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Article

The Mediating Role of Perceived Stress and Student Engagement for Student Teachers’ Intention to Drop Out of University in Germany: An Analysis Using the Study Demands–Resources Model Under Pandemic and Post-Pandemic Conditions

by
Edgar Hahn
1,*,
Dina Kuhlee
1,
Julia Zimmermann
2 and
Juan Serrano-Sánchez
2
1
Chair for Business Education, Faculty of Humanities, Otto-von-Guericke-Universität Magdeburg, 39106 Magdeburg, Germany
2
Department of Educational Psychology, Faculty of Psychology, FernUniversität in Hagen, 58097 Hagen, Germany
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(6), 719; https://doi.org/10.3390/educsci15060719
Submission received: 15 April 2025 / Revised: 23 May 2025 / Accepted: 5 June 2025 / Published: 8 June 2025
(This article belongs to the Special Issue Mind Matters: Exploring Mental Health and Well-Being in the Education System)
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Abstract

:
This article examines the interplay between study demands, institutional resources, and individual resources, specifically resilience, with the perceived stress, study engagement, and dropout intentions of student teachers using the Study Demands–Resources model. The aim is to describe the relevance of these variables in relation to student teachers’ intention to drop out of their studies as an indicator of student success. Further, we aim to explore whether the correlation structures can also be confirmed under different conditions, particularly in the context of the COVID-19 pandemic. To answer these questions, data collected under pandemic study conditions (NLA1 = 510) and post-pandemic study conditions (NLA2 = 433) are used and analysed by SEM. The results show that the Study Demands–Resources model is applicable in the two different contexts based on its validation in both study contexts. In line with the model, in both contexts, perceived stress and student engagement were significantly related to student teachers’ dropout intentions. Furthermore, study demands and resilience influenced perceived stress, which in turn affected dropout intentions, whereas institutional resources were associated with dropout intentions via student engagement. This article contextualises the findings within the existing research landscape. Based on the results, theoretical implications are discussed and approaches to reduce perceived stress in a sustainable manner to support student teachers and their academic success are described.

1. Introduction

A report by Stifterverband (2024) shows that in the academic years between 2019 and 2023, each year about 47,400 students enrolled in a university teacher education programme in Germany, but only about 27,800 student teachers actually graduated annually. This indicates an attrition rate of about 40%. Similar findings were reported by Güldener et al. (2020). This rate is significantly higher than the dropout rate estimated by Heublein et al. (2022), who reported dropout rates of around 20% for student teachers at Bachelor’s level and 16% at Master’s level in Germany. The lack of comprehensive data on student pathways in Germany generally complicates the discourse on dropout (Richter & Richter, 2024). However, the available data, albeit fragmentary, suggest that there is a significant discrepancy between the number of students enrolling in university teacher education programmes and the number of graduates (Richter & Richter, 2024). This raises the question of how study success in university teacher education programmes can be promoted (Stifterverband, 2024), particularly in light of the acute teacher shortage in Germany (Geis-Thöne, 2022; Klemm, 2022; KMK, 2023). In this context, perceived stress and student engagement play a crucial role, as they significantly influence both study success and dropout rates in higher education (Lei et al., 2018; Pascoe et al., 2020; Richardson et al., 2012; Truta et al., 2018). In particular, the impact of perceived stress on study success was intensively discussed during and after the pandemic (Becker & Brändle, 2022; Hahn et al., 2021a; Wagner et al., 2024; Weiss et al., 2022).
In order to analyse the interplay between perceived stress, student engagement, and student success in teacher education, the following article draws on the Study Demands–Resources model (SD–R model) (Bakker & Mostert, 2024; Lesener et al., 2020). The SD–R model is derived from the well-established Job Demands–Resources model (JD–R model) (Bakker et al., 2023; Demerouti et al., 2001). The latter is used in organisational psychology to examine how job demands and job resources affect health and work-related outcomes (well-being, job satisfaction, or turnover intentions). Against the background of the SD–R model, the article analyses the interplay between potential study demands, study resources, personal resources, perceived stress, student engagement, and dropout intentions of university student teachers (Bakker & Mostert, 2024; Lesener et al., 2020). This is achieved using cross-sectional data collected under pandemic and post-pandemic study conditions. The aim is to investigate the importance of institutional conditions (study requirements and study resources) and personal resources (resilience) for perceived stress, student engagement, and dropout intentions. Furthermore, it compares how these relationships differ between pandemic and post-pandemic conditions.
Accordingly, the following questions arise: (1) What are the relationships between perceived study demands, study resources, resilience, perceived stress, student engagement, and dropout intentions? (2) Are there differences in these relationships under pandemic and post-pandemic conditions? To answer these questions, data from two cross-sectional surveys collected under pandemic (NLA1 = 510) and post-pandemic (NLA2 = 433) study conditions will be analysed using structural equation modelling.

2. Dropout Intention, Perceived Stress, and Student Engagement

2.1. Study Demands–Resources Model: Constructs and Theoretical Framework

During the pandemic, academic success and dropout in higher education, as well as the relevant influencing factors, have been increasingly discussed (Becker & Brändle, 2022; Hahn et al., 2021b; Koopmann et al., 2024; Moussa & Ali, 2022; Vollmann et al., 2022). Study success as a multidimensional construct is operationalised in this article by the dropout intention (Hillebrecht, 2019; Weissenbacher et al., 2024). Dropout intention refers to students’ thoughts about quitting their studies or changing their course (Bean, 1982; Bohndick, 2020). Although dropout intention does not necessarily lead to actual dropout, it is associated with a higher likelihood of dropping out and is therefore considered a key predictor of dropout behaviour (Bäulke et al., 2022; Bean, 1982).
The SD–Rs model describes perceived stress as a crucial factor influencing academic success. According to Lazarus and Folkman (1984), stress arises when an individual is confronted with environmental demands (stressors) that exceed the available (individual) resources needed to cope with these demands. Consequently, the experience of stress is highly individual, as it is the cognitive appraisal of stressors against the background of available resources that determines whether a stressor is perceived as stressful (Folkman et al., 1986). Stress has been shown to be directly related to dropout intention (Sinval et al., 2025). Additionally, persistent high demands or insufficient recovery opportunities can lead to physical and psychological impairments such as burnout (Gusy et al., 2016). Burnout is a multidimensional construct with three key dimensions: exhaustion, reduced personal accomplishment, and depersonalisation (Maslach et al., 1996).
Another key determinant of academic success proposed by the SD–R model is student engagement. Schaufeli et al. (2002a, 2002b) define student engagement as a sustained, positive affective-cognitive state represented by the three dimensions of vitality, dedication, and absorption. Vitality describes high levels of energy, mental resilience, and the willingness to invest effort and persistence in studying despite difficulties (Schaufeli et al., 2002a, 2002b). Dedication is characterised by the perception of positive feelings such as enthusiasm and pride (Schaufeli et al., 2002a, 2002b). Absorption is characterised by a state in which students are completely immersed in their study-related activities, making it difficult to detach from studying (Gusy et al., 2019; Schaufeli et al., 2002b). Absorption can also be understood as a product of vitality and dedication (Gusy et al., 2019). Existing empirical research highlights the importance of student engagement for students’ satisfaction with their studies (Helm et al., 2025), academic performance (Bakker et al., 2015; Martínez et al., 2016), and dropout intentions (Kuhlee et al., 2025; Teuber et al., 2021).
In the following sections, the SD–R model will be applied to analyse the interplay between the constructs presented. The transfer of the JD–R model to a study programme is justified by structural similarities between studying and professional work (Bakker & Mostert, 2024; Gusy et al., 2016). Additionally, existing empirical research supports its applicability to higher education (Bakker & Mostert, 2024; Lesener et al., 2020). Like the JD–R model, the SD–R model is based on two assumptions.
The first assumption is that the study environment can be characterised by two categories: study demands and study resources (Bakker & Mostert, 2024; Bakker & Demerouti, 2007). Study demands refer to study-related factors that require effort as well as cognitive, physical, and emotional energy to overcome (Bakker & Mostert, 2024; Lesener et al., 2020). These include factors such as cognitive performance demands, study-life balance, workload, or time pressure (Bakker & Mostert, 2024; Lesener et al., 2020). In contrast, study resources refer to study-related factors that have a motivational effect, promote personal development, and help to alleviate study demands (Bakker & Mostert, 2024; Lesener et al., 2020). Learning and social support from lecturers, autonomy support, or development opportunities can be perceived as possible study resources (Bakker & Mostert, 2024).
The second assumption is that study demands and study resources initiate two parallel processes (Demerouti et al., 2001; Lesener et al., 2020). The health-impairment process suggests that study demands can favour the development of stress and related symptoms such as burnout, which negatively affects health- and study-related outcomes (e.g., well-being or intention to drop out) (Bakker et al., 2023; Demerouti et al., 2001; Lesener et al., 2020). In contrast, the motivational process posits that study resources enhance student engagement, which in turn positively influences health- and study-related outcomes (Bakker et al., 2023; Demerouti et al., 2001; Lesener et al., 2020).
Existing meta-analyses in the organisational psychology literature (Crawford et al., 2010; Nahrgang et al., 2011) also suggest that work resources are negatively associated with burnout and work demands are negatively associated with work engagement. In contrast, for students in higher education, studies suggest that study resources are negatively associated with student exhaustion, but study demands are not associated with student engagement (Gusy et al., 2016; Lesener et al., 2020; Niewöhner et al., 2021). However, for student teachers in Germany, Hartl et al. (2022) could not confirm that social support from lecturers (a study resource) helps to explain the variance in exhaustion.
In addition to study demands and study resources, personal resources are also considered by the SD–R model (Bakker et al., 2023; Bakker & Mostert, 2024; Xanthopoulou et al., 2007). Personal resources are negatively associated with burnout and stress and positively associated with student engagement (Bakker & Mostert, 2024). One personal resource that is commonly associated with the experience of stress is resilience. Resilience describes an individual’s ability to cope with internal or external stressors (Connor & Davidson, 2003). Previous research with students suggests the importance of resilience in the development of stress and burnout (Ghislieri et al., 2023; Hahn et al., 2024) as well as engagement (Ghislieri et al., 2023; Koob et al., 2021).
Overall, available empirical studies using the SD–R model confirm the assumptions regarding the health-impairment process and the motivational process (Gusy et al., 2016; Hahn et al., 2024; Helm et al., 2025; Kuhlee et al., 2025; Lesener et al., 2020). However, previous studies have mostly focused on health-related outcomes such as life satisfaction or student well-being (Gusy et al., 2016; Hahn et al., 2024; Lesener et al., 2020). In contrast, study-related outcomes such as student satisfaction (Helm et al., 2025) or dropout intentions (Kuhlee et al., 2025; Teuber et al., 2021) were less frequently considered. Similarly, the role of personal resources such as resilience, as a potential variable influencing students’ perceived stress or engagement, warrants further exploration. In particular, the relative importance of institutional versus personal resources for student stress and engagement under different contextual conditions, e.g., during and after the COVID-19 pandemic, provides an interesting avenue for further research.

2.2. Impact of the Pandemic on Study Conditions

In higher education, measures to combat the spread of COVID-19 led to far-reaching changes in teaching and learning. These included, in particular, the abrupt shift from face-to-face classes to online teaching, as well as modified regulations in Germany regarding the standard period of study and examination procedures and with regard to the possibility of repeating examinations (German Rectors’ Conference, 2022). These changed institutional contexts were mainly in force from the summer semester 2020 to the winter semester 2021/22 (German Rectors’ Conference, 2022). Due to Germany’s federal structure and the autonomy of higher education institutions, the specific implementation of these changes may have varied across different states (Länder) and universities.
Against the background of changing study conditions, the question of their impact on students’ academic success and health has become a central focus of public and academic discourse. A longitudinal study (January 2020, 2021, 2022, and 2023) by Schriek et al. (2024) of pre-service teachers in Germany shows that the intention to drop out increased significantly between pre-pandemic and post-pandemic study conditions. In addition, Schriek et al. (2024) report higher levels of emotional exhaustion among the group of student teachers in the pandemic and post-pandemic study conditions compared to the pre-pandemic conditions. The highest level of emotional exhaustion was reported during the lockdown in January 2021 (Schriek et al., 2024). In contrast, Capone et al. (2020) found no differences in perceived stress between pre-pandemic and pandemic study conditions among students in Italy. Salmela-Aro et al. (2022) compared three cross-sectional datasets (May 2020, December 2020, and April 2021) of Finnish students. Their results suggest that student burnout increased in this group under pandemic study conditions. In terms of student engagement, the May 2020 cohort showed higher levels than the December 2020 cohort, but engagement in April 2021 returned to a similar level as in April 2020 (Salmela-Aro et al., 2022). Thus, student engagement initially decreased under pandemic study conditions, but later stabilised at its original level in the cohort comparison (Salmela-Aro et al., 2022).
For students in the United States, Tasso et al. (2021) show that they retrospectively perceived a higher workload under pandemic conditions compared to pre-pandemic conditions. Students in the Netherlands retrospectively rated their work-study balance during the pandemic as worse than before (Hendriksen et al., 2021). In addition, the findings of Hendriksen et al. (2021) suggest that student-lecturer interactions were worse during the pandemic. For students in Sweden, Munir (2022) reports, based on qualitative interviews, that they experienced increased difficulties in interacting and communicating with both fellow students and lecturers under pandemic study conditions. At the same time, the findings suggest a greater perceived flexibility and autonomy in managing their own learning processes under pandemic conditions (Munir, 2022). Students in Germany reported a poorer study-life balance and increased perceived time pressure under pandemic study conditions compared to post-pandemic conditions (Hahn et al., 2024). However, no differences in performance demands were found (Hahn et al., 2024). Factors such as increased workload, time pressure, and lack of interaction with lecturers during the pandemic appear to have influenced the stress levels and mental health of students in Germany (Hahn et al., 2021a, 2024; Heumann et al., 2023).
Overall, existing research on students’ perceived stress, potential burnout, engagement, academic success, and perceived study demands and resources in the context of the pandemic presents a rather fragmented and inconsistent picture (Vollmann et al., 2022). In addition to some longitudinal studies, particularly cross-sectional data comparisons have increasingly been used to describe changes in single variables. In addition, many studies have relied on retrospective assessments of the constructs of interest, which may lead to biased results.

2.3. The Present Study

Against the background of theoretical considerations and existing research findings, it is striking that specific groups of students, such as student teachers, have received limited attention. The focus on student teachers is due to the structural characteristics of university teacher education as a cross-cutting university responsibility and the resulting conditions of these degree programmes (Blömeke, 2009). In view of the increasing shortage of teachers in various educational domains in Germany, the successful graduation of student teachers is also of core societal interest. Furthermore, most comparative analyses of pre-pandemic, pandemic, and post-pandemic conditions tend to focus on single constructs such as perceived stress, engagement, study demands, or study resources. A comparative examination of the interplay between study demands, study resources, personal resources, perceived stress, student engagement, and dropout intentions considering different institutional contexts as they existed in pandemic and post-pandemic periods has not yet been conducted. However, such analyses appear to be crucial for deriving targeted recommendations for measures to counteract potential dropout risks, even under different study conditions.
Based on the previous considerations and the SD–R model, the structural equation model (SEM) illustrated in Figure 1 is used to analyse the interplay among the relevant constructs, in accordance with the following hypotheses.
H1. 
Higher study demands are associated with higher perceived stress (H1a) and lower student engagement (H1b).
H2. 
Higher study resources are associated with higher student engagement (H2a) and lower perceived stress (H2b).
H3. 
Higher resilience is associated with lower perceived stress (H3a) and higher student engagement (H3b).
H4. 
Higher perceived stress is associated with higher dropout intentions.
H5. 
Higher student engagement is associated with lower dropout intentions.
H6. 
Higher study demands are associated with higher dropout intentions via higher stress (H6a) and lower student engagement (H6b).
H7. 
Higher study resources are associated with lower dropout intentions via lower stress (H7a) and higher student engagement (H7b).
H8. 
Higher resilience is associated with lower dropout intentions via lower stress (H8a) and higher student engagement (H8b).
Given these hypotheses, it should be investigated whether these associations differ between pandemic and post-pandemic conditions.

3. Materials and Methods

3.1. Sample

The data for the following analyses are based on two cross-sectional surveys conducted as part of the LeBeS project (‘Structural and individual study conditions and their relevance to perceptions of stress and study behaviours among students’) between February 2021 and early March 2021, and between February 2023 and mid-March 2023. As part of the project, students from different disciplines at universities and universities of applied sciences in Germany were surveyed via an online questionnaire using the SoSci Survey (Leiner, 2024). The survey covered aspects such as stress, institutional parameters, individual characteristics, and academic success. The following analyses are based on the subsample of students with a German higher education entrance qualification who were enrolled in a university teacher education programme at the time of the survey.
The data collection in February and early March 2021 took place during the second lockdown of the COVID-19 pandemic in Germany, which began in December 2020, with the first relaxation of restrictions in March 2021 (Federal Government, 2020, 2021a, 2021b, 2021c). A total of N = 983 completed surveys were collected during this period. Of these, 510 students could be classified as domestic student teachers for vocational schools (n = 323, 63.33%) or for general education schools (n = 187, 36.67%). In this sample, n = 383 (75.10%) student teachers were female, n = 124 (24.31%) were male, and n = 3 (0.59%) identified as non-binary. The mean age was 26.12 years (SD = 5.79). A total of n = 242 (47.45%) students were enrolled in a Bachelor’s programme, n = 192 (38.23%) in a Master’s programme, and n = 72 (14.12%) in a programme leading to a state examination (Staatsexamen). A migration background1 was reported by n = 56 (10.98%) students, and n = 288 (56.47%) were first-generation students.
The second dataset was collected from February to mid-March 2023. A total of N = 2084 students completed the survey. Of these, n = 433 students could be classified as domestic student teachers for vocational schools (n = 134, 30.95%) or general education schools (n = 299, 69.05%). Among the sample, n = 311 (71.82%) were female, n = 116 (26.79%) were male, and n = 6 (1.39%) identified as non-binary. A total of n = 83 (19.17%) students were enrolled in a Bachelor’s programme, n = 89 (20.55%) in a Master’s programme, and n = 261 (60.28%) in a programme leading to a state examination (Staatsexamen). The mean age was 24.48 years (SD = 5.55). A migration background was indicated by n = 31 (7.16%) of the students, n = 242 (51.73%) were first-generation students, n = 203 (46.88%) were non-first-generation students, and n = 6 (1.39%) students responded that they did not know the educational background of their parents.

3.2. Measures

To test the hypotheses, the relevant constructs were assessed using the same instruments under pandemic and post-pandemic study conditions. To operationalise the constructs, validated instruments from previous studies based on the SD–R model were used (Gusy et al., 2016; Kuhlee et al., 2025; Lesener et al., 2020).
To operationalise the latent construct of perceived study demands, three scales—performance demands, study-life balance, and time pressure—were used. All items were measured using a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree). The performance demands scale (adapted from Hillebrecht, 2019) consists of three items that assess the cognitive evaluation of the level of demands within the study programme (example item: “I am fine with the level of the academic requirements.”). Study-life balance was measured using an adapted scale from Hillebrecht (2019), which consists of five items assessing the extent to which studying is manageable in terms of organisation and time, as well as its compatibility with private life (example item: “My studies can be organised at short notice (for example, within the current academic term) and can be easily combined with my private life.”). According to Gusy et al. (2016) and Gusy and Lohmann (2011), perceived time pressure describes whether time bottlenecks occur when completing study-related tasks and was measured with three items (example item: “I don’t have enough time to prepare for and follow up on the lectures and seminars I attend.”).
The latent construct of perceived study resources is operationalised through three scales—decision-making autonomy, lecturer support, and student support—which are measured on a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree). The decision-making autonomy scale consists of four items, adapted from Grützmacher et al. (2018), which assess the extent to which students can shape their course of study as well as decision-making autonomy in study-related tasks, such as projects or essays (example item: “I can decide for myself how to proceed with my academic coursework (e.g., projects, papers).”). The lecturer support scale (Gusy et al., 2016; Gusy & Lohmann, 2011) consists of three items and describes the support and advice provided by lecturers on study-related issues (example item: “My lecturers advise me on problems related to my studies.”). Finally, the student support scale (Gusy et al., 2016; Gusy & Lohmann, 2011) consists of three items and assesses interactions with fellow students regarding organisational and study-related matters (example item: “If I would like to discuss questions related to my studies I find fellow students and they give me their time and listen to me carefully.”).
Resilience was measured on a 6-point Likert scale (1 = strongly disagree to 6 = strongly agree) using the short version (ten items) of the German Connor-Davidson Resilience Scale (CD-RISC, Connor & Davidson, 2003; Sarubin et al., 2015) (example item: “I can deal with whatever comes my way.”).
The Perceived Stress Questionnaire (PSQ) by Fliege et al. (2001) was used to assess perceived stress, which refers to an individual’s subjective experience of stress based on appraisals of cognitive and emotional stressors in their environment. The PSQ consists of four dimensions: tension, demands, worry, and joy. Each dimension was assessed using five items rated on a 4-point Likert scale (1 = hardly ever, 2 = sometimes, 3 = often, and 4 = most of the time) (example items for each dimension: “I feel tense.” (tension); “I have enough time for myself.” (demands); “I feel frustrated.” (worry); “I am full of energy.” (joy)).
Student engagement was measured using a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree) with five items from the student version of the Utrecht Work Engagement Scale by Schaufeli and Bakker (2003). This assesses a sustained positive affective-motivational state while studying (example item: “I can get carried away by my studies.”).
Ditton’s (1998) scale was used to measure the dropout intention of university students. The three items assess the hypothetical change of course or intention to drop out of university and were measured on a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree) (example item: “I’ve often thought about dropping out.”).

3.3. Analyses

Descriptive data analysis was performed using SPSS version 30 (IBM Corp., 2024). SEMs were estimated using Mplus version 8.10 (Muthén & Muthén, 1998–2017) with ML estimators (standard maximum likelihood estimator) and bootstrapping procedures (boot = 1000). For this, first-order measurement models were modelled for the unidimensional latent constructs of resilience, student engagement, and dropout intention. Second-order models were modelled for the multidimensional constructs of study demands, study resources, and perceived stress. Item parcelling was used to reduce the complexity of the measurement models for the latent constructs of resilience, tension, demand, worry, joy, and student engagement. The single-factor method (Landis et al., 2000) was used to determine the parcels. Accordingly, as a first step, a confirmatory factor analysis was conducted for each dataset and for each of the latent constructs of resilience, strain, demand, worry, enjoyment, and student engagement. The next step was to create item parcels based on the factor loadings. In order to obtain balanced parcels, items were assigned according to the item-to-construct method (Little et al., 2002). Within the SEM, the measurement model of resilience was represented by three parcels, while the measurement models of tension, demands, worry, joy, and student engagement were each represented by two parcels. Common goodness-of-fit indices such as CFI, TLI, SRMR, and RMSEA were used to assess the quality of the models. For CFI and TLI values > 0.90 and >0.95, respectively, and for SRMR and RMSEA values < 0.08 and <0.05, respectively, indicate an acceptable or good model fit (Hu & Bentler, 1999). The interpretation of explained variance is based on Cohen (1988).

4. Results

4.1. Descriptive Results

The descriptive results of the metric scales are presented in Table 1. The bivariate correlations between the scales tend to be similar in both datasets. For example, study demands are positively associated with perceived stress and dropout intention and negatively associated with student engagement. In contrast, study resources and resilience are both positively correlated with student engagement and negatively correlated with perceived stress and dropout intention.

4.2. Results of the Structural Equation Models

The theoretical considerations of the SD–R model are applicable to university student teachers under pandemic study conditions. Overall, the model shows acceptable to good fit indices [CFI = 0.911, TLI = 0.903, SRMR = 0.058, RMSEA = 0.052, 90% CI for RMSEA (0.049, 0.056)]. The explained variance is 57.8% for perceived stress, 41.0% for student engagement, and 33.6% for dropout intention, indicating high effect sizes according to Cohen (1988).
With regard to the result presented in Figure 2 for the pandemic study conditions, the SEM can confirm that the health-impairment process aligns with hypothesis H1a, according to which high study demands are associated with high perceived stress (β = 0.67; p < 0.001). In addition, high perceived stress is associated with a high dropout intention (H3) (β = 0.17; p < 0.001). The indirect effect of study demands on dropout intentions via perceived stress (H6a) was significant (b = 0.11; p = 0.001). However, contrary to hypothesis H2b, study resources are not related to perceived stress (β = −0.01; p = 0.971). The hypothesised motivational process can be verified (H2a). According to this, high study resources are associated with high student engagement (β = 0.62; p < 0.001). Furthermore, high student engagement is associated with low dropout intentions (H5) (β = −0.49; p < 0.001). The indirect effect of study resources on dropout intentions via student engagement (H7b) was significant (b = −0.31, p = 0.001). In contrast, student engagement is not significantly related to study demands (H1b) (β = −0.02; p = 0.838). With regard to resilience, the results show that under pandemic study conditions, resilience is negatively associated with perceived stress (H3a) (β = −0.20; p < 0.001), but not significantly related to student engagement (H3b) (β = 0.07; p = 0.304). In addition, only the indirect effect of resilience on dropout intentions via perceived stress (H8a) was significant (b = −0.03, p = 0.022), whereas the indirect effect via student engagement (H8b) was not significant (b = −0.03, p = 0.317).
The estimated SEM is also applicable to student teachers under post-pandemic study conditions and shows acceptable to good fit indices [CFI = 0.900, TLI = 0.891, SRMR = 0.066, RMSEA = 0.056, 90% CI for RMSEA (0.052, 0.060)]. The explained variances are 75.2% for perceived stress, 38.2% for student engagement, and 35.9% for dropout intentions. Thus, the model explains a high degree of variance in the constructs analysed (Cohen, 1988).
The results under the post-pandemic study conditions can be summarised as follows: The health-impairment process (H1a) can be verified, indicating that high study demands are associated with high perceived stress (β = 0.63; p < 0.001). Additionally, high perceived stress is associated with high dropout intentions (H3) (β = 0.17; p < 0.001). The indirect effect of study demands on dropout intentions via perceived stress (H6a) was significant (b = 0.23; p = 0.001). Even under post-pandemic study conditions, the hypothesised association between study resources and perceived stress could not be confirmed (H2b) (β = −0.08; p = 0.257). Regarding the motivational process (H2a), the results show that high study resources are related to high student engagement (β = 0.39; p < 0.001). Furthermore, high student engagement is associated with low dropout intentions (H5) (β = −0.42; p < 0.001). The indirect effect of study resources on dropout intentions via student engagement (H7b) was significant (b = −0.19; p = 0.001). As before, study demands are not significantly related to student engagement (H1b) (β = −0.14; p = 0.103). With respect to resilience, the results under post-pandemic study conditions indicate that it is negatively associated with perceived stress (H3a) (β = −0.23; p < 0.001) and positively associated with student engagement (H3b) (β = 0.14; p = 0.03). The indirect effects of resilience on dropout intentions via perceived stress (H8a) (b = −0.07; p = 0.001) and student engagement (H8b) (b = −0.06; p = 0.045) were significant.

5. Discussion

The results presented are subject to certain limitations. The data are based on two cross-sectional surveys conducted during and shortly after the pandemic, which precludes causal inference. Integrating pre-pandemic data or further measurements with a greater time distance from the pandemic could provide an even more comprehensive picture. In addition, the data are based on self-reports by students, which means that response bias due to social desirability cannot be fully ruled out. It would therefore be valuable to validate students’ subjective perceptions with additional objective indicators of study demands, such as the number of exams, or study resources, such as the frequency of interactions with lecturers. In addition, a more nuanced analysis of students’ dropout intentions—distinguishing between intentions to change their major and to completely leave higher education (dropout)—would be valuable (Bäulke et al., 2022). Nevertheless, the findings provide valuable new insights into the interplay between study demands, study resources, resilience, perceived stress, student engagement, and dropout intentions.
Overall, the results presented support the basic assumptions of the SD–R model regarding the interplay of the constructs of interest for student teachers under both pandemic and post-pandemic study conditions. Consistent with the findings of Hartl et al. (2022) for student teachers, the results indicate no significant relationship between study resources and perceived stress. However, a significant association was found between study demands and perceived stress, with the association being stronger under post-pandemic study conditions than during the pandemic. The same pattern applies to the explained variance of perceived stress, which was higher under post-pandemic study conditions than under pandemic study conditions. In addition, the association between perceived stress and the intention to drop out of university appears to be lower during the pandemic than in the post-pandemic period. A possible explanation for the stronger association under post-pandemic study conditions could be the relaxed regulations in Germany during the pandemic, especially regarding the standard period of study and examination procedures (German Rectors’ Conference, 2022). These measures may have reduced the role of perceived stress in the dropout process, thereby influencing the strength of the association between perceived stress and students’ intention to drop out. However, it remains unclear whether such changes in institutional conditions will ultimately prevent students from dropping out or merely delay the process.
Resilience was found to be similarly associated with perceived stress in both pandemic and post-pandemic study conditions. Strengthening resilience may therefore be an effective approach to reducing stress, regardless of the specific study context. Strengthening the resilience of student teachers appears to be beneficial beyond their studies, as teaching is an extremely demanding profession (Viac & Fraser, 2020) associated with high levels of stress and emotional exhaustion (Blossfeld et al., 2014; Cramer et al., 2014). Students should therefore build and strengthen their resilience during their studies. This reduces the intention to drop out of university and prepares them for their future teaching careers. Furthermore, it provides them with the necessary skills to navigate disruptive events (such as a pandemic) that may lead to changes in institutional contexts. Interventions aimed at strengthening resilience can play a key role in coping with stress in the university context (Hofmann et al., 2020; Houston et al., 2017). Accordingly, the use of previously evaluated resilience interventions, such as the Resilience and Coping Intervention (RCI) (Houston et al., 2017), could be an effective approach in higher education.
It is important to note that resilience under post-pandemic study conditions is also significantly associated with student engagement. Furthermore, study resources are associated with student engagement in both pandemic and post-pandemic study conditions. However, in line with existing findings (Gusy et al., 2016; Lesener et al., 2020; Niewöhner et al., 2021), there is no significant association between study demands and student engagement under either pandemic or post-pandemic study conditions. Nevertheless, student engagement remains strongly associated with dropout intentions in both pandemic and post-pandemic study conditions. It appears to be a crucial factor associated with dropout intentions, as this association persists despite changes in institutional conditions. Strengthening student engagement, therefore, seems to be an effective approach to counteract dropout in different contexts. For instance, students with low levels of engagement could receive targeted support in individual cognitive-behavioural training sessions to help them cope with challenging situations (Bresó et al., 2011). The results of Bresó et al. (2011) show that such interventions can be effective and not only increase engagement, but also increase self-efficacy and well-being and reduce burnout.

6. Conclusions

Despite the limitations mentioned, the findings suggest that the integration of personal resources into the SD–R model—alongside resilience, for example, self-efficacy or optimism—could be a valuable addition to understanding the interplay between stress, engagement, and academic success or dropout. The present findings highlight the particular importance of personal resources in this dynamic and their indirect effect on academic success. Such integration could provide deeper insights into these relationships and facilitate the development of more effective interventions. Future research should focus more on longitudinal study designs to explore causal relationships between variables.

Author Contributions

Conceptualization, E.H. and D.K.; methodology, E.H., D.K. and J.Z.; formal analysis, E.H.; data curation, E.H., D.K. and J.Z.; writing—original draft preparation, E.H. and D.K.; writing—review and editing, E.H., D.K., J.Z. and J.S.-S.; visualization, E.H. and J.S.-S.; supervision, D.K.; project administration, E.H. and D.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The APC was funded by Otto-von-Guericke University Magdeburg.

Institutional Review Board Statement

Participants were treated in accordance with the guidelines of the German Research Foundation (DFG). As the study was non-medical, no explicit approval was required from the responsible ethics committee at Otto-von-Guericke University Magdeburg. All students gave their consent and could withdraw from the study at any time.

Informed Consent Statement

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

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. This study’s design and its analysis were not preregistered.

Conflicts of Interest

The authors declare no conflicts of interest.

Note

1
A migration background (first or second generation) is indicated if the student, or at least one of the parents, was born abroad.

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Figure 1. Hypothesised structural equation model (SEM) of the SD–R model.
Figure 1. Hypothesised structural equation model (SEM) of the SD–R model.
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Figure 2. Structural equation model (SEM) of the SD–R model with standardised path coefficients: on the right-hand side, coefficients for the 2021 dataset (N = 510); on the left-hand side, coefficients for the 2023 dataset (N = 433). * p < 0.05, and *** p < 0.001.
Figure 2. Structural equation model (SEM) of the SD–R model with standardised path coefficients: on the right-hand side, coefficients for the 2021 dataset (N = 510); on the left-hand side, coefficients for the 2023 dataset (N = 433). * p < 0.05, and *** p < 0.001.
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Table 1. Means, standard deviations, Cronbach’s alphas, and bivariate correlations in each of the datasets.
Table 1. Means, standard deviations, Cronbach’s alphas, and bivariate correlations in each of the datasets.
Scales Bivariate Correlations
Dataset (2021, NLA1 = 510)M (SD)α(1)(2)(3)(4)(5)(6)(7)(8)(9)
(1)
performance demands
2.18 (0.62)0.82
(2)
study-life balance
2.48 (0.65)0.820.53 ***
(3)
time pressure
2.54 (0.74)0.690.46 ***0.59 ***
(4)
decision-making autonomy
2.53 (0.62)0.70−0.32 ***−0.30 ***−0.25 ***
(5)
student support
2.92 (0.86)0.83−0.12 **−0.17 ***−0.080.16 ***
(6)
lecturer support
2.81 (0.71)0.82−0.28 ***−0.30 ***−0.19 ***0.42 ***0.25 ***
(7)
resilience
4.12 (0.72)0.82−0.31 ***−0.25 ***−0.23 ***0.25 ***0.16 ***0.17 ***
(8)
perceived stress 1
2.60 (0.61)0.930.39 ***0.59 ***0.53 ***−0.28 ***−0.14 **−0.27 ***−0.38 ***
(9)
student engagement
2.49 (0.68)0.90−0.34 ***−0.29 ***−0.21 ***0.43 ***0.23 ***0.34 ***0.26 ***−0.37 ***
(10)
dropout intention
1.51 (0.62)0.620.24 ***0.14 ***0.16 ***−0.20 ***−0.20 ***−0.22 ***−0.28 ***0.27 ***−0.43 ***
Dataset (2023, NLA2 = 433)M (SD)α(11)(12)(13)(14)(15)(16)(17)(18)(19)
(11)
performance demands
2.23 (0.59)0.79
(12)
study-life balance
2.40 (0.65)0.850.52 ***
(13)
time pressure
2.53 (0.68)0.630.43 ***0.62 ***
(14)
decision-making autonomy
2.62 (0.56)0.66−0.33 ***−0.33 ***−0.16 ***
(15)
student support
3.00 (0.76)0.78−0.21 ***−0.19 ***−0.090.12 *
(16)
lecturer support
2.99 (0.68)0.84−0.32 ***−0.21 ***−0.16 ***0.42 ***0.23 ***
(17)
resilience
4.04 (0.84)0.88−0.31 ***−0.32 ***−0.28 ***0.17 ***0.22 ***0.19 ***
(18)
perceived stress 1
2.58 (0.58)0.930.49 ***0.61 ***0.57 ***−0.25 ***−0.15 **−0.21 ***−0.50 ***
(19)
student engagement
2.46 (0.70)0.91−0.41 ***−0.33 ***−0.21 ***0.38 ***0.24 ***0.29 ***0.32 ***−0.38 ***
(20)
dropout intention
1.66 (0.67)0.570.30 ***0.25 ***0.21 ***−0.09−0.23 ***−0.08−0.31 ***0.31 ***−0.42 ***
1 Note: PSQ dataset (2021): transformed M = 0.53 (SD = 0.20); PSQ dataset (2023): transformed M = 0.53 (SD = 0.19). * p < 0.05, ** p < 0.01, and *** p < 0.001.
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Hahn, E.; Kuhlee, D.; Zimmermann, J.; Serrano-Sánchez, J. The Mediating Role of Perceived Stress and Student Engagement for Student Teachers’ Intention to Drop Out of University in Germany: An Analysis Using the Study Demands–Resources Model Under Pandemic and Post-Pandemic Conditions. Educ. Sci. 2025, 15, 719. https://doi.org/10.3390/educsci15060719

AMA Style

Hahn E, Kuhlee D, Zimmermann J, Serrano-Sánchez J. The Mediating Role of Perceived Stress and Student Engagement for Student Teachers’ Intention to Drop Out of University in Germany: An Analysis Using the Study Demands–Resources Model Under Pandemic and Post-Pandemic Conditions. Education Sciences. 2025; 15(6):719. https://doi.org/10.3390/educsci15060719

Chicago/Turabian Style

Hahn, Edgar, Dina Kuhlee, Julia Zimmermann, and Juan Serrano-Sánchez. 2025. "The Mediating Role of Perceived Stress and Student Engagement for Student Teachers’ Intention to Drop Out of University in Germany: An Analysis Using the Study Demands–Resources Model Under Pandemic and Post-Pandemic Conditions" Education Sciences 15, no. 6: 719. https://doi.org/10.3390/educsci15060719

APA Style

Hahn, E., Kuhlee, D., Zimmermann, J., & Serrano-Sánchez, J. (2025). The Mediating Role of Perceived Stress and Student Engagement for Student Teachers’ Intention to Drop Out of University in Germany: An Analysis Using the Study Demands–Resources Model Under Pandemic and Post-Pandemic Conditions. Education Sciences, 15(6), 719. https://doi.org/10.3390/educsci15060719

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