, contributing to a growing area of interest as online education continues to expand. Another strength was the direct measurement of expectation mismatches rather than relying on course evaluations. Much of the existing literature has examined whether course evaluation data or student approaches to learning predict academic achievement (e.g., Asikainen et al., 2014; Diseth, 2007; Hua et al., 2024). However, routine course evaluations, while useful for gauging satisfaction, do not typically capture the subjective experience of learning or how students adjust their approaches in response to academic demands.

Despite these strengths, researchers should consider several limitations. Our sample was limited to undergraduate psychology students studying online, meaning the findings do not generalize to other disciplines or on-campus learners. Additionally, while we identified approaches to learning as mediators, we assessed mediation using cross-sectional data. Although common in social science research, this design limits causal inferences, and future studies should use longitudinal designs to confirm the proposed mediation sequence. Finally, we measured expectations retrospectively at a single time point, requiring participants to recall their initial expectations after starting their program. A longitudinal approach tracking expectations before and after enrollment would provide a more reliable assessment of expectation mismatches over time.

Conclusion

This study highlights the complex role of expectation mismatches in student learning, wellbeing, and academic performance. While learning approaches mediated the relationship between mismatches and wellbeing, they did not explain the differences in GPA. This suggests that other factors, such as motivation and self-regulation, play a more significant role in academic success. Strategic learning emerged as a key protective factor for wellbeing, reinforcing the importance of structured self-regulation strategies in psychology education. Given the persistence of expectation mismatches in online learning, future research should examine how individual differences, including motivation, cognitive load, and adaptability, moderate the effects of mismatches on student outcomes. Longitudinal research could further clarify how student expectations evolve and whether shifts in learning approaches predict sustained wellbeing and academic achievement in online education.

	N	%
Gender
Female	90	79.6
Male	18	15.9
Nonbinary	4	3.5
Prefer not to state	1	.9
Age (in years)
18–20	4	3.5
21–25	11	9.7
26–30	13	11.5
31–35	20	17.7
36–40	18	16.0
41–45	15	13.3
46–50	16	14.2
51+	15	13.3
Not stated	1	.9
Enrollment information
Studying part-time	70	61.9
Studying full-time	43	38.1
Domestic student	112	99.1
International student	1	.9
Degree enrolled in
Bachelor of Psychology	84	74.3
Bachelor of Psychological Science and Sociology	20	17.7
Bachelor of Health Science	2	1.8
Double Degree Psychology and Law	2	1.8
Bachelor of Criminal Justice	2	1.8
Bachelor of Occupational Therapy	1	.9
Not reported	2	1.9

Measure	N	Min	Max	M (SD)	Skewness (SE)	Kurtosis (SE)
Expectations
Initial	113	32	76	55.22 (8.04)	−.06 (.23)	.26 (.45)
Current	113	31	78	56.75 (9.12)	−.11 (.23)	−.09 (.45)
Mismatch	113	−17.03	14.74	0.00 (6.07)	−.18 (.23)	.31 (.45)
Burnout
Exhaustion	113	1.00	7.00	3.08 (1.34)	.61 (.23)	−.06 (.45)
Cynicism	113	1.00	5.35	2.12 (1.16)	1.00 (.23)	.25 (.45)
Efficacy	113	2.67	7.00	5.05 (.93)	−.19 (.23)	−.34 (.45)
SAL
Deep	113	2.64	5.00	3.87 (.51)	−.25 (.23)	−.22 (.45)
Strategic	113	1.00	5.00	3.73 (.88)	−.58 (.23)	−.17 (.45)
Surface	113	1.00	4.50	2.67 (.77)	−.08 (.23)	−.86 (.45)
Wellbeing
Stress	113	1.19	3.19	2.00 (.41)	.36 (.23)	−.40 (.45)
Anxiety	113	1.00	4.83	2.67 (1.00)	.39 (.23)	−.92 (.45)
GPA	96	2.64	7.00	5.64 (.92)	−.70 (.25)	−.08 (.49)

Variable	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
1. Initial expectations	–
2. Current expectations	.75**	–
3. Mismatch in expectations	.00	.67**	–
4. Deep approach	.35**	.43**	.25**	–
5. Strategic approach	.41**	.58**	.42**	.35**	–
6. Surface approach	−.35**	−.48**	−.33**	−.27**	−.42**	–
7. Stress	−.02	−.15	−.20*	−.09	−.29**	.45**	–
8. Anxiety	−.22*	−.30**	−.21*	−.16	−.30**	.70**	.47**	–
9. Burnout—exhaustion	−.33**	−.34**	−.14	−.24*	−.36**	.53**	.60**	.52**	–
10. Burnout—cynicism	−.28**	−.47**	−.39**	−.29**	−.41**	.52**	.22*	.37**	.36**	–
11. Burnout—academic-efficacy	.39**	.61**	.48**	.38**	.64**	−.53**	−.26**	−.48**	−.28**	−.48**	–
12. Age	.27**	.15	−.08	.16	.23*	−.09	−.18	−.04	−.24*	.09	.14	–
13. Gender	−.04	.11	.21*	.04	.07	.06	.03	−.01	.04	−.17	.03	−.17	–
14. GPA^a	−.09	.12	.27**	.18	.20*	−.24*	−.11	−.21*	−.03	−.15	.23*	.09	−.01	–
15. Study hours	.13	.29**	.29**	.05	.39**	−.15	.03	−.13	.04	−.25**	.31**	.08	−.01	.15	–
16. Work hours	.07	−.02	−.10	−.02	−.11	.12	.06	.15	.05	.00	−.17	.06	−.09	−.20*	−.34**	–
17. Childcare hours	.20*	.14	−.02	.14	.13	−.10	.27**	−.07	.05	−.08	.18	.14	.00	.00	.06	−.23*	–
18. Prior psych study	.20*	.09	−.08	.10	.03	.03	.01	−.06	.03	.05	−.02	.14	.00	−.11	−.07	.18	.03	–
19. Prior other study	.07	.02	−.05	.00	−.12	.16	.10	.15	.02	.05	−.14	.08	.07	.16	−.12	.17	−.03	−.04	–

Path	Surface	Strategic	Deep
a: (Mismatch → SAL)	β = −.33**	β = .42**	β = .25*
Stress
b: (SAL → Stress)	β = .44**	β = −.25*	β = −.04
c: Total (Mismatch → Stress)	β = −.20*	β = −.20*	β = −.20*
c’: Direct (Mismatch → Stress)	β = −.05	β = −.20	β = −.19*
ab: Indirect (Mismatch → SAL → Stress)	β = −.14 (.05)†, 95% CI [−.27, −.05]	β = −.10 (.04)†, 95% CI [−.19, −.03]	β = −.01 (.02), 95% CI [−.06, .04]
Anxiety
b: (SAL → Anxiety)	β = .70**	β = −.25*	β = −.12
c: Total (Mismatch → Anxiety)	β = −.21*	β = −.21*	β = −.21*
c’: Direct (Mismatch → Anxiety)	β = .02	β = −.11	β = −.19
ab: Indirect (Mismatch → SAL → Anxiety)	β = −.04 (.01)†, 95% CI [−.35, −.10]	β = −.10 (.05)†, 95% CI [−.21, −.01]	β = −.03 (.03), 95% CI [−.09, .02]
Burnout—exhaustion
b: (SAL → Exhaustion)	β = .55**	β = −37**	β = −.56 (.25)*
c: Total (Mismatch → Exhaustion)	β = −.13	β = −.13	β = −.13
c’: Direct (Mismatch → Exhaustion)	β = .05	β = .02	β = −.08
ab: Indirect (Mismatch → SAL → Exhaustion)	β = −.18 (.06)†, 95% CI [−.31, −.07]	β = −.15 (.04)†, 95% CI [−.25, −.08]	β = −.05 (.03), 95% CI [−.12, −.00]
Burnout—cynicism
b: (SAL → Cynicism)	β = .44**	β = −.30**	β = −.20*
c: Total (Mismatch → Cynicism)	β = −.39**	β = −.39**	β = −.39**
c’: Direct (Mismatch → Cynicism)	β = −.24**	β = −.26**	β = −.34**
ab: Indirect (Mismatch → SAL → Cynicism)	β = −.15 (.05)†, 95% CI [−.25, −.06]	β = −.12 (.04)†, 95% CI [−.21, −.05]	β = −.05 (.03), 95% CI [−.12, .00]
Burnout—efficacy
b: (SAL → Self-efficacy)	β = −.41**	β = .53**	β = .28**
c: Total (Mismatch → Efficacy)	β = .48**	β = .48**	β = .48**
c’: Direct (Mismatch → Efficacy)	β = .35	β = .26**	β = .41**
ab: Indirect (Mismatch → SAL → Efficacy)	β = .14 (.04)†, 95% CI [.05, .22]	β = .22 (.05)†, 95% CI [.12, .31]	β = .07 (.03)†, 95% CI [.01, .14]
GPA
b: (SAL → GPA)	β = −.17 (.14)	β = .10	β = .12
c: Total (Mismatch → GPA)	β = .27**	β = .27	β = .27**
c’: Direct (Mismatch → GPA)	β = .22	β = .23	β = .24*
ab: Indirect (Mismatch → SAL → GPA)	β = .06 (.04), 95% CI [−.02, .13]	β = .04 (.04), 95% CI [−.03, .11]	β = .03 (.03), 95% CI [−.03, .09]

Footnotes

Acknowledgements

The study has been financed by a UniSA Online Research Incentive Fund from the University of South Australia.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics Approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the University of South Australia Human Research Ethics Committee (Protocol: 205621).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a UniSA Online Research Incentive Fund from the University of South Australia.

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Consent for Publication

The present manuscript does not include any individual person's data in any form including individual details, images, or videos.

Data Availability Statement

The data that support the findings of this study are available upon reasonable request from the corresponding author.

ORCID iDs

John Mingoia

Laura M. Engfors

Brianna Le Busque

Olivia Burton

References

Alvarez

R. P.

Jivet

Perez-Sanagustin

Scheffel

Verbert

(2022). Tools designed to support self-regulated learning in online learning environments: A systematic review. IEEE Transactions on Learning Technologies, 15(4), 508–522. https://doi.org/10.1109/tlt.2022.3193271

Ang

C.-S.

Lee

K.-F.

Dipolog-Ubanan

G. F.

(2019). Determinants of first-year student identity and satisfaction in higher education: A quantitative case study. Sage Open, 9(2). https://doi.org/10.1177/2158244019846689

Asikainen

Gijbels

(2017). Do students develop towards more deep approaches to learning during studies? A systematic review on the development of students’ deep and surface approaches to learning in higher education. Educational Psychology Review, 29(2), 205–234. https://doi.org/10.1007/s10648-017-9406-6

Asikainen

Parpala

Lindblom-Ylänne

Vanthournout

Coertjens

(2014). The development of approaches to learning and perceptions of the teaching-learning environment during bachelor-level studies and their relation to study success. Higher Education Studies, 4(4). https://doi.org/10.5539/hes.v4n4p24

Asikainen

Salmela-Aro

Parpala

Katajavuori

(2020). Learning profiles and their relation to study-related burnout and academic achievement among university students. Learning and Individual Differences, 78, 101781. https://doi.org/10.1016/j.lindif.2019.101781

Australian Psychological Society. (2022). What do psychology graduates really think of their undergraduate degree? https://psychology.org.au/for-members/publications/inpsych/2022/summer-2022/what-do-psychology-graduates-really-think-of-their

Baeten

Kyndt

Struyven

Dochy

(2010). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness. Educational Research Review, 5(3), 243–260. https://doi.org/10.1016/j.edurev.2010.06.001

Beccaria

Kek

Huijser

Rose

Kimmins

(2014). The interrelationships between student approaches to learning and group work. Nurse Education Today, 34(7), 1094–1103. https://doi.org/10.1016/j.nedt.2014.02.006

Beenen

Arbaugh

(2019). Flipping class: Why student expectations and person-situation fit matter. The International Journal of Management Education, 17(3), 100311. https://doi.org/10.1016/j.ijme.2019.100311

10.

Biggs

(2001). The reflective institution: Assuring and enhancing the quality of teaching and learning. Higher Education, 41, 221–238. https://doi.org/10.1023/A:1004181331049

11.

Bishara

A. J.

Hittner

J. B.

(2012). Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches. Psychological Methods, 17(3), 399–417. https://doi.org/10.1037/a0028087

12.

Cassady

J. C.

Pierson

E. E.

Starling

J. M.

(2019). Predicting student depression with measures of general and academic anxieties. Frontiers in Education, 4, https://doi.org/10.3389/feduc.2019.00011

13.

Collisson

Eck

B. E.

Harig

(2023). Introducing Gen Z psychology majors: Why they choose to major in psychology (and what they expect to learn). Scholarship of Teaching and Learning in Psychology, 9(3), 276–287. https://doi.org/10.1037/stl0000249

14.

The Condition of Education. (2024). Undergraduate degree fields. National Center for Education Statistics. https://nces.ed.gov/programs/coe/pdf/2024/cta_508c.pdf

15.

Cranney

Dunn

D. S.

Hulme

J. A.

Nolan

S. A.

Morris

Norris

(2022). Psychological literacy and undergraduate psychology education: An international provocation. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.790600

16.

Cronbach

L. J.

Furby

(1970). How we should measure “change”: Or should we? Psychological Bulletin, 74(1), 68–80. https://doi.org/10.1037/h0029382

17.

Cruwys

Greenaway

K. H.

Haslam

S. A.

(2015). The stress of passing through an educational bottleneck: A longitudinal study of psychology honours students. Australian Psychologist, 50(5), 372–381. https://doi.org/10.1111/ap.12115

18.

Denovan

Macaskill

(2017). Stress and subjective well-being among first year UK undergraduate students. Journal of Happiness Studies, 18, 505–525. https://doi.org/10.1007/s10902-016-9736-y

19.

Dias Lopes

L. F.

Chaves

B. M.

Fabrício

Porto

Machado de Almeida

Obregon

S. L.

Pimentel Lima

Vieira da Silva

Camargo

M. E.

da Veiga

C. P.

de Moura

G. L.

Costa Vieira da Silva

L. S.

Flores Costa

V. M.

(2020). Analysis of well-being and anxiety among university students. International Journal of Environmental Research and Public Health, 17(11), 3874. https://doi.org/10.3390/ijerph17113874

20.

Diseth

Å.

(2007). Approaches to learning, course experience and examination grade among undergraduate psychology students: Testing of mediator effects and construct validity. Studies in Higher Education, 32(3), 373–388. https://doi.org/10.1080/03075070701346949

21.

Edgell

S. E.

Noon

S. M.

(1984). Effect of violation of normality on the t test of the correlation coefficient. Psychological Bulletin, 95(3), 576–583. https://doi.org/10.1037/0033-2909.95.3.576

22.

Entwistle

McCune

(2004). The conceptual bases of study strategy inventories. Educational Psychology Review, 16(4), 325–345. https://doi.org/10.1007/s10648-004-0003-0

23.

Entwistle

Ramsden

(2015). Understanding student learning. Routledge.

24.

Entwistle

Tait

McCune

(2000). Patterns of response to an approaches to studying inventory across contrasting groups and contexts. European Journal of Psychology of Education, 15, 33–48. https://doi.org/10.1007/BF03173165

25.

Faul

Erdfelder

Buchner

Lang

A.-G.

(2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

26.

Fryer

L. K.

(2017). Building bridges: Seeking structure and direction for higher education motivated learning strategy models. Educational Psychology Review, 29(2), 325–344. https://doi.org/10.1007/s10648-017-9405-7

27.

García-Jiménez

Trigo

M. E.

Varo

Aires-González

M. M.

Cano-García

F. J.

(2024). Confirmatory factor analysis and gender invariance of the Coping Strategies Inventory in academic university stress. Clínica y Salud, 35(1), 13–19. https://doi.org/10.5093/clysa2024a6

28.

Garip

Seneviratne

S. R.

Iacovou

(2020). Learners’ perceptions and experiences of studying psychology online. Journal of Computers in Education, 7(4), 553–573. https://doi.org/10.1007/s40692-020-00167-4

29.

Gogol

Brunner

Goetz

Martin

Ugen

Keller

Fischbach

Preckel

(2014). Academic Anxiety Scale [Database record]. APA PsycTests.

30.

Hauser-Cram

Krauss

M. W.

(1991). Measuring change in children and families. Journal of Early Intervention, 15(3), 288–297. https://doi.org/10.1177/105381519101500308

31.

Hayes

A. F.

(2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.

32.

Hayes

Luoma

Bond

Masuda

Lillis

(2006). Acceptance and commitment therapy: Model, processes, and outcomes. Behaviour Research and Therapy, 44(1), 1–25. https://doi.org/10.1016/j.brat.2005.06.006

33.

Hua

Wang

(2024). The impact of self-directed learning experience and course experience on learning satisfaction of university students in blended learning environments: The mediating role of deep and surface learning approach. Frontiers in Psychology, 14, 1278827. https://doi.org/10.3389/fpsyg.2023.1278827

34.

Hyllegard

Deng

Hunter

(2008). Why do students leave online courses? Attrition in community college distance learning courses. International Journal of Instructional Media, 35(4), 429–435. https://link.gale.com/apps/doc/A273359032/AONE?u=anon∼e5cc6436&sid=googleScholar&xid=95534531

35.

Kim

Jee

Lee

S. M.

(2018). Relationships between social support and student burnout: A meta-analytic approach. Stress and Health, 34(1), 127–134. https://doi.org/10.1002/smi.2771

36.

Kyndt

Dochy

Struyven

Cascallar

(2011). The direct and indirect effect of motivation for learning on students’ approaches to learning through the perceptions of workload and task complexity. Higher Education Research & Development, 30(2), 135–150. https://doi.org/10.1080/07294360.2010.501329

37.

Lake

Boyd

(2015). Is the university system in Australia producing deep thinkers? Australian Universities’ Review, 57(2), 54–59. https://doi.org/10.3316/informit.434636817984504

38.

Lobos

Cobo-Rendón

Mella-Norambuena

Maldonado-Trapp

Fernández Branada

Bruna Jofré

(2022). Expectations and experiences with online education during the COVID-19 pandemic in university students. Frontiers in Psychology, 12, https://doi.org/10.3389/fpsyg.2021.815564

39.

Malgady

R. G.

Colon-Malgady

(1991). Comparing the reliability of difference scores and residuals in analysis of covariance. Educational and Psychological Measurement, 51(4), 803–807. https://doi.org/10.1177/001316449105100401

40.

Maloshonok

Terentev

(2017). The mismatch between student educational expectations and realities: Prevalence, causes, and consequences. European Journal of Higher Education, 7(4), 356–372. https://doi.org/10.1080/21568235.2017.1348238

41.

Marton

Säljö

(1976). On qualitative differences in learning: I—Outcome and process. British Journal of Educational Psychology, 46(1), 4–11. https://doi.org/10.1111/j.2044-8279.1976.tb02980.x

42.

Mingoia

Skinner

Conboy

Engfors

Le Busque

(2025). From clicks to crisis: A systematic review of stressors faced by higher education students studying online. Future in Educational Research, 3(2), 239–258. https://doi.org/https://doi.org/10.1002/fer3.70001

43.

Murphy

D. J.

(2024). Psychology in the United Kingdom. In M. Bullock, M. Stevens, D. Wedding, & A. Clinton (Eds.), The handbook of international psychology (pp. 515–523). Routledge.

44.

Nadeem

N. H.

Al Falig

H. A.

(2020). Kahoot! Quizzes: A formative assessment tool to promote students’ self-regulated learning skills. Journal of Applied Linguistics and Language Research, 7(4), 1–20. https://www.jallr.com/index.php/JALLR/article/view/1117

45.

Osborne

J. W.

Overbay

(2004). The power of outliers (and why researchers should ALWAYS check for them). Practical Assessment, Research, and Evaluation, 9(6), https://doi.org/10.7275/qf69-7k43

46.

Oxford Learning College. (2024). Online education & e-learning statistics UK. https://www.oxfordcollege.ac/news/online-education-statistics/

47.

Prat-Sala

Redford

(2010). The interplay between motivation, self-efficacy, and approaches to studying. British Journal of Educational Psychology, 80(2), 283–305. Portico. https://doi.org/10.1348/000709909×480563

48.

Räihä

Katajavuori

Vehkalahti

Huotilainen

Asikainen

(2024). University students’ stress and burnout risk: Results of an ACT-based online course using self-assessments and HRV measurements. Current Psychology, 43(22), 20182–20195. https://doi.org/10.1007/s12144-024-05800-4

49.

Reid

M. P.

Ghose

S. M.

MacPherson

A. R.

Sabet

S. M.

Williams

C. M.

Dautovich

N. D.

(2024). Learning in the time of COVID: Undergraduate experiences of a mid-semester transition to virtual learning due to the COVID-19 pandemic. Teaching of Psychology, 51(1), 81–92. https://doi.org/10.1177/00986283221082987

50.

Reynolds

C. R.

Richmond

B. O.

Lowe

P. A.

(2003). The Adult Manifest Anxiety Scale– College version (AMAS-C). Western Psychological Services.

51.

Richardson

Abraham

Bond

(2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838

52.

Rowley

Hartley

Larkin

(2008). Learning from experience: The expectations and experiences of first-year undergraduate psychology students. Journal of Further and Higher Education, 32(4), 399–413. https://doi.org/10.1080/03098770802538129

53.

Shankar

Arora

Binz-Scharf

M. C.

(2023). Evidence on online higher education: The promise of COVID-19 pandemic data. Management and Labour Studies, 48(2), 242–249. https://doi.org/10.1177/0258042X211064783

54.

Shi

Gugiu

P. C.

Crowe

R. P.

Way

D. P.

(2018). A Rasch analysis validation of the Maslach Burnout Inventory–Student Survey with preclinical medical students. Teaching and Learning in Medicine, 31(2), 154–169. https://doi.org/10.1080/10401334.2018.1523010

55.

Singh

A. K.

Sengupta

A. S.

(2013). Manual for Academic Anxiety Scale for Children. National Psychological Corporation.

56.

Skinner

E. A.

(1995). Perceived control, motivation, & coping. Sage Publications, Inc.

57.

Slimmen

Timmermans

Mikolajczak-Degrauwe

Oenema

(2022). How stress-related factors affect mental wellbeing of university students: A cross-sectional study to explore the associations between stressors, perceived stress, and mental wellbeing. PLoS ONE, 17(11), e0275925. https://doi.org/10.1371/journal.pone.0275925

58.

Snyder

Brey

Dillow

(2018). Digest of education statistics 2018. https://nces.ed.gov/pubs2020/2020009.pdf

59.

Stallman

H. M.

Hurst

C. P.

(2016). The University Stress Scale: Measuring domains and extent of stress in university students. Australian Psychologist, 51(2), 128–134. https://doi.org/10.1111/ap.12127

60.

Syngene Research. (2019). Global e-learning market analysis 2019. https://www.researchandmarkets.com/reports/4769385/global-e-learning-market-analysis-2019

61.

Tabachnick

B. G.

Fidell

L. S.

(2007). Experimental designs using ANOVA. Thomson/Brooks/Cole.

62.

Tomlinson

Simpson

Killingback

(2023). Student expectations of teaching and learning when starting university: A systematic review. Journal of Further and Higher Education, 47(8), 1054–1073. https://doi.org/10.1080/0309877X.2023.2212242

63.

Valadas

S. T.

Almeida

L. S.

Araújo

A. M.

(2016). The mediating effects of approaches to learning on the academic success of first-year college students. Scandinavian Journal of Educational Research, 61(6), 721–734. https://doi.org/10.1080/00313831.2016.1188146

64.

Vanthournout

Donche

Gijbels

Van Petegem

(2013). (Dis) similarities in research on learning approaches and learning patterns. In Gijbels

Donche

Richardson

J. T. E.

Vermunt

J. D.

(Eds.), Learning patterns in higher education (pp. 11–32). Routledge.

65.

Wang

Cheung

Wong

Kwong

(2013). An exploration of Biggs’ constructive alignment in course design and its impact on students’ learning approaches. Assessment & Evaluation in Higher Education, 38(4), 477–491. https://doi.org/10.1080/02602938.2012.658018

66.

Winstone

Bretton

(2013). Strengthening the transition to university by confronting the expectation–reality gap in psychology undergraduates. Psychology Teaching Review, 19(2), 2–14. https://doi.org/10.53841/bpsptr.2013.19.2.2

67.

Winstone

N. E.

Hulme

J. A.

(2019). Duck to water’ or ‘fish out of water’? Diversity in the experience of negotiating the transition to university. Engaging Student Voices in Higher Education, 159–174. https://doi.org/10.1007/978-3-030-20824-0_10

68.

Yalçın

Dennen

V. P.

(2023). An investigation of the factors that influence online learners’ satisfaction with the learning experience. Education and Information Technologies, 29(4), 3807–3836. https://doi.org/10.1007/s10639-023-11984-2

69.

Yan

Pourdavood

R. G.

(2024). Faculty and student perspectives on online learning in higher education. Education Sciences, 14(8), 801. https://doi.org/10.3390/educsci14080801