# Exploring the Joint Effects of Stressors and Resources on Student Engagement in Teacher Education: A French Study

Fernando Núñez-Regueiro, Nadia Leroy

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Fernando Núñez-Regueiro, Nadia Leroy. Exploring the Joint Effects of Stressors and Resources on Student Engagement in Teacher Education: A French Study. Teaching Education, 2023, 35 (3), pp.332-351. 10.1080/10476210.2023.2286356. hal-04086576

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Exploring the Joint Effects of Stressors and Resources on Student Engagement in Teacher

Education: A French Study

Fernando Núñez-Regueiroª, Nadia Leroyª

ªUniversité Grenoble Alpes

**Author Note**

Article accepted for publication in *Teaching Education*.

Corresponding author: fernando.nunez-regueiro@univ-grenoble-alpes.fr
Abstract

Evidence points to the existence of several stressors and resources that contribute to students' engagement in teacher education, but more research is needed to integrate both kinds of factors within a common analytical framework. This study proposes such integration by testing a stress process model, in which stressors and resources combine with each other to predict preservice teachers' engagement in their training. Using data from a sample of French preservice teachers (N = 340 French preservice teachers), results from structural equation models of student engagement (45.9% of variance explained) provide evidence for moderation and mediation effects characteristic of stress processes, notably between academic stressors (study workload, poor quality courses), academic resources (college self-efficacy, support from teacher educators) and vocational resources (intrinsic career motive). Implications are drawn on ways to counteract the negative impact of stressors in teacher education, and on ways to foster more integrative research on the stress processes that undergird preservice teachers' engagement processes.

Keywords: preservice teachers; student engagement; stressors; resources; structural equation modeling
# Exploring the Joint Effects of Stressors and Resources on Student Engagement in Teacher Education: A French Study

With over 90% of youth schooled for an average of 14 years according to international data (OECD, 2021), teachers play a crucial role in shaping future generations. Yet, attrition rates in the profession remain high, especially among beginning teachers. For example, reviews have estimated attrition rates among teachers with less than 5 years experience to be between 15% and 50% in many industrialized countries (Viac & Fraser, 2020), and data from the Teaching and Learning International Survey (TALIS) show that at least 21% of them intend to leave the profession in the next 5 years (OECD, 2020). This problematic attrition is accompanied by difficulties in recruitment processes, leading to shortages of qualified teachers (e.g., in over 13 European countries; Viac & Fraser, 2020). In France, where the current study took place, institutional reports have pointed to declining ratios of teacher candidates to teacher positions available since the late 1990s, and over 13% of newly created teaching positions have not been provided for between 2012 and 2015 (CNESCO, 2016).

The above observations point to the need to identify strategies to improve recruitments in the profession. Much research has been conducted on tackling the attrition problem among beginning teachers by fostering induction processes (e.g., collaborative workshops, orientation sessions, meetings with supervisors, mentorships), or by hiring beginning teachers on a full-time basis while enabling a reduced teaching load (Ingersoll & Strong, 2011; OECD, 2019). By contrast, more research is needed to understand what can be done to improve the quality of teacher education programs. Studies have indeed provided insights on the academic dimensions (e.g., stressors associated with courses and evaluations, or resources related to interest in the subjects, learning strategies, college self-efficacy, support from peers and teachers; Clarke et al.,
2012; Deasy et al., 2016; Guilbert et al., 2016; Hobson et al., 2009; Kim & Corcoran, 2018; Lee
& Turner, 2018; Löfström et al., 2010; Núñez-Regueiro et al., 2023) and vocational dimensions
(e.g., stressors such as student misbehavior or teaching workload, and resources such as teaching
self-efficacy, teaching motivations; (Dicke et al., 2014; D'Rozario & Wong, 2013; Hockley &
Hemmings, 2001; Hong, 2010; Klassen et al., 2013; Núñez-Regueiro, Escriva-Boulley, et al.,
2023; Núñez-Regueiro, et al., 2023; Núñez-Regueiro & Leroy, 2022; Paquette & Rieg, 2016;
Valenzuela et al., 2019) that undergird engagement in teacher education, but without providing a
formal integration of these factors. Such integration can shed light on the challenges and
opportunities that preservice teachers face during their training, in a view to ameliorate their
recruitment and commitment to the profession. Drawing from stress theories, the present article
responds to this research need by reviewing the evidence on the stressors and resources
associated with student engagement and, more specifically, on the kinds of stress processes that
arise from their combination. In an empirical section, we extend this evidence by exploring the
joint effects of academic stressors, academic resources, and vocational resources on indicators of
engagement during the first year of teacher education (N = 340 French preservice teachers).

Engaging in Teacher Education: A Stress Process Approach Based on Academic and
Vocational Factors

Engagement in teacher education can be defined as the motivational process by which preservice teachers identify with and commit to their training, in the hope of graduating from teacher education (Kim & Corcoran, 2018; Roberts, 2012). Moreover, teacher education can be defined as the training process by which *college students* become *teaching professionals*. This ambivalent status has produced separate areas of research on the academic and vocational factors that determine preservice teachers’ engagement processes. The integration of these approaches is
yet to be developed (Roberts, 2012). By explicitly articulating environmental and personal
factors to explain individual behavior, notably as factors of stress (i.e., stressors) or motivation
(i.e., resources), the theories of stress offer a powerful integrative framework to this end (Dupéré
et al., 2015; Núñez-Regueiro, 2017, 2018; Núñez-Regueiro, Archambault, et al., 2022; Núñez-
Regueiro et al., 2021; Núñez-Regueiro, Jamain, et al., 2022; Núñez-Regueiro & Núñez-Regueiro,
2021; Pearlin & Bierman, 2013; Salmela-Aro et al., 2022; Wheaton et al., 2013). As explained
hereafter (see also Figure 1), research in the field points to the existence of multiple stressors and
resources in the academic and vocational domains of teacher education that, when combined
(e.g., as additive, moderation and/or mediation effects), can contribute to explain individual
differences in student engagement.

Vocational Stressors and Resources: Preservice Teachers as Future Professionals

The vocational factors of engagement in teacher education have been the focus of
research based upon theories of teacher stress (Dicke et al., 2014; Klassen et al., 2013) and
teaching career motives and values (Hong, 2010; Valenzuela et al., 2019; Watt & Richardson,
2007). These theories are ‘vocational’ in nature because they mainly characterize preservice
teachers as future professionals, notably during the practical phase of teacher education when
candidates are offered the opportunity to practice their teaching skills (i.e., the *practicum*), or
during the academic phase of learning in teacher education (i.e., the *academia*). In this research
(see right panel of Figure 1), vocational stressors and resources have offered a unifying
framework for analyzing teacher attrition or resilience, notably among beginning teachers (e.g.,
Fuller, 1969; LeCompte & Dworkin, 1991; Mansfield et al., 2016).

Two studies have analyzed the incidence of vocational stressors on preservice teachers'
engagement during the *practicum* (Dicke et al., 2014; Klassen et al., 2013). Their results showed
that cumulating vocational stressors in multiple areas (e.g., student misbehavior, lack of
recognition in the career, time or resource constraints, poor work relationships) was negatively
associated with commitment to teaching and positively related to emotional exhaustion, thus
suggesting a deleterious impact of such stressors on engagement. Complementary studies further
showed that the strongest vocational stressors reported by preservice teachers related to
managing the teaching workload, student misbehavior and being evaluated as a teacher by
teacher educators (D'Rozario & Wong, 2013; Hockley & Hemmings, 2001; Paquette & Rieg,
2016). Although vocational stressors are numerous, Figure 1 retains the latter three stressors as
major contributors to vocational stress processes in teacher education.

Complementarily, research suggests that vocational resources specific to teaching
education include teaching self-efficacy, social support, teaching values and teaching motives
(Figure 1; for a review, see Mansfield et al., 2016). These resources may act during the academic
phase or the practical phase of teacher education. On a personal level, teaching self-efficacy was
found to protect against emotional exhaustion and to increase commitment to teaching among
preservice teachers (Dicke et al., 2014; Klassen et al., 2013). A meta-analysis also found that
teaching self-efficacy and commitment to teaching were positively related (i.e., r = .300,
p < .001; Chesnut & Burley, 2015). Preservice teachers with strong confidence in their teaching
abilities are therefore inclined to be more adapted to their training. Moreover, preservice teachers
who value teaching (i.e., take pleasure in teaching, find social benefits in teaching; Hong, 2010;
Valenzuela et al., 2019) or who have multiple motives to enter the teaching career (i.e., intrinsic
or extrinsic motives; Thomson et al., 2012; Valenzuela et al., 2019; Watt & Richardson, 2007)
have been found to be more engaged in their training, more committed to the profession and less
likely to dropout from teacher education, thus indicating that a strong calling for the job protects
against vocational stress. According to qualitative data, these teaching values or motivations are
important resources to cope with the academic phase of teacher education, when students are
confronted with multiple academic stressors that challenge their career determination (Núñez-
Regueiro, Escriva-Boulley, et al., 2023). On a interpersonal level, a few studies also reported that
student teachers viewed social support from supervisors or school mentors (e.g., teaching advice,
emotional and organizational support) as a valuable coping resource that helped them persevere
in their training (Paquette & Rieg, 2016; Stanulis et al., 2018). Complementary quantitative
research is needed to test the impact of social support on student teachers' engagement, but
theories of stress (Pearlin & Bierman, 2013; Wheaton et al., 2013) and teacher stress (Dicke et al.,
2014; LeCompte & Dworkin, 1991) indeed support the relevance of this factor as a coping
resource (Figure 1).

Academic Stressors and Resources: Preservice Teachers as College Students

Research on the academic factors of teacher education considers preservice teachers
primarily as college students (see left panel of Figure 1). In this approach, studies have benefited
from complementary theoretical insights from models focusing on the environmental factors that
contribute to college students’ retention in the program (e.g., college organization, peer
environment; Kim & Corcoran, 2018; Roberts, 2012); and models of student motivation focusing,
instead, on the intermediate personal factors that moderate or mediate the link between
contextual factors and students’ engagement and performance (e.g., academic self-regulation,
self-efficacy, or self-determined motivation; Guilbert et al., 2016; Lee & Turner, 2018; Löfström
et al., 2010). This research is therefore relevant to identify and organize the academic
determinants of engagement in teacher education, notably academic stressors and resources.
A few studies have explored the academic stressors associated with teacher education using qualitative data from interviews (Clarke et al., 2012; Deasy et al., 2016), checklists of reasons for withdrawal from teacher education (Hobson et al., 2009) and autobiographical texts on experiences of teacher education (Núñez-Regueiro et al., 2023). The available evidence points to the existence of three major academic sources of stress (see Figure 1), namely the ‘study workload’ associated with evaluations (written essays, examinations) and study assignments (Clarke et al., 2012; Deasy et al., 2016; Hobson et al., 2009; Núñez-Regueiro, Escriva-Boulley, et al., 2023; Núñez-Regueiro & Leroy, 2022); the ‘poor quality of courses’ in the form of lectures perceived as irrelevant or inappropriate for acquiring good teaching practices (Clarke et al., 2012; Deasy et al., 2016; Núñez-Regueiro, Escriva-Boulley, et al., 2023); and, finally, the ‘competitive study climate’ created by the teacher recruitment context (e.g., competitive examination in France; Núñez-Regueiro et al., 2023). Although informative with regards to dimensions of academic stress, these qualitative studies provided little information on how these academic stressors related to student behavior, so that complementary research is needed to understand their association with student engagement.

Concerning academic resources, the available evidence is also quite slim albeit more diversified in methods. For example, two predictive studies found that being intrinsically motivated by teacher education (i.e., being inherently interested by the content of studies) was significantly associated with more emotional engagement (i.e., holding positive views about courses on educational research; Guilbert et al., 2016) and better learning (i.e., more extensive knowledge integration; Lee & Turner, 2018), whereas extrinsic motivation did not appear to have a significant impact on preservice teachers’ adaptation. Similarly, a content analysis found that receiving stimulating courses or being passionate about the subject matter was considered as
a motivational resource for many candidate teachers. These studies thus point to the 'value of training' as an academic resource that might help in coping with academic stressors (see Figure 1). Complementarily, some studies found that using 'learning strategies' associated with metacognition (e.g., self-monitoring, self-planning), or privileging approach coping strategies (e.g., behavioral attempts to deal directly with a problem) as opposed to avoidance coping strategies (e.g., cognitive attempts to avoid thinking realistically about a problem) was also beneficial to student teachers' learning (Gustems-Carnicer et al., 2019; Lee & Turner, 2018). The model presented in Figure 1 also includes academic self-efficacy (e.g., confidence in study skills) because of its central importance in fostering student motivation and, in particular, the decision to become a teacher (Löfström et al., 2010). Finally, some studies have underlined 'social support', such as positive and supportive experiences with peers and teachers, as an important self-reported motivational resource (Núñez-Regueiro, Escriva-Boulley, et al., 2023) that contributes significantly to persistence in teacher education (Figure 1; Kim & Corcoran, 2018).
<figure><img src="image_2.png" /><figcaption>Figure 1. Stressors and Resources Affecting Preservice Teachers’ Engagement in Teacher Education</figcaption></figure>

Note. Arrows denote positive (resources) and negative influences (stressors) on engagement.

Factors underlined are those included in the present study.
Integrating Stressors and Resources Undergirding Engagement during the Academic
Phase of Teacher Education: The Case of First Year Preservice Teachers in France

Preservice teachers can be viewed both as college students and as future professionals, so that a more complete theoretical framework can be reached by integrating academic and vocational stress processes, in which stressors and resources from both domains determine the processes of engagement in teacher education. However, the present study focuses on the academic stress processes of teaching education, which are most evident during the first year of teacher education in France. More precisely, teacher education in France begins after undergraduate studies and consists of two year of training, one academic-oriented (academia, first year) and one vocational-oriented (practicum, second year; MENJS-DEPP, 2020). During academic training, preservice teachers study educational theories and research methodology, while also preparing a competitive examination that conditions their recruitment as qualified teachers. At this stage, few vocational stressors exist (teaching practices are very limited), but numerous academic stressors occur, such as passing college examinations, preparing for the teacher recruitment contest at the end of year, and writing a research dissertation that often includes data collection and statistical analyses. These academic stressors are likely to disrupt engagement levels in academic training (i.e., vigor, dedication, absorption in studies) or in vocational aspirations (e.g., satisfaction, commitment to teaching), depending on preservice teachers' available academic or vocational resources. According to theories, four kinds of stress processes can contribute to explain variations in engagement levels in this context (see Figure 2).

First, stressors and resources can be conceived as exerting independent effects on engagement, as postulated by the original demands-resources model of organizational stress
(Demerouti et al., 2001). According to this approach, preservice teachers' academic and vocational stressors will undermine their engagement, whereas their academic resources will contribute to support it (*additive effects*; solid arrows in Figure 2). Importantly, this approach assumes that resources will not be affected by stressors (i.e., no mediation effect) and will not influence the impact of stressors on student engagement (i.e., no moderation effect; Demerouti et al., 2001).

By contrast, a mediation approach on stress processes (Lazarus, 1999) assumes that stressors will have a negative effect on engagement by eroding preservice teachers' resources (*mediation effects*; dashed arrows in Figure 2). For example, contextual stressors have been shown to negatively affect preservice teachers' self-efficacy which, in turn, undermined their commitment to teaching (Klassen et al., 2013). Complementarily, stress researchers (Wheaton et al., 2013) have underlined the existence of moderation effects between stressors and resources, according to which the impact of stressors on engagement are stronger among students with lower levels of resources (*moderation effects*; empty arrow in Figure 2). Similar mechanisms have been reported in studies showing that self-efficacy moderated the impact of vocational stressors (i.e., classroom disturbances) on teacher candidates' emotional exhaustion during *practicum*, in the sense that stressors had a negative impact only among low-confident candidates (i.e., low self-efficacy levels; Dicke et al., 2014).

A final approach on stress processes assumes that the above mechanisms combine in the form of moderated-mediated effects, in which mediation effects are expected to be stronger for individuals with lower levels of resources. More precisely, stressors are here expected to undermine engagement by eroding the levels of resources, but this mediation process of erosion is expected to be strongest or only observed for low-resourced individuals, notably because of
their heightened vulnerability to the impact of stressors (moderation-mediation effects; dashed
and empty arrows in Figure 2; Hobfoll et al., 2018; Pearlin & Bierman, 2013). To our knowledge,
no study has assessed the dual mediation-moderation function of resources with regards to
stressors' effects on preservice teachers' engagement. In sum, stress processes in teacher
education can be characterized according to four different mechanisms that have not yet been
compared (i.e., additive, mediation, moderation and moderated-mediation effects), but
preliminary evidence indicates the existence of complementary mechanisms of moderation and
mediation.
<figure><img src="image_3.png" /><figcaption>Figure 2. Stress Process Model of Student Engagement during the Academic Phase of Teacher Education</figcaption></figure>

Note. Academic and vocational factors correspond to stressors and resources that have additive (solid arrows), mediation (dashed arrow) or moderation effects (empty arrows) on engagement.
Study Aims and Hypotheses

The present study investigated the determinants of engagement in the first year of teacher
education, using a stress process accounting for both stressors and resources undergirding
student engagement. More specifically, the study explored the joint effects of academic stressors
and of vocational or academic resources on preservice teachers' engagement. According to stress
theories and preliminary studies, four different processes could be expected (see Figure 2). First,
it was expected that academic stressors (i.e., study workload, poor quality of courses,
competitive study climate) would have negative effects on students' engagement (i.e., academic
engagement in studies, satisfaction with career choice, commitment to teaching), whereas
academic resources (i.e., academic self-efficacy, support from peers and teachers) and vocational
resources (i.e., intrinsic teaching career motive, intrinsic and extrinsic teaching career value)
would have positive effects on engagement. These effects would not interact nor mediate one
another, but would occur independently (Hypothesis 1). Alternatively, it was expected that the
negative effects of stressors on engagement would be mediated by their negative effects on
resources (Hypothesis 2), or that the negative effects of stressors on engagement would be
moderated by students' resources, and more specifically, would be stronger for students with low
levels of resources (Hypothesis 3). A final hypothesis was that the mediation and moderation
effects would combine in explaining students' engagement, notably in the form a mediation
process being stronger for low-resourced individuals (Hypothesis 4). These alternative
hypotheses were explored to better characterize the interplay between stressors and resources in
teacher education, which previous studies had often analyzed separately.
Method

Participants

Data for the study was collected from 340 students in their first year of preservice teacher
education (i.e., post-graduate education), across 10 training sites from all over France. Although
the sampling scheme was not formally stratified for representativeness, equivalency tests show
that the study sample is significantly equivalent to the national population in terms of age (M =
23.5 years), gender (90.2%) and father SES (25.5% very high SES, 34.2% intermediate SES,
31.6% low SES) for students trained to teach in primary education (Table S1, Supplemental
Material—SM). Similar equivalencies are observed for secondary education student teachers,
except for the overrepresentation of female students in this group (i.e., 73.8% in the study sample
versus 54.4% in the national population; Table S1, SM). With the latter exception, the sample
can be described as very similar to the population in terms of socio-demographic background
(SIES, 2020).

Procedure

After approval of the research project by the ethic research committee (CERGA, #22-22),
teacher educators responsible for all 32 training centers in France were contacted to send an
invitation mail to the targeted students. The invitation mail contained a presentation of the study
and the link to an online survey measuring students' socio-demographic, academic and
psychosocial characteristics. Out of the 32 training centers, 10 accepted to take part in the study,
and 340 students accepted to participate. Participant and non-participant training centers did not
differ in their curricula or student admission processes, as these are harmonized on a national
level by law. On median, students completed the survey in 15 minutes. All measures were
obtained using this online survey.
Measures

Measures for the study were based on observables reported by participants, on scales adapted
from the educational literature, and on scales developed for the present purpose. For each scale,
factor scores were extracted and their reliability indices evaluated (omega), based on
measurement models specified on the R package lavaan (Jorgensen et al., 2020; McNeish &
Wolf, 2020; Rosseel, 2012). All measurements were reported by students and therefore reflected
their perceived experience of teacher education.

**Engagement in Teacher Education.** Student engagement was measured as a second-order latent factor for academic and vocational engagement (ω<sub>partial</sub> = .715). Academic engagement was measured by indicators of students' vigor, dedication, and absorption in their studies (6 items rated on a 7-point Likert scale of frequency, ω<sub>total</sub> = .769, sample item = *I am enthusiastic about my work*; reduced version of the Utrecht Work Engagement Scale, Zecca et al., 2015), whereas vocational engagement was measured by a second-order factor (ω<sub>partial</sub> = .900) reflecting students' commitment to teaching (2 items, sample item = *On a scale from 0 to 100, what are the chances that you stay in the teaching profession in the next 5 years*; Watt & Richardson, 2007), and satisfaction with career choice (3 items rated on 6-point Likert scale of agreement, sample item = *You feel that your decision to become a teacher was well informed*; Watt and Richardson 2007). A confirmatory factor analysis (CFA) for this structure provided excellent fit to the data [χ<sup>2</sup>(41) = 80.8, p < .001; CFI = .974; RMSEA = .057; SRMR = .050] (Hu & Bentler, 1999) and strong standardized loadings (.476 < β < .946).

**Academic Stressors.** Following studies on academic stressors (see Introduction), original
items rated on a 6-point Likert scale of agreement (1 = ‘Completely disagree’ to 6 = ‘Totally
agree’) were developed to obtain measures of study workload (3 items, ω<sub>total</sub> = .658, sample
item = There are many assignments or exams to take), poor quality of courses (4 items, $\omega_{total}$=.803, sample item = The course material lacks relevance or clarity) and competitive study climate (2 items, $\omega_{total}$= .879, sample item = There is a competitive climate between students).

Although scales initially comprised three additional items, these were removed to enhance the reliability of the scales. The measurement model for these scales provided excellent fit to the data [$\chi^2(24) = 48.1, p = .002$; CFI = .970; RMSEA = .056; SRMR = .041] and strong standardized loadings ($.470 < \beta < .729$).

**Academic Resources.** Resources specific to the academic domain were measured by using scales of college self-efficacy (6 items rated on a 6-point Likert scale of agreement, $\omega_{total}$= .879, sample item = *How confident are you that you could successfully complete the following tasks:/* Do well on your exams; Solberg et al., 1993), social support from peers (4 items rated on a 6-point Likert scale of agreement, $\omega_{total}$= .949) and social support from teachers (4 items rated on a 6-point Likert scale of agreement, $\omega_{total}$= .902). The latter scales were adapted from the Multidimensional Scale of Perceived Social Support (sample item = *I can count on my friends when things go wrong*; Denis et al., 2015), by changing the target ‘my friends’ by ‘my peers’ and ‘my teachers’, respectively. A CFA for this structure provided limited fit to the data [$\chi^2(74) = 312.5, p < .001$; CFI = .905; RMSEA = .107; SRMR = .052] but strong standardized loadings ($.450 < \beta < .950$).

**Vocational Resources.** Resources specific to the teaching profession were measured in terms of intrinsic teacher career motive, intrinsic teacher career value and extrinsic teacher career value. These scales were adapted from the Factors Influencing Teaching Choice scale (FIT-choice; Watt & Richardson, 2007). For some FIT-choice items, the original prefacing statement ‘I chose to become a teacher because…’ was kept to reflect teaching career motives or reasons.
This applied to items measuring intrinsic career reasons (5 items rated on a 4-point Likert scale of agreement, sample item = *I have always wanted to become a teacher*, ω<sub>total</sub> = .642). For a subset of the remaining FIT-choice items, the prefacing statement was replaced by an agreement statement ('To what extent do you agree with the following statements', rated on a 6-point Likert scale of agreement) to reflect teacher career values or motivations. This applied to items for social utility (5 items, sample item = *Teaching allows me to shape important child and adolescent values*) and teaching skills (2 items, sample item = *Teaching requires a high level of technical knowledge*) which loaded on a second-order factor measuring intrinsic teaching career value (ω<sub>partial</sub> = .776), and to items for family benefits (3 items, sample item = *Teaching hours fit well with the responsibilities of having a family*) and job benefits (3 items, sample item = *Teaching offers a secure job*), which loaded on a second-order factor measuring extrinsic teacher career value (ω<sub>partial</sub> = .799). Factor scores for the three scales were extracted from a CFA model showing satisfactory fit to the data [χ<sup>2</sup>(99) = 246.3, p < .001; CFI = .906; RMSEA = .069; SRMR = .048] and moderate to strong loadings (.237 < β < .887).

**Controls for Student Background.** Students’ socio-demographic and academic background was accounted for to control for confounding effects in the analyses of engagement in teacher education. Controls included students’ age, gender (0 = ‘Male’, 1 = ‘Female’), mother and father socioeconomic status (SES; 4-point French classification, 1 = ‘Low’ to 4 = ‘Very High’), parental and student educational level (8-point International Standard Classification of Education, 1 = ‘Primary education or less’ to 8 = ‘Doctoral or equivalent level’), parent in teaching (0 = ‘No’, 1 = ‘Yes’), number of children and parents under student’s care, single status (0 = ‘In a couple’, 1 = ‘Single’), and teaching level specialty (0 = ‘Primary education’, 1 = ‘Secondary education’).
Analytic Strategy

A series of structural equation models was estimated on the R package lavaan (Rosseel, 2012), using a sandwich estimator robust to non-normality, non-independence of observations and Full Information Maximum Likelihood to account for missing data (max. missing = 23% for age; FIML coverage = 99.5% to 100% of the sample). In a first step, significant effects were identified concerning control variables, in a view to obtain a baseline model of engagement. In a second step, multiple tests of the effects of stressors and resources were conducted to identify the stress processes described in H1 to H4, following mediation-moderation analyses (Figure 3; Muthén & Asparouhov, 2015). For each combination of stressors and resources, we first tested the significance of their additive effects (γ₁ and γ₂; H1). For parsimony and to avoid confounding, each of these effects was also assessed in combination with other stressors or resources, respectively. We then added and assessed the significance of their mediation effect (product θ₁ * γ₂ in Figure 3; H2), or ‘pure natural indirect effect’ (PNIE; Muthén & Asparouhov, 2015). Next, we tested H3 by assessing the significance of the stressor-resource moderation effect (γ₁₂ in Figure 3) in combination with additive effects (i.e., excluding the mediation effect of H2). Finally, we tested H4 by combining all previous effects and assessing the significance of the mediated-moderated effect (sum of products θ₁ * γ₂ + θ₁ * γ₁₂ in Figure 3) or ‘total natural indirect effect’ (TNIE; Muthén & Asparouhov, 2015). In these models, the significance of effects was established for each coefficient based on the probability of its z-value, on its contribution to approximating satisfactory fit indices (CFI ≥ .95, TLI ≥ .95, RMSEA ≤ .06, SRMR ≤ .08; Hu & Bentler, 1999) and on the probability of the z-value re-computed to account for false discovery rates associated with multiple testing (Benjamini & Hochberg, 1995).
<figure><img src="image_4.png" /><figcaption>Figure 3. Analytic Strategy for the Identification of Mediation-Moderation Processes</figcaption></figure>

Note. Mediation-moderation model are tested to identify stress processes associated with student engagement in teacher education.
## Results

### Associations Between Student Engagement and Predictors

Table 1 reports correlations between student engagement and its hypothesized determinants. Aligning with the literature (see Figure 1), engagement levels appeared to be negatively associated with academic stressors measured in terms of poor quality of courses ($r = -.254, p < .001$) and, marginally, of study workload ($r = -.093, p = .083$), and positively associated with academic and vocational resources such as college self-efficacy ($r = .395, p < .001$), peer support ($r = .174, p = .005$), teacher support ($r = .233, p < .001$), intrinsic teacher career motive ($r = .581, p < .001$) and intrinsic ($r = .297, p < .001$) or extrinsic teaching career values ($r = .223, p = .001$). In other words, preservice teachers showed greater engagement in their training when they experienced less stress (i.e., less stressors and more resources), which gives preliminary support to H1 through H4. However, one expected association was not apparent between engagement and competitive study climate ($r = -.034, p = .057$).

Moreover, all academic stressors correlated negatively with one or many academic and vocational resources, which might be coherent with either a mediation or moderated-mediation effect (H2 and H4). This was particularly true for relationships between academic stressors and academic resources such as college self-efficacy ($r_{study workload} = -.108, p = .061$; $r_{poor course material} = .215, p = .001$; $r_{competitive study climate} = -.106, p = .066$), peer support ($r_{poor course material} = -.154, p = .020$; $r_{competitive study climate} = -.388, p < .001$) and teacher support ($r_{study workload} = -.110, p = .030$; $r_{poor course material} = -.460, p < .001$; $r_{competitive study climate} = -.204, p < .001$). Relationships with vocational resources (i.e., intrinsic career motive, intrinsic or extrinsic career value) were weaker and less systematic, but were nevertheless apparent with regards to one stressor, namely receiving poor quality courses (-.138 < $r$ < -.108).
<table><caption>Table 1. Correlations Between Student Engagement, Stressors and Resources</caption><thead><tr><th>Variables</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th></tr></thead><tbody><tr><td>1. Student engagement</td><td>-.093†</td><td>-.254***</td><td>-.034</td><td>.395***</td><td>.174**</td><td>.233***</td><td>.581***</td><td>.297***</td><td>.223***</td></tr><tr><td>2. Study workload</td><td></td><td>.186***</td><td>.234***</td><td>-.108†</td><td>-.074</td><td>-.110*</td><td>-.041</td><td>.020</td><td>-.112*</td></tr><tr><td>3. Poor quality of courses</td><td></td><td></td><td>.271***</td><td>-.215***</td><td>-.154*</td><td>-.460***</td><td>-.138**</td><td>-.099†</td><td>-.108†</td></tr><tr><td>4. Competitive climate</td><td></td><td></td><td></td><td>-.106†</td><td>-.388***</td><td>-.204***</td><td>.080</td><td>.009</td><td>-.055</td></tr><tr><td>5. College self-efficacy</td><td></td><td></td><td></td><td></td><td>.251***</td><td>.233***</td><td>.209***</td><td>.209***</td><td>.195**</td></tr><tr><td>6. Support from peers</td><td></td><td></td><td></td><td></td><td></td><td>.332***</td><td>.061</td><td>.148*</td><td>.118†</td></tr><tr><td>7. Support from teachers</td><td></td><td></td><td></td><td></td><td></td><td></td><td>.081</td><td>.143**</td><td>.169**</td></tr><tr><td>8. Intrinsic teach. career motive</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>.250***</td><td>.120*</td></tr><tr><td>9. Intrinsic teach. career value</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>.325***</td></tr><tr><td>10. Extrinsic teach. career value</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note. Correlations are estimated jointly using robust maximum likelihood.

†*p* < .10. *p* < .05. **p* < .01. ***p* < .001.

## Models of Student Engagement

**Baseline Model.** In a preliminary model of student engagement, control variables accounting for student teachers' social, family and academic background were assessed (Model 2, Table S2, SM). This model showed that students' number of children in their care (ß = .121, *p* = .002) and, marginally, their educational level (ß = .076, *p* = .076) had significant, positive effects that contributed to explain approximately 2.5% of the variance in levels of student engagement. This constituted our baseline model to test effects of scientific interest (i.e., academic and vocational stressors and resources), first as independent effects and then conjointly using mediation-moderation analyses (cf. Tables S2 and S3, SM).

**Additive Effects.** The analysis of additive effects showed that the poor quality of the course material provided in teacher education had a significant, negative effect on engagement
(β = -.247, *p* < .001, 6 points of variance explained), whereas students' college self-efficacy (β = .237, *p* < .001), support from teachers (β = .102, *p* = .036) and intrinsic teaching career motive (β = .485, *p* < .001) had positive effects that contributed to explain 44 points of the variance. Other stressors (study workload, competitive study climate) and resources (support from peers, intrinsic or extrinsic valuing of teaching) had negative and positive effects on student engagement, but these effects were not significant when assessed in combination with other stressors or resources (Model 3, Table S2, SM) and were therefore dropped at this stage.

**Mediation-Moderation Effects.** Next, mediation-moderation analyses showed that the effects of stressors and resources were not independent from each other, but instead combined to explain 45.9 points of the variance in levels of engagement. This final model reported in Figure 4 showed excellent fit to the data and did not significantly differ from a saturated model with perfect fit [$\chi^2(18) = 23.7, *p* = .165; CFI = .976; TLI = .960; RMSEA = .035; SRMR = .039$]. Aligning with H2, four mediation processes were identified, three of which provided satisfactory fit to the data as well as robustness to false discovery rates corrections (Table S3, SM). More precisely, the negative effect of poor quality courses on engagement appeared to be due to its negative effects on levels of college self-efficacy (β = -.216, *p* = .001; PNIE = -.058, *p* = .002), support from teachers (β = -.461, *p* < .001; PNIE = -.041, *p* = .052) and intrinsic teaching career motive (β = -.139, *p* = .004; PNIE = -.072, *p* = .006). The effect of poor quality courses was reduced threefold by these mediation processes (changing from β = -.247 to β = -.073) and became non-significant in this model (i.e., β = -.073, *p* = .105). Moreover, five moderation effects between stressors and resources appeared to be significant, two of which also contributed to satisfactory fit indices, namely interaction between college-self-efficacy and study workload or poor quality courses. The former moderation (i.e., self-efficacy*workload) was found to be non-
significant after accounting for false discovery rates (Table S3, SM), but appeared to be
significant after accounting for other factors in the model (β = .008, *p* = .035) and is included in
the model for exploratory purposes that might serve future research, for replication studies (see
Figure 4). To probe the moderation effects, simple slopes were computed to represent the effect
of stressors at varying levels of self-efficacy (Table 2). Aligning with H3, this probing showed
that the negative effect of study workload reached near significance at very low levels of self-
efficacy (e.g., at 2SD under the mean, β = -.021, *p* = .063), and that very high levels of self-
efficacy contributed to cancel out and even revert in sign this effect (e.g., at +2SD, β = .153, *p* =
.042; see Table 2), thus suggesting that study workload is associated with higher levels of
engagement among preservice teachers who feel very efficacious in their academic work.
Similarly, poor quality courses had a negative effect on engagement among low-confident
individuals (e.g., -1SD in self-efficacy, β = -.196, *p* = .002), but a positive effect among very
confident students (e.g., +1SD in self-efficacy, β = .175, *p* = .013; Table 2).

Finally, aligning with H4, one moderated-mediation effect was identified for the
relationship between poor quality courses and college self-efficacy (TNIE = -.085, *p* = .002),
thus indicating the above mediation and moderation processes combined. Accordingly, receiving
poor quality courses had a negative effect on engagement that transited through its negative
effect on self-efficacy, but this mediation effect was only observed at low levels of self-efficacy.
For high-resourced individuals, receiving poor quality courses actually reinforced their efficacy
levels that, in turn, augmented their engagement levels.
<figure><img src="image_5.png" /><figcaption>Figure 4. Model of Student Engagement in the Academic Phase of Teacher Education</figcaption></figure>

Note. N = 340 preservice teachers. Exogenous variables are allowed to covary. PNIE = Pure natural indirect effect; TNIE = Total natural indirect effect. Model fit: $\chi^2(18) = 23.7$, p = .165; CFI = 0.976; TLI = 0.960; RMSEA = 0.035; SRMR = 0.039.

$\dagger p < .10. \ \ast p < .05. \ \thinspace \ \ast\thinspace p < .01. \ \thinspace \ \ast\thinspace\thinspace p < .001.$
<table><caption>Table 2. Effects of Academic Stressors on Student Engagement as a Function of College Self-Efficacy</caption><thead><tr><th rowspan="2">Academic Stressor</th><th rowspan="2">College Self-Efficacy<br>Levels</th><th colspan="2">Moderation Effect</th></tr><tr><th>Simple Intercept</th><th>Simple Slope</th></tr></thead><tbody><tr><td rowspan="5">Study workload</td><td>-2SD</td><td>-.980***</td><td>-.201†</td></tr><tr><td>-1SD</td><td>-.711**</td><td>-.113</td></tr><tr><td>Mean</td><td>-.442†</td><td>-.024</td></tr><tr><td>+1SD</td><td>-.173</td><td>.064</td></tr><tr><td>+2SD</td><td>.096</td><td>.153*</td></tr><tr><td rowspan="5">Poor course material</td><td>-2SD</td><td>-.980***</td><td>-.320***</td></tr><tr><td>-1SD</td><td>-.711**</td><td>-.196**</td></tr><tr><td>Mean</td><td>-.442†</td><td>-.073</td></tr><tr><td>+1SD</td><td>-.173</td><td>.051</td></tr><tr><td>+2SD</td><td>.096</td><td>.175*</td></tr></tbody></table>

Note. SD = Standard deviation. †p < .10. *p < .05. **p < .01. ***p < .001.
Discussion

The present study developed and tested an integrative model of engagement in teacher education, based on students’ perceived resources and stressors. Building on stress theories (Aldwin, 2007; Lazarus, 1999; Pearlin & Bierman, 2013; Wheaton et al., 2013) and studies on preservice teachers, this model conceptualized engagement as a function of stressors and resources related to preservice teachers’ academic identity (academic stressors and resources as college students) and vocational identity (vocational motivations as future professionals), and hypothesized on the relationships between these determinants to better characterize stress processes (i.e., additive, mediation and moderation effects) undergirding engagement in teacher education. Analyses of data on a national sample of French preservice teachers (N = 340 students) provided some support for the proposed model, thus shedding light on the motivational processes at stake in teacher education programs.

First, the investigation of preservice teachers' levels of engagement (i.e., engagement in the training and in the teaching career) confirmed the importance of academic stressors in the first year of teacher education in France. Results showed that when student teachers reported experiencing poor quality courses (i.e., perceived as lacking relevance or clarity, perceived limited competence of lecturers), they also tended to be less engaged in their training. Second, aligning with moderation processes (H3; Wheaton et al., 2013), study workload was negatively associated with engagement only among preservice teachers who did not feel confident in their study skills (i.e., very low levels of college self-efficacy). On the contrary, these academic stressors (i.e., study workload, poor quality courses) were positively associated with engagement
among those who felt very confident. Third, aligning with hypothesized mediation processes (H2; Lazarus, 1999), receiving poor quality courses had a negative effect on engagement that was completely due to its negative effects on perceived social support from teachers and on their vocational call for teaching (i.e., having decided to enter teaching for its intrinsic value). Finally, it was found that the poor quality of courses had a negative impact on engagement by eroding students' feelings of efficacy (mediation effect), but only among low confident students (i.e., with low levels of efficacy). Self-efficacy therefore played a mediation-moderation role that aligned with the hypothesized vulnerability-to-stress of students with lower resources (validation of H4; Hobfoll et al., 2018; Pearlin & Bierman, 2013). Unexpectedly, this mediation-moderation effect had the reverse effect among high-confident students, in the sense that poor quality courses tended to increase their engagement levels by boosting their feelings of efficacy. No evidence was found for the hypothesis that stressors and resources had independent (non-interrelated) effects on engagement (no validation of H1 from the demands-resources model; Demerouti et al., 2001).

The above findings offer novel insights on the processes that undergird student teachers' engagement in their training while also echoing previous research. Concerning moderation processes, the fact that academic stressors enhanced engagement among high self-efficacy students is resonant with findings indicating that students who feel very confident also have stronger mastery goal orientations (vs. performance-avoidance orientation; Hsieh et al., 2007), which increases their levels of motivation for learning and mastering teaching skills (Malmberg, 2006). This stronger motivation may thus modify their relationship towards academic stressors by perceiving these as challenges (vs. difficulties) that require a stronger investment on their part, thus eventually raising their levels of engagement (i.e., boosting effect of academic
stressors for academically-strong students, a kind of “psychological growth” under stress; Aldwin, 2007). On the contrary, low-confident students might perceive these stressors as insurmountable difficulties that deter them from investing their efforts. Study workload and poor quality courses might therefore have beneficial or deleterious effects on engagement depending on student profiles.

Moreover, the findings on mediation processes suggest that the poor quality of courses undermined levels of engagement by negatively affecting students’ academic (college self-efficacy, support from teachers) and, to a lesser extent, vocational resources (vocational call to teach), especially among students lacking confidence in their study abilities. As college educators, we may wonder which aspects of the course material can erode students’ resources in such a general manner. Previous research indicated that one major source of stress comes from the perception of courses as being overly academic in nature, such as courses on abstract educational theories or scholastic activities (e.g., dissertations, research papers) with limited transferability to teaching practices (Clarke et al., 2012; Deasy et al., 2016; Núñez-Regueiro, Escriva-Boulley, et al., 2023). Such courses are both demanding and indirectly related to the teaching profession, which might suffice to put pressure on academic and vocational resources alike. Bridging the gap between educational theories and teaching practices, as well as minimizing the accumulation of academic demands (e.g., by spacing them out more thoroughly over the course year), might be of service to alleviate this kind of pressure.

More generally, our analyses highlight the importance of stress processes during teacher education. Indeed, the mediation-moderation processes identified in our study explained almost 45% of the variance in preservice teachers’ engagement. As explained above, these processes also aligned with characteristic person-environment fit processes portrayed in the social stress
literature (including teacher stress), thus supporting the epistemic specificity of this framework for explaining engagement processes, notably in terms of teacher education stressors and resources. Although our study focused on stress processes induced by academic stressors only, their relationships with both academic and vocational resources showed evidence of the double-faceted identity of preservice teachers—i.e., as college students and future teaching professionals—in explaining their levels of engagement. More importantly, these results suggest that stress processes may emerge during the academic phase of teacher education, thus starting a process of erosion of resources (including teaching motivations) even before beginning teaching practices. In this perspective, and provided complementary research, the proposed framework might be of service to unify research on preservice teachers around integrative academic and vocational stress processes, by building on previous frameworks on academic processes of college student dropout or retention (Kim & Corcoran, 2018; Roberts, 2012), or on vocational processes of teacher stress or resilience (Dicke et al., 2014; Hong, 2010; Klassen et al., 2013; Valenzuela et al., 2019).

The above conclusions should be understood in the light of certain limitations that might be addressed in future research. Given that the identified stress processes were explored in this study for the first time, and using a cross-sectional design, complementary studies are needed to replicate these findings (notably those vulnerable to multiple-testing and false discovery corrections) and to test their robustness using diversified scales of stressors and resources and novel samples, while probing the causal nature of the identified relationships using a longitudinal design. This would include psychometric studies to validate scales of stressors and resources specific to different phases of teacher education (e.g., academic learning vs. teaching practicum), possibly based on the reviewed dimensions (see Figure 1), mediation-moderation analyses of the
relationships between these variables, to probe the regularity with which certain kinds of stress
processes relate to preservice teachers' engagement and performance and, complementarily,
research on processes of change in student teachers' stressors and resources, for which specific
techniques and preliminary applications exist but remain rare (Dicke et al., 2014; Klassen &
Durksen, 2014; Núñez-Regueiro, Juhel, et al., 2022). Finally, because few studies have actually
investigated engagement processes among student teachers (Kim & Corcoran, 2017), more
research is needed to identify influential factors more exhaustively. This implies combining in-
depth analyses of preservice teachers' self-reported experiences of these factors (e.g., analyses of
interviews or autobiographical texts), with regression models assessing their relative importance
for explaining student engagement, performance and graduation from teacher education.

Conclusion

The path to becoming a teacher implies developing satisfactory levels of teaching proficiency, as well as a resolute commitment to stay in the training program before entering the profession. As the present research has shown, teacher education can be a fulfilling experience characterized by high levels of engagement and learning, but this fulfillment is jeopardized when stress processes specific to the training phases (academic phase, practical phase) undermine preservice teachers' feelings of self-efficacy, social support and, importantly, vocational calling for the profession. Although these sources of stress are multidimensional (academic, vocational) and possibly numerous, the current state of the art provides limited evidence on their impact on dropout or disengagement behavior among candidate teachers. The integrative approach presented in this study might therefore serve as a stepping-stone towards the joint investigation of these stress processes, in a view to tackle multiple sources of difficulty in teacher education and, possibly, foster training, recruitment and retention rates in this valuable profession.
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