# Predictors of Dropout Intention in French Secondary School Students: The Role of Test Anxiety, School Burnout, and Academic Achievement

Chloé Sperduto, Fabien Fenouillet, Christophe Boujon, Marie Oger, Charles

Martin-Krumm, Evgeny Osin

► To cite this version:

Chloé Sperduto, Fabien Fenouillet, Christophe Boujon, Marie Oger, Charles Martin-Krumm, et al. Predictors of Dropout Intention in French Secondary School Students: The Role of Test Anxiety, School Burnout, and Academic Achievement. Central European Journal of Educational Research, 2024, 13 (4), pp.1901 - 1915. 10.12973/eu-jer.13.4.1901. hal-05465470

HAL Id: hal-05465470

https://univ-angers.hal.science/hal-05465470v1

Submitted on 19 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

European Journal of Educational Research

Volume 13, Issue 4, 1901 - 1915.

ISSN: 2165-8714

http://www.eu-jer.com/

# Predictors of Dropout Intention in French Secondary School Students: The Role of Test Anxiety, School Burnout, and Academic Achievement

Chloé Sperduto* ID

Université Paris Nanterre, FRANCE

Fabien Fenouillet ID

Université Paris Nanterre, FRANCE

Christophe Boujon

Université Angers, FRANCE

Marie Oger ID

IFEPSA-UCO Angers, FRANCE

Charles Martin-Krumm ID

École de Psychologues Praticiens de Paris of the Catholic Institute of Paris and UMR 1319 INSPIIRE INSERM
Université de Lorraine, FRANCE

Evgeny Osin ID

Université Paris Nanterre, FRANCE

Received: June 2, 2023 • Revised: July 6, 2024 • Accepted: September 13, 2024

**Abstract:** School dropout intention and reduced academic achievement are two crucial indicators of school dropout risk. Past studies have shown that school performance plays a mediating role in the models explaining dropout intentions. School burnout and test anxiety have been identified as predictors of both academic performance and school dropout. However, their combined effects on the intention to leave school have not yet been investigated. We aimed to address this gap by exploring the predictors of school dropout intention in a sample of 205 French secondary school students. Structural equation modelling analyses have revealed the specific facets of school burnout (devaluation) and test anxiety (cognitive interference) that explained the school dropout intentions and academic performance. Grade Point Average (GPA) was a mediator of the effects of these variables on the intention to drop out of school. The findings highlight the need to acknowledge assessments as a school stress factor that could contribute to health problems and intentions to drop out of school.

**Keywords:** Academic achievement, intention to leave school, school burnout, school demands, test anxiety.

**To cite this article:** Sperduto, C., Fenouillet, F., Boujon, C., Oger, M., Martin-Krumm, C., & Osin, E. (2024). Predictors of dropout intention in French secondary school students: The role of test anxiety, school burnout, and academic achievement. *European Journal of Educational Research, 13*(4), 1901-1915. https://doi.org/10.12973/eu-jer.13.4.1901

## Introduction

Despite a considerable decline in the average rate of dropout from school across Europe over the past ten years (from 13.8% to 9.7%; Fournier & Rakocevic, 2021, 2022), school dropout remains an important yet complicated issue. Past research has revealed the role of numerous factors, such as dropout intentions and academic performance, in explaining this phenomenon (Gubbels et al., 2019; Vallerand et al., 1997; Vinciguerra et al., 2020). The intention to drop out of school predicts dropout behavior (Eicher et al., 2014; Findeisen et al., 2024; Vallerand et al., 1997) and is also correlated with academic performance. Both dropout intention and academic performance can be viewed as outcomes of students' adaptation to the school system with its numerous demands, such as the need to choose between educational trajectories, academic performance requirements, high workload with intensive schedules, and pressure from parents and teachers (Oger, Broc, Martin-Krumm, et al., 2022). Various reports (Hoibian & Millot, 2018; National Centre for the Study of School Systems, 2017; Organisation for Economic Co-operation and Development [OECD], 2017, 2019) have highlighted that these demands may constitute serious challenges for students and generate anxiety associated with school assessments (Fenouillet et al., 2023; Putwain et al., 2020). Past research has also revealed the role of students' limited resources in explaining the problems with academic performance and health (Oger, Broc, Martin-Krumm, et al., 2022; Oger, Martin-Krumm, et al., 2022). The high level of demands can also lead to school burnout, which, together with test anxiety, could contribute to school dropout. However, to our knowledge, no research has investigated the joint effects of school burnout and test anxiety on academic performance and school dropout intentions. The present study aims to fill this gap.

<footnote>* Corresponding author:
Chloé Sperduto, Laboratoire Interdisciplinaire et Neurosciences, Physiologie et Psychologie. Université Paris Nanterre, France.
✉ chloe.sperduto@outlook.com</footnote>
## School Dropout: Intention and Behavior

In France, dropping out of school is defined by the law as any early interruption of schooling during the compulsory education period which lasts until the age of 16. Pupils are also obliged to follow a vocational training course until the age of 18 to obtain a professional qualification (Republic of France, 2024).

School dropout can be predicted by various factors, firstly, dropout intentions. Vallerand et al. (1997) aimed to explain why some students (9th-10th grade) develop intentions to drop out of school. They conceptualized dropout intention as a predictor of school dropout behavior and a mediator of the effects of academic competencies and other variables, such as academic motivation, perceived autonomy, and autonomy support by parents, teachers, and school administration. Although dropout intention was a statistically significant predictor of dropout behavior, the effect size was modest (β = .24). Eicher et al. (2014) have examined this link in secondary school students over three years and found that the intention to drop out was a significant yet modest predictor of actual dropout in the subsequent year (OR = 1.69, 1.75, and 1.37, corresponding to r = .14, .15, and .09, respectively).

Numerous studies have revealed a wide range of predictors of the intention to drop out of school and the dynamic nature of relationships between these variables (e.g., Alivernini & Lucidi, 2011; Eicher et al., 2014; Freeney & O'Connell, 2012; Hardre & Reeve, 2003; Legault et al., 2006; Rump et al., 2017; Samuel & Burger, 2020). The findings of longitudinal studies suggest that these relationships might be reciprocal, with dropout intention being influenced by continuously evolving school experiences and vice versa (Scheunemann et al., 2022). Despite the diversity of predictors, numerous studies suggest that poor academic achievement plays a central role in explaining dropout intentions (Alivernini & Lucidi, 2011; Balkis, 2018; Hardre & Reeve, 2003; Haugan et al., 2019).

## Academic Achievement and School Dropout

Academic success is conceptualized as the result of academic performance that depends on students' ability to meet the demands imposed by their school environments (Fiorilli et al., 2017). At schools, grade point average (GPA) is typically used to assess students' performance across different subjects (Florin et al., 2023; Martin-Krumm et al., 2005; Peterson & Barrett, 1987). This measure provides a summary of current academic results, is related to student abilities and academic motivation (Kriegbaum et al., 2018) and is also predictive of future performance (Papadogiannis et al., 2023). The GPAs are of vital importance for students, because they determine the range of available choices of future educational trajectories, as well as for educational stakeholders who use them to evaluate the educational outcomes. All of this results in a strong pressure on students to succeed (Kriegbaum et al., 2018). Therefore, it is not surprising that GPA often emerges as an important predictor of dropout (Gubbels et al., 2019; Vinciguerra et al., 2020) and a mediator of the effects of other variables on dropout intention (Balkis, 2018; Hardre & Reeve, 2003).

OECD reports point out the important role of school demands in explaining academic outcomes. Firstly, because academic performance is an inescapable demand at school, a significant proportion of students experience test anxiety (OECD, 2017). Given that assessments are very common in the educational system, test anxiety can disrupt the adaptive cognitive and self-regulatory processes and lead to repeated maladaptive responses, undermining academic success (Lowe, 2021). Secondly, school demands that are perceived by students as excessive in relation to the resources available for them to cope may result in school fatigue and, eventually, burnout, which contributes to maladjustment (Oger, Broc, Martin-Krumm, et al., 2022; Oger, Martin-Krumm, et al., 2022). Thus, evaluation outcomes shape future educational trajectories and reflect the students' adaptation to the demands imposed by school environment and this adaptive process may be undermined by text anxiety and school burnout.

## The Effects of School Burnout and Test Anxiety on Academic Outcomes

The concept of burnout has been recently extended to the school setting (Oger et al., 2020; Salmela-Aro et al., 2008, 2009). School burnout manifests itself in three different dimensions: a cynical attitude (i.e., loss of interest and detachment from school), a reduced sense of achievement and competence in school, and chronic physical and/or emotional exhaustion (Lebert-Charron et al., 2021; Meylan et al., 2015; Salmela-Aro et al., 2009). Although these dimensions are specific, it is their combination that determines the extent of school burnout (Isoard-Gautheur et al., 2010).

The negative influence of school burnout on academic performance has been demonstrated by numerous studies (Madigan & Curran, 2021) that have revealed the negative effects of burnout on specific performance indicators, such as those in physical education (PE; Martin-Krumm et al., 2011), as well as on general academic performance (Fiorilli et al., 2017). Notably, the findings of Martin-Krumm et al. (2011) suggest that school burnout inhibits the effect of learner-perceived ability on performance. According to Madigan and Curran (2021), increased physical and emotional exhaustion leads to difficulty in managing academic demands, such as school assessments. Their meta-analysis suggests that school burnout leads to a lack of interest regarding school tasks and an inability to invest effort, negatively affecting assessment outcomes.

With respect to dropout, Bask and Salmela-Aro (2013) have examined the direct effects of school burnout on this outcome over a five-year period. They found that dropout was mainly predicted by the cynicism dimension of burnout,
as well as by feelings of inadequacy, whose effect overlapped with GPA. However, the effect of burnout on dropout intention has only been studied in higher education settings (Abreu Alves et al., 2022; Marôco et al., 2020). In sum, past studies indicate that school burnout influences students' academic success and, as a result, their educational trajectories.

The academic outcomes also depend on the way students perceive assessments. If an assessment is perceived as threatening by the learner, this depletes the resources available for meeting the assessment demands and may negatively impact academic performance. This phenomenon is labelled as test anxiety. This context-specific type of trait anxiety (Spielberger & Vagg, 1995) leads to worry and cognitive dysfunction, as well as emotional and/or physiological stress reactions related to assessments (Putwain & von der Embse, 2021). As a result, students suffering from this form of anxiety incur significant psychological costs, whether in anticipation of, during, or after exams (Putwain & von der Embse, 2021) and this effect can be exacerbated by repeated assessments that are supposed to support learning (Fagnant, 2022; von der Embse & Hasson, 2012).

Numerous studies show that test anxiety is associated with lower academic performance (Hembree, 1988; Robson et al., 2023; Schnell et al., 2015). According to a recent meta-analysis, this negative effect occurs regardless of the learner's grade and is particularly strong for national exams (von der Embse et al., 2018). These findings also suggest that the detrimental effects of test anxiety on student adjustment may depend on the place of assessments in the educational system. Compared to other European countries, France is one of those making the most use of assessment and the least use of self-assessment (Ministry of National Education and Youth, 2022). In France, assessments are perceived as an important source of stress by one in two pupils (Florin et al., 2023). Therefore, it is no surprise that in the French context test anxiety emerges as an important negative predictor of academic success (Fenouillet et al., 2023; Fortin et al., 2006; Janosz et al., 2000), which may result in dropout. Some existing data indicate that general anxiety is characteristic of students at risk of dropout (Vinciguerra et al., 2020), however, to our knowledge, the effect of test anxiety on dropout intentions has not yet been explored.

## The Present Study

The aim of our study was to explore the role of test anxiety, school burnout, and school performance in predicting school dropout intentions. Previous studies have found links between these variables in isolation, but did not study them together. For instance, although general anxiety has been linked to dropout (Vinciguerra et al., 2020), there is no data on the relationship of test anxiety to dropout intentions. Similarly, although research has shown the detrimental effect of burnout on academic performance (Fiorilli et al., 2017; Madigan & Curran, 2021) and the effect of GPA on dropout intention (Alivernini & Lucidi, 2011; Balkis, 2018; Hardre & Reeve, 2003; Haugan et al., 2019), to our knowledge, no studies have investigated these links in secondary school students.

Our research aims to fill these gaps by modelling these variables together in one analysis using a secondary school sample. Based on past findings, we aim to test whether academic performance mediates the effects of school burnout and test anxiety on dropout intentions. The conceptual model is shown in Figure 1.

<figure><img src="image_3.png" /><figcaption>Figure 1. The Hypothesized Model</figcaption></figure>
We aim to test the following hypotheses:

* H1: School burnout and test anxiety are positively related to dropout intentions.

* H2: School burnout and test anxiety are negatively related to GPA.

* H3: GPA is a negative predictor of dropout intentions.

* H4: GPA mediates the effects of school burnout and test anxiety on dropout intentions.

Methodology

Design

The study used a longitudinal design with two waves to examine the relationships between test anxiety, school burnout, academic performance, and intention to drop out of school. Test anxiety and school burnout were measured at the beginning of the school year (Wave 1: October and November 2021), whereas the dropout intention was measured at the end of the year (Wave 2: June 2021), together with objective academic performance data (GPA). We used structural equation modelling with latent variables to test the partial mediation model. The following sections describe in detail the characteristics of the participants and the measurement instruments we used.

Participants

Four hundred and forty-three French school students enrolled in public and private schools participated in the study. The final sample only includes those students who provided their responses at both waves of the study (N = 205). We had aimed for a minimum sample size of N = 200, which is considered sufficient for latent models with four indicators (Westland, 2010) and would allow us to detect medium-sized mediation effects (Sim et al., 2022).

The participants came from schools located in Angers, Nantes, and Le Mans – areas where the dropout rate ranges from fairly high to the highest in the Pays-de-la-Loire region (Hervy & Kaiser, 2023). The adolescents were enrolled in grades 8 to 11, with 70.73% coming from middle school and 29.7% from high school. They ranged in age from 12 to 17 years old (M = 14.1; SD = 1.13), with 101 students identifying as girls (49.3%), 102 as boys (49.8%), and 2 (1%) as non-binary (Table 1).

<table><caption>Table 1. Participant Characteristics</caption><thead><tr><th rowspan="2">Characteristic</th><th rowspan="2">N</th><th colspan="2">Grade 8<br>(N = 78)</th><th colspan="2">Grade 9<br>(N = 68)</th><th colspan="2">Grade 10<br>(N = 35)</th><th colspan="2">Grade 11<br>(N = 24)</th></tr><tr><th>M</th><th>SD</th><th>M</th><th>SD</th><th>M</th><th>SD</th><th>M</th><th>SD</th></tr></thead><tbody><tr><td>Age</td><td></td><td>13.1</td><td>.50</td><td>14.1</td><td>.30</td><td>15.3</td><td>.70</td><td>15.6</td><td>.80</td></tr><tr><td>Girls</td><td>101</td><td>13.0</td><td>.40</td><td>14.1</td><td>.30</td><td>15.3</td><td>.80</td><td>15.5</td><td>.80</td></tr><tr><td>Boys</td><td>102</td><td>13.1</td><td>.50</td><td>14.1</td><td>.30</td><td>15.3</td><td>.70</td><td>15.5</td><td>.80</td></tr><tr><td>Non-binary</td><td>2</td><td></td><td></td><td>14.0</td><td></td><td></td><td></td><td>16.0</td><td></td></tr></tbody></table>

Note. n=205

Measures

Test anxiety was measured using the Multidimensional Test Anxiety Scale (MTAS; Putwain et al., 2020). The French translation was validated by Fenouillet et al., (2023). This scale consists of 16 items grouped into four distinct dimensions: tension, physiological indicators of anxiety, cognitive interference, and worry. Each dimension is measured by four items evaluated using a Likert scale from 1 “strongly disagree” to 5 “strongly agree”. The scale showed satisfactory psychometric properties and acceptable internal consistency (ω, tension: .81, physiological indicators: .84, cognitive interference: .78, and worry .76) in a sample of French secondary school students.

School burnout was measured using the Athlete Burnout Questionnaire (Isoard-Gautheur et al., 2010) that taps into exhaustion in the context of school and sport. This French-language scale consists of 12 items divided into three dimensions: physical and emotional exhaustion, devaluation, and reduced sense of accomplishment. The responses are given using a Likert scale ranging from 1 “never” to 5 “always”. In the French context, the scale showed satisfactory psychometric properties and acceptable internal consistency (i.e., α: physical and emotional exhaustion .88, devaluation .75 and reduced sense of accomplishment .77; Isoard-Gautheur et al., 2010; ω for the global factor .90, .88, respectively; Oger, Broc, Martin-Krumm, et al., 2022; Oger, Broc, Rotonda, et al., 2022).

Dropout intention was assessed using a single question recently proposed by Balkis (2018): “How often do you consider dropping out of school?”. The responses were given using a Likert scale ranging from 1 “never” to 5 “often”.

As a measure of school performance, we used the annual GPA taken from school records and based on all grades in the subjects studied during the year. The GPA ranged from 7.20 to 18.00. To control for the possible effects of differences in marking across schools, we standardized the GPA scores within each school.
## Procedure

Informed consent was obtained from parents and participants at the beginning of the school year by means of a letter sent to the families. The scales were administered in computerized format during the lessons. The instructions for the study were given to the students in the computer room before the questionnaires were presented. The students then had 15 minutes to complete the questionnaires individually. Participants completed the measures of test anxiety and school burnout during the months of October and November 2021. At the end of the school year, they answered the question about their intention to drop out of school (Balkis, 2018) and their cumulative GPAs for the year were collected. The study protocol was approved by the institutional research ethics committee of Angers University in France (ethics approval number; UA-CER-2021-05).

## Analytic Strategy

First, we used McDonald's ω to assess the internal consistency of the scales and performed descriptive and correlation analyses using R 4.1 (package Psych 2.09; R Core Team, n.d.) and Jamovi 2.3.28 (Jamovi, n.d.). Finally, structural equation modeling was performed using Mplus 8.8 (B. O. Muthén et al., 2017; L. K. Muthén & Muthén, 2017) with WLSMV estimator. In line with Sellbom and Tellegen (2019), items using Likert scales were modelled as ordered-categorical. Following Hu and Bentler (1999), we used the values of RMSEA and SRMR below .08 and CFI and TLI above .90 as evidence of acceptable fit. Finally, to estimate the indirect effects, we used 95% bias-corrected confidence intervals (CI) derived based on 1,000 bootstrap samples, as this approach allows us to maximize statistical power to detect the indirect effects (Preacher & Hayes, 2008; Sim et al., 2022).

There was only one missing response to a questionnaire item and 33 missing responses to the intention to dropout question. The Little's MCAR test (R package misty 0.6.2) was not significant (χ² = 56.82, df = 58, p = 0.52), suggesting that the data were missing completely at random in line with the assumption of the WLSMV estimator (B. O. Muthén et al., 2017). To rule out the possible parameter bias due to missing data, we also repeated the analysis with multiple imputation using 10 datasets. This approach allows a more robust missing data treatment (van Ginkel et al., 2020), but cannot be currently used with bootstrap in Mplus (instead, we provide classical symmetric confidence intervals and p-values).

## Results

### Descriptive Statistics and Correlations

Table 2 presents the descriptive statistics. The values of skewness and kurtosis indicated that the scores were approximately normally distributed (Kim, 2013; Markus, 2012) and all the sum scales had ω values greater than .70, indicating good internal consistency (Dunn et al., 2014).

<table><caption>Table 2. Descriptive Statistics</caption><thead><tr><th></th><th>N</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th><th>Skewness</th><th>Kurtosis</th><th>ω</th></tr></thead><tbody><tr><td>GPA</td><td>205</td><td>13.4</td><td>2.02</td><td>7.20</td><td>18</td><td>-0.04</td><td>-0.29</td><td></td></tr><tr><td>DI</td><td>172</td><td>2.50</td><td>1.42</td><td>1</td><td>5</td><td>0.52</td><td>-1.07</td><td></td></tr><tr><td>W</td><td>205</td><td>13.5</td><td>4.62</td><td>4</td><td>20</td><td>-0.43</td><td>-0.84</td><td>.88</td></tr><tr><td>CI</td><td>205</td><td>10.6</td><td>4.62</td><td>4</td><td>20</td><td>0.35</td><td>-0.91</td><td>.89</td></tr><tr><td>T</td><td>205</td><td>12.5</td><td>4.90</td><td>4</td><td>20</td><td>-0.06</td><td>-1.06</td><td>.90</td></tr><tr><td>PI</td><td>205</td><td>9.00</td><td>4.77</td><td>4</td><td>20</td><td>0.93</td><td>-0.18</td><td>.89</td></tr><tr><td>E</td><td>205</td><td>11.6</td><td>4.54</td><td>4</td><td>20</td><td>0.13</td><td>-1.11</td><td>.91</td></tr><tr><td>DE</td><td>205</td><td>10.3</td><td>3.57</td><td>4</td><td>19</td><td>0.17</td><td>-0.81</td><td>.72</td></tr><tr><td>A</td><td>205</td><td>10.9</td><td>3.51</td><td>4</td><td>20</td><td>0.30</td><td>-0.55</td><td>.84</td></tr></tbody></table>

Note. DI= Dropout Intention; Dimension of test anxiety, W=Worry; CI=Cognitive Interference; T=Tension; PI=Physiological Indicators (PI); Dimension of school burnout, E=Exhaustion; DE= Devaluation; A=Accomplishment.

The correlations presented in Table 3 revealed that the dropout intention was negatively associated with academic performance (GPA), in line with Hypothesis 3. In line with Hypotheses 1 and 2, all three dimensions of school burnout were positively associated with dropout intention and negatively associated with GPA. However, with respect to test anxiety, only its cognitive interference dimension showed the same pattern of associations. The worry, tension, and physiological indicators dimensions of MTAS were not significantly associated either with GPA or dropout intention.
<table><caption>Table 3. Correlations Between Study Variables</caption><thead><tr><th></th><th>GPA</th><th>DI</th><th>W</th><th>CI</th><th>T</th><th>PI</th><th>E</th><th>DE</th><th>A</th></tr></thead><tbody><tr><th>GPA</th><td>—</td><td>-.31***</td><td>-.04</td><td>-.33***</td><td>.02</td><td>.05</td><td>-.14*</td><td>-.30***</td><td>-.45***</td></tr><tr><th>DI</th><td>-.25**</td><td>—</td><td>-.08</td><td>.30***</td><td>-.04</td><td>.06</td><td>.26**</td><td>.38***</td><td>.26**</td></tr><tr><th>W</th><td>-.03</td><td>-.05</td><td>—</td><td>.57***</td><td>.90***</td><td>.72***</td><td>.27***</td><td>.14</td><td>.42***</td></tr><tr><th>CI</th><td>-.33***</td><td>.28***</td><td>.52***</td><td>—</td><td>.58***</td><td>.53***</td><td>.38***</td><td>.47***</td><td>.59***</td></tr><tr><th>T</th><td>.01</td><td>-.01</td><td>.80***</td><td>.52***</td><td>—</td><td>.85***</td><td>.40***</td><td>.10</td><td>.31***</td></tr><tr><th>PI</th><td>.03</td><td>.06</td><td>.60***</td><td>.47***</td><td>.73***</td><td>—</td><td>.44***</td><td>.16*</td><td>.23**</td></tr><tr><th>E</th><td>-.15*</td><td>.24**</td><td>.23**</td><td>.36***</td><td>.36***</td><td>.42***</td><td>—</td><td>—</td><td>.53***</td></tr><tr><th>DE</th><td>-.23**</td><td>.32***</td><td>.09</td><td>.37***</td><td>.08</td><td>.14*</td><td>.37***</td><td>—</td><td>.72***</td></tr><tr><th>A</th><td>-.41***</td><td>.23**</td><td>.35***</td><td>.54***</td><td>.28***</td><td>.22**</td><td>.48***</td><td>.51***</td><td>—</td></tr></tbody></table>

Note. * *p* < .05, ** *p* < .01, *** *p* < .001. The correlations of observed variables are shown below the diagonal, and the correlations of latent variables (based on the measurement model) are given above the diagonal. DI=Dropout Intention; Dimensions of test anxiety: W=Worry, CI=Cognitive Interference, T=Tension, PI = Physiological Indicators; Dimensions of school burnout: E=Exhaustion, DE=Self-devaluation, A=Accomplishment.

## Mediation Analysis

To test the Hypothesis 4 that the effects of test anxiety and school burnout on the dropout intention are mediated by academic performance, we conducted structural equation modelling. First, we tested a measurement model, where the 7 latent factors corresponding to the dimensions of test anxiety and burnout were freely correlated with GPA and dropout intention. The theoretical model fit the data acceptably [$\chi^2(371) = 639.78, p < .001$; CFI = .971, TLI = .966, RMSEA = .059, 90% CI [.052, .067], SRMR = .053], because RMSEA and SRMR are below .08 and CFI and TLI are above .90. The factor loadings were in the .46-.94 range for the items ($M = .83, SD = .11$). The correlations between the latent factors, GPA, and dropout intention (see Table 3) were quite consistent with the results based on observed variables.
<figure><img src="image_4.png" /><figcaption>Figure 2. Results of Structural Equation Modelling (Model 1)

Note. The correlations between the latent factors are not shown for readability and can be found in Table 3. \*p\*<.05; \*\*p\*<.01; \*\*\*p\*<.001</figcaption></figure>

Next, we tested a mathematically equivalent model with partial mediation (Model 1, see Figure 2), where the seven latent factors predicted GPA, which partially mediated their effects on dropout intention. Only two scales had statistically significant effects on GPA: Accomplishment (β = -.51, 95% CI [-1.00, -.16]) and Cognitive Interference (β = -.33, 95% CI [-.57, -.10]). Cognitive Interference was also the only scale to show a significant direct effect on dropout intention (β = .30, 95% CI [.04, .66]). GPA also had a significant negative effect on dropout intention (β = -.19, 95% CI [-.41, -.004]). However, the indirect effects on dropout intention mediated by GPA were marginal for both Cognitive Interference (β = .063, 95% CI [-.006, .164]) and Accomplishment (β = .098, 95% CI [-.006, .306]). None of the other indirect or direct effects approached significance. This model is presented in Figure 2; the full version can be found in Supporting Information.

To increase statistical power, we tested another model (Model 2, see Figure 3) with non-significant direct effects omitted [$\chi^2(377) = 663.50, p < .001$; CFI = .969, TLI = .964, RMSEA = .061, 90% CI [.053, .068], SRMR = .058]. In this model, Cognitive Interference emerged as the only significant predictor of GPA (β = -.41, 95% CI [-.77, -.15]), which, in turn, predicted the dropout intention (β = -.53, 95% CI [-.88, -.19]). The indirect effect of Cognitive Interference on dropout intention was statistically significant (β = .22, 95% CI [.04, .62]), whereas its direct effect was not (β = .07, 95% CI [-.33, .33]). The indirect effect of reduced sense of accomplishment was, again, marginal (β = .23, 95% CI [-.03, .54]), as was its effect on GPA (β = .43, 95% CI [-.03, .54]). This model is presented in Figure 3; the full version can be found in Supporting Information.
<figure><img src="image_5.png" /><figcaption>Figure 3. Results of Structural Equation Modelling (Model 2)

*Note.* The correlations between the latent factors are not shown for readability and can be found in Supporting Information. \*p<.05; \*\*p<.01; \*\*\*p<.001</figcaption></figure>

Finally, to rule out the possible bias due to missing data, we re-tested Models 1 and 2 with multiple imputation. The results for both models were in line with those reported above. In the Model 1, which fit the data acceptably [$\chi^2(371) = 641.77, p < .001$; CFI = .971, TLI = .966, RMSEA = .060, SRMR = .053], the parameter estimates were quite similar to those obtained previously, with the only minor difference being a slightly stronger effect of GPA on dropout intention ($\beta = -.22, 95\% CI [-.37, -.06], p = .007$). As a result, the indirect effects on dropout intention mediated by GPA were significant for both Cognitive Interference ($\beta = .07, 95\% CI [.01, .13], p = .031$) and reduced sense of accomplishment ($\beta = .11, 95\% CI [.00, .22], p = .049$). Again, Cognitive Interference was the only scale with a significant direct effect on dropout intention ($\beta = .33, 95\% CI [.09, .57], p = .006$).

The Model 2 also fit the data acceptably [$\chi^2(377) = 674.26, p < .001$; CFI = .968, TLI = .963, RMSEA = .062, SRMR = .059]. The effect of GPA on dropout intention was, again, somewhat stronger ($\beta = -.61, 95\% CI [-.83, -.38], p = .007$) and both the effects of cognitive interference ($\beta = -.48, 95\% CI [-.71, -.24], p < .001$) and accomplishment ($\beta = -.39, 95\% CI [-.74, -.04], p = .029$) on GPA were significant. As a result, the indirect effects on dropout intention mediated by GPA were significant for both cognitive interference ($\beta = .29, 95\% CI [.10, .49], p = .003$) and reduced sense of accomplishment ($\beta = .23, 95\% CI [.01, .46], p = .039$)

Overall, the results indicate that higher cognitive interference component of test anxiety predicts increased dropout intention at the end of the year and that this effect is mediated by lower GPA.
## Discussion

Combating school dropout is a priority goal within the educational system (Fournier & Rakocevic, 2021). Our study aims to help educational professionals to better understand the impact of the challenges students face and to adjust demands accordingly. The results reveal several links between dropout intentions, GPA, test anxiety, and academic burnout. Correlational analysis showed that all dimensions of burnout were positively related to dropout intentions with the strongest effect of reduced sense of accomplishment. Regarding test anxiety, we found that neither its emotional nor physiological dimensions were associated with the intention to drop out: the latter was only related to the cognitive dimension of test anxiety. This finding suggests that the cognitive effects of test anxiety are of particular importance for GPA and dropout intentions.

Our main aim was to assess the mediating role of GPA in explaining the intention to drop out. The first structural equation model revealed that only cognitive interference and reduced sense of accomplishment were negative predictors of GPA. Indirect effects on intention to quit mediated by GPA were also found for these two variables but failed to reach significance. In a second model calculated without the non-significant direct effects, only cognitive interference emerged as the only reliable predictor of intention to drop out, whose effects were mediated by GPA. However, in the models with multiple imputations, both cognitive interference and reduced sense of accomplishment emerged as significant predictors. These results confirm our hypotheses and are consistent with previous findings showing the mediating role of academic performance on dropout and dropout intention (Alivernini & Lucidi, 2011; Balkis, 2018; Hardre & Reeve, 2003). Past studies, however, have focused primarily on motivational predictors, such as self-efficacy (Alivernini & Lucidi, 2011), lower value placed on task (Balkis, 2018), or self-determination (Hardre & Reeve, 2003), revealing their direct effects on dropout outcomes, as well as indirect effects mediated by GPA.

Overall, this study highlights test anxiety and reduced sense of accomplishment as two risk factors of school dropout intention that have not yet been fully explored in France (Fenouillet et al., 2023; Lebert-Charron et al., 2021), although their negative impact has been widely documented in the literature (Oger et al., 2020; von der Embse et al., 2018). We see the contribution of our study in showing that the cognitive interference dimension of test anxiety may be particularly detrimental for academic outcomes, independently of burnout. This is consistent with prior findings showing that the cognitive dimension of test anxiety is particularly detrimental for academic performance (Eysenck et al., 2007; Fenouillet et al., 2023; Steinmayr et al., 2016; von der Embse et al., 2013). According to attentional control theory, test anxiety caused by students' perception of test as a threat leads to a reduction in the capacity of working memory available for the task and to a redirection of attention from the task to the perceived threat (Derakshan & Eysenck, 2009; Eysenck et al., 2007; Spalding & Brown Nicholls, 2021). Anxiety limits the capacity of working memory because it competes with other cognitive processes needed to perform the task (Moran, 2016). In addition to being a major obstacle for academic performance (Fenouillet et al., 2023), text anxiety may be particularly problematic in the context of high-stakes examinations that have important implications for the students' future. The findings suggest that it may be helpful to specifically target the manifestations of cognitive interference in future educational interventions aimed at reducing school dropout.

On the other hand, the indicators of school burnout did not emerge as important predictors of dropout quite consistently. Although some previous studies have shown the relationship between school burnout and dropout (Bask & Salmela-Aro, 2013), the data from secondary school settings are still rather scarce. Studies in the higher education context show that school burnout can also be viewed as a mediator of the effects of other variables (e.g., social support, negative coping; Abreu Alves et al., 2022; Marôco et al., 2020; Sinval et al., 2024) on dropout. These findings highlight the need for further research in secondary schools to explore the relationship of school burnout to dropout intentions. Unlike test anxiety, which is often considered a stable trait (Spielberger & Vagg, 1995), school burnout is a more dynamic state that may fluctuate in line with the individual's ability to respond to the demands of their environment (Lebert-Charron et al., 2021; Vansoeterstede et al., 2023). The weak contribution of burnout may also be due to the fact that we attempted to measure it at the beginning of the year, at a time when school demands may not have increased sufficiently to result in burnout.

## Conclusion

This study used mediation analysis to examine the influence of test anxiety and school burnout at the beginning of the year on school performance and dropout intentions. The results show that the cognitive effects of assessment anxiety play a crucial role in explaining GPA and dropout intentions. The results of this study highlight the importance of considering the impact of assessment from the beginning of the school year in France to prevent pupils from dropping out. A critical review of current assessment practices could help change the conditions in which assessment takes place in the educational system and design measures to reduce the negative impact of cognitive interference. Furthermore, this study highlights the importance of a holistic approach when considering the risk factors that affect schooling and indirectly contribute to dropout prevention. In short, since assessment is one of the requirements of the school environment, these results underline the need to recognize and take into account the impact of school requirements in order to support students on their journey and to put in place teaching practices that protect them on a daily basis and help prevent dropout stemming from a mismatch between the students' resources and the perceived demands of school (Oger, Broc, Martin-Krumm, et al., 2022; Oger et al., 2020; Oger, Martin-Krumm, et al., 2022).
## Recommendations

If the way people respond to assessment predicts their intention to drop out, it would seem that institutions could help prevent dropout by optimizing the learning conditions. First of all, it is important to ensure that learners have the necessary resources to cope with the evaluation of their knowledge. Whenever this is not the case, assessment fails to be fair and objective and thus loses its value for educators. The French education system attaches great importance to evaluation (Rey & Feyfant, 2014) and uses it to reward the most deserving students with better professional opportunities (Florin et al., 2023). Studies of national assessments in France show that assessments that encourage comparison between pupils have harmful effects (Florin et al., 2023). Getting rid of grades does not seem to be the solution to this problem, as countries and regions that had opted for this practice in the past (e.g., Switzerland, Quebec) have since reversed it. Instead, it is recommended to adopt assessment practices based on criteria that are clearly explained to the students. This would make it possible to monitor individual progress without an emphasis on comparison across individuals (Florin et al., 2023). Other practices, such as self-assessment and peer assessment, have been identified in the literature as more conducive to good school performance than traditional teacher assessment (Double et al., 2020). To combat school dropout, it seems useful for our education system to draw on these approaches and review the assessment methods, particularly in situations when assessment outcomes impact the individuals' access to further education opportunities.

Another way to prevent dropout in this stressful, high-stakes context would be to provide students with educational programs and interventions to help them cope with anxiety, especially related to national exams (von der Embse et al., 2018). Previous studies have shown strong evidence of the effectiveness of interventions aimed to tackle assessment anxiety (Hembree, 1988; Segool et al., 2013), such as the STEPS cognitive-behavioral intervention (Putwain et al., 2014, 2022; Putwain & Pescod, 2018). This multimodal approach, consisting of six sessions, teaches students about the effects of assessment anxiety, invites them to reflect on their beliefs and to adopt productive self-regulatory strategies in assessment situations. These programs may also have wider beneficial effects, helping to reduce panic disorder and generalized anxiety disorder (Putwain & von der Embse, 2021).

An alternative approach to preventing dropout lies in ensuring that learners have the resources to cope with the assessments and other learning requirements. According to Oger, Broc, Martin-Krumm, et al. (2022), the best way to reduce school burnout may not consist in the reduction of academic demands, but, rather, in offering students a wider range of resources (e.g., counseling, tutoring, active pedagogies, etc.). We believe that this approach could be even more beneficial if the teachers would use student feedback to verify how easily their requirements can be met by their students.

Academic research studies rarely use objective dropout indicators, which are only modestly related to self-reported dropout intentions (Eicher et al., 2014; Vallerand et al., 1997). Future research could extend the existing findings (Bask & Salmela-Aro, 2013) by examining the joint effects of burnout and test anxiety on dropout intentions and behavior, controlling for past GPA. Finally, the factors contributing to school dropout might not be limited to the school domain, but may also pertain to students' demographic characteristics, family structure, or low parental control (Gubbels et al., 2019; Paksi et al., 2023; Vinciguerra et al., 2020). Although variables related to the school context make it easy to develop interventions for schools, they may not be sufficient to explain the variance in dropout intentions. Individual risk factors, such as physical health problems or emotional disorders (i.e., depression, anxiety, etc.) could also play an important role in the dropout process (Filippello et al., 2019; Fortin et al., 2006; Gubbels et al., 2019; Paksi et al., 2023; Quiroga et al., 2013). Other important individual variables that could complete the model include intelligence and motivation (Kriegbaum et al., 2018). Thus, our findings could be replicated and extended in future studies.

## Limitations

Naturally, the present study has several limitations. First, similarly to the previous studies of dropout intentions (Alivernini & Lucidi, 2011; Balkis, 2018; Hardre & Reeve, 2003; Haugan et al., 2019; Rump et al., 2017), we only measured students' self-reported dropout intentions. The use of more comprehensive measures addressing the different stages of dropout intention implementation could potentially reveal stronger effects. Second, the limited sample size may have precluded us from discovering weaker effects and did not allow us to include a wider range of variables. Third, the design of our study did not allow us to address the dynamic nature of the dropout intention variable, which could be revealed by using repeated measurements during the year. We also could not control the pre-existing (past year's) individual differences in GPA and dropout intention. As a result, the findings do not allow us to make any causal inferences, for instance, whether the intention to drop out increases after a change in GPA or vice versa, and whether changes in these variables could lead to changes in test anxiety and burnout. The explanatory power of these variables may vary across the periods of schooling (Alivernini & Lucidi, 2011; Haugan et al., 2019) and might be stronger as test anxiety increases as national exams approach (von der Embse et al., 2018). Ideally, understanding the dynamics of the dropout process requires using a longitudinal design over a longer period of time that would include the national exams.
**Ethics Statements**

The research was conducted in line with the Declaration of Helsinki. The study protocol was approved by the research ethics committee of the University of Angers (ethics approval number UA-CER-2021-05).

**Funding**

This research was supported by l'Agence Nationale de la Recherche et de la Technologie (National Research and Technology Agency) and by l'Académie des Projets de Vie (Academy of Life Projects) [grant number 2020/0588].

**Authorship Contribution Statement**

Sperduto: Concept and design, data acquisition, data interpretation, writing, securing funding. Fenouillet: Concept and design, statistical analyse, writing. Boujon: Concept and design, data acquisition. Oger: Critical revision of manuscript. Martin-Krumm: Critical revision of manuscript. Osin: Statistical analyse, writing.

**References**

Abreu Alves, S., Sinval, J., Neto, L. L., Marôco, J., Gonçalves Ferreira, A., & Oliveira, P. (2022). Burnout and dropout intention in medical students: The protective role of academic engagement. *BMC Medical Education, 22*, Article 83.
https://doi.org/10.1186/s12909-021-03094-9

Alivernini, F., & Lucidi, F. (2011). Relationship between social context, self-efficacy, motivation, academic achievement, and intention to drop out of high school: A longitudinal study. *The Journal of Educational Research, 104*(4), 241-252.
https://doi.org/10.1080/00220671003728062

Balkis, M. (2018). Academic amotivation and intention to school dropout: The mediation role of academic achievement and absenteeism. *Asia Pacific Journal of Education, 38*(2), 257-270.
https://doi.org/10.1080/02188791.2018.1460258

Bask, M., & Salmela-Aro, K. (2013). Burned out to drop out: Exploring the relationship between school burnout and school dropout. *European Journal of Psychology of Education, 28*, 511-528. https://doi.org/10.1007/s10212-012-0126-5

Derakshan, N., & Eysenck, M. W. (2009). Anxiety, processing efficiency, and cognitive performance: New developments from attentional control theory. *European Psychologist, 14*(2), 168-176. https://doi.org/10.1027/1016-9040.14.2.168

Double, K. S., McGrane, J. A., & Hopfenbeck, T. N. (2020). The impact of peer assessment on academic performance: A meta-analysis of control group studies. *Educational Psychology Review, 32*, 481-509.
https://doi.org/10.1007/s10648-019-09510-3

Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. *British Journal of Psychology. 105*(3), 399-412.
https://doi.org/10.1111/bjop.12046

Eicher, V., Staerklé, C., & Clémence, A. (2014). I want to quit education: A longitudinal study of stress and optimism as predictors of school dropout intention. *Journal of Adolescence, 37*(7), 1021-1030.
https://doi.org/10.1016/j.adolescence.2014.07.007

Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. *Emotion, 7*(2), 336-353. https://doi.org/10.1037/1528-3542.7.2.336

Fagnant, A. (2022). *L'évaluation en classe, au service de l'apprentissage des élèves* [Classroom assessment to support student learning]. Centre National d'Étude des Systèmes Scolaires. https://bit.ly/3Xd5WbJ

Fenouillet, F., Prokofieva, V., Lorant, S., Masson, J., & Putwain, D. W. (2023). French study multidimensional test anxiety scale in relation to performance, age and gender. *Journal of Psychoeducational Assessment, 41*(7), 828-834.
https://doi.org/10.1177/07342829231186871

Filippello, P., Buzzai, C., Costa, S., & Sorrenti, L. (2019). School refusal and absenteeism: Perception of teacher behaviors, psychological basic needs, and academic achievement. *Frontiers in Psychology, 10*, Article 1471.
https://doi.org/10.3389/fpsyg.2019.01471

Findeisen, S., Brodsky, S., Michaelis, C., Schimmelpenningh, B., & Seifried, J. (2024). Dropout intention: a valid predictor of actual dropout? *Empirical Research in Vocational Education and Training, 16*, Article 10.
https://doi.org/10.1186/s40461-024-00165-1
Fiorilli, C., De Stasio, S., Di Chiacchio, C., Pepe, A., & Salmela-Aro, K. (2017). School burnout, depressive symptoms and engagement: Their combined effect on student achievement. *International Journal for Educational Research, 84*, 1-12. https://doi.org/10.1016/j.ijer.2017.04.001

Florin, A., Tricot, A., Chesné, J.-F., Piedfer-Quêney, L., & Simonin-Kunerth, M. (2023). L'évaluation en classe, au service de l'apprentissage des élèves. *Recommandations du jury*. Cnesco-Cnam. [Classroom assessment to support student learning. The jury's recommendations. Cnesco-Cnam.] Ministère de l'Éducation Nationale et de la Jeunesse. https://www.cnesco.fr/evaluation-en-classe/.

Fortin, L., Marcotte, D., Potvin, P., Royer, É., & Joly, J. (2006). Typology of students at risk of dropping out of school: Description by personal, family and school factors. *European Journal of Psychology of Education, 21*, 363-383. https://doi.org/10.1007/BF03173508

Fournier, Y., & Rakocevic, R. (2021). The European Union has set new education and training targets for 2030. *Ministère de l'Éducation Nationale et de la Jeunesse*. https://bit.ly/3XbNp0n

Fournier, Y., & Rakocevic, R. (2022). Objectifs éducation et formation 2030 de l'UE: où en est la France? [The EU's education and training objectives for 2030: where does France stand?]. Ministère de l'Éducation Nationale et de la Jeunesse. https://bit.ly/4dUKsal

Freeney, Y., & O'Connell, M. (2012). The predictors of the intention to leave school early among a representative sample of Irish second-level students. *British Educational Research Journal, 38*(4), 557-574. https://doi.org/10.1080/01411926.2011.563838

Gubbels, J., Van der Put, C. E., & Assink, M. (2019). Risk factors for school absenteeism and dropout: A meta-analytic review. *Journal of Youth and Adolescence, 48*, 1637-1667. https://doi.org/10.1007/s10964-019-01072-5

Hardre, P. L., & Reeve, J. (2003). A motivational model of rural students' intentions to persist in, versus drop out of, high school. *Journal of Educational Psychology, 95*(2), 347-356. https://doi.org/10.1037/0022-0663.95.2.347

Haugan, J. A., Frostad, P., & Mjaavatn, P.-E. (2019). A longitudinal study of factors predicting students' intentions to leave upper secondary school in Norway. *Social Psychology of Education, 22*, 1259-1279. https://doi.org/10.1007/s11218-019-09527-0

Hembree, R. (1988). Correlates, causes, effects, and treatment of test anxiety. *Review of Educational Research, 58*(1), 47-77. https://www.jstor.org/stable/1170348

Hervy, C., & Kaiser, O. (2023). *Un décrochage scolaire régional très localisé*. [Regional dropouts are highly localised]. Institut National de la Statistique et des Études Économiques. https://www.insee.fr/fr/statistiques/6959357.

Hoibian, S., & Millot, C. (2018). *Aider les jeunes à mieux identifier leurs gouts et motivations personnelles : Un levier pour améliorer l'orientation*. [Helping young people to better identify their personal tastes and motivations: a lever for improving guidance]. Ministère de l'Éducation Nationale et de la Jeunesse. https://hal.science/hal-03249689/document

Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. *Structural Equation Modeling, 6*(1), 1-55. https://doi.org/10.1080/10705519909540118

Isoard-Gautheur, S., Oger, M., Guillet, E., & Martin-Krumm, C. (2010). Validation of a French version of the athlete burnout questionnaire: In Competitive sport and physical education context. *European Journal of Psychological Assessment, 26*(3), 203-211. https://doi.org/10.1027/1015-5759/a000027

Jamovi. (n.d.). Jamovi [Computer software]. https://www.jamovi.org

Janosz, M., Le Blanc, M., Boulerice, B., & Tremblay, R. E. (2000). Predicting different types of school dropouts: A typological approach with two longitudinal samples. *Journal of Educational Psychology, 92*(1), 171-190. https://doi.org/10.1037/0022-0663.92.1.171

Kim, H.-Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. *Restorative Dentistry and Endodontics, 38*(1), 52-54. https://doi.org/10.5395/rde.2013.38.1.52

Kriegbaum, K., Becker, N., & Spinath, B. (2018). The relative importance of intelligence and motivation as predictors of school achievement: A meta-analysis. *Educational Research Review, 25*, 120-148. https://doi.org/10.1016/j.edurev.2018.10.001

Lebert-Charron, A., Boujut, E., Beaudouin, M., & Quintard, B. (2021). Du stress au burnout scolaire: définitions, déterminants psychosociaux, évaluation et pistes de prévention. [From stress to school burnout: definitions, psychosocial determinants, assessment and ways to prevent it]. *Pratiques Psychologiques, 27*(1), 57-69. https://doi.org/10.1016/j.prps.2019.02.002
Legault, L., Green-Demers, I., & Pelletier, L. (2006). Why do high school students lack motivation in the classroom? *Toward an understanding of academic motivation and the role of social support*. *Journal of Educational Psychology*, *98*(3), 567-582. https://doi.org/10.1037/0022-0663.98.3.567

Lowe, P. A. (2021). Examination of latent test anxiety profiles in a sample of U.S. adolescents. *International Education Studies, 14*(2), 12-20. https://doi.org/10.5539/ies.v14n2p12

Madigan, D. J., & Curran, T. (2021). Does burnout affect academic achievement? A meta-analysis of over 100,000 students. *Educational Psychology Review, 33*, 387-405. https://doi.org/10.1007/s10648-020-09533-1

Markus, K. A. (2012). Principles and practice of structural equation modeling by Rex B. Kline. *Structural Equation Modeling: A Multidisciplinary Journal, 19*(3), 509-512. https://doi.org/10.1080/10705511.2012.687667

Marôco, J., Assunção, H., Harju-Luukkainen, H., Lin, S.-W., Sit, P.-S., Cheung, K.-C., Maloa, B., Stepanović Ilic, I., Smith, T. J., & Campos, J. A. D. B. (2020). Predictors of academic efficacy and dropout intention in university students: Can engagement suppress burnout? *PLoS ONE*, *15*(10), Article e0239816. https://doi.org/10.1371/journal.pone.0239816

Martin-Krumm, C., Oger, M., Sarrazin, P., & Pelayo, P. (2011). Moderator effect of burnout in secondary school context. In I. Brdar (Ed.), *The human pursuit of well-being* (pp. 191-200). Springer. https://doi.org/10.1007/978-94-007-1375-8_16

Martin-Krumm, C. P., Sarrazin, P. G., & Peterson, C. (2005). The moderating effects of explanatory style in physical education performance: A prospective study. *Personality and Individual Differences, 38*(7), 1645-1656. https://doi.org/10.1016/j.paid.2004.09.023

Meylan, N., Doudin, P.-A., Antonietti, J.-P., & Stéphan, P. (2015). Validation française du School Burnout Inventory [School Burnout Inventory: A French validation]. *European Review of Applied Psychology, 65*(6), 285-293. https://doi.org/10.1016/j.erap.2015.10.002

Ministry of National Education and Youth. (2022). *L'Europe de l'éducation en chiffres 2022* [European education in figure 2022]. https://www.education.gouv.fr/EuropeEducation2022

Moran, T. P. (2016). Anxiety and working memory capacity: a meta-analysis and narrative review. *Psychological Bulletin, 142*(8), 831-864. https://doi.org/10.1037/bul00000051

Muthén, B. O., Muthén, L. K., & Asparouhov, T. (2017). *Regression and mediation analysis using Mplus*. Muthén & Muthén.

Muthén, L. K., & Muthén, B. O. (2017). *Mplus statistical analysis with latent variables: user's guide* (8th ed.). Muthén & Muthén.

National Centre for the Study of School Systems. (2017). *Comment agir plus efficacement face au décrochage scolaire? Dossier de synthèse*. [How can we tackle early school leaving more effectively? Summary file]. Ministère de l'Éducation Nationale et de la Jeunesse. https://bit.ly/3Xsypub

Oger, M., Broc, G., Martin-Krumm, C., Le Roux, F., Muller, A., & Tarquinio, C. (2022). Pupils' adaptability at school, a balance between demands and resources? *Frontiers in Education, 7*, Article 814376. https://doi.org/10.3389/feduc.2022.814376

Oger, M., Broc, G., Rotonda, C., Tarquinio, C., & Martin-Krumm, C. (2022). Adaptation et validation française de l'échelle Job Demands-Resources (JD-R) en contexte scolaire. [French adaptation and validation of the Job Demands-Resources (JD-R) scale in a school context]. *Revue Canadienne de l'Éducation, 45*(1), 184-226. https://doi.org/10.53967/cje-rce.v45i1.5071

Oger, M., Martin-Krumm, C., Fenouillet, F., Müller, A., Le Roux, F., Tarquinio, C., & Broc, G. (2022). Quality of life at school: between vulnerability and robustness of students. *Psychology in the Schools, 59*(8), 1686-1700. https://doi.org/10.1002/pits.22719

Oger, M., Martin-Krumm, C., & Tarquinio, C. (2020). Épuisement scolaire et qualité de vie, chimère ou réalité? [School exhaustion and quality of life, fantasy or reality?]. *Psychologie Française, 66*(4), 393-411. https://doi.org/10.1016/j.psfr.2020.11.001

Organisation for Economic Co-operation and Development. (2017). *PISA 2015 results (Volume III): Students' well-being*. OECD Publishing. https://doi.org/10.1787/9789264273856-en

Organisation for Economic Co-operation and Development. (2019). *PISA 2018 results (Volume II): Where all students can succeed*. OECD Publishing. https://doi.org/10.1787/b5fd1b8f-en

Paksi, B., Széll, K., & Fehérvàri, A. (2023). Empirical testing of a multidimensional model of school dropout risk. *Social Sciences, 12*(2), Article 50. https://doi.org/10.3390/socsci12020050
Papadogiannis, I., Poulooulos, V., Platis, N., Vassilakis, C., Lepouras, G., & Wallace, M. (2023). First grade GPA as a predictor of later academic performance in high school. *Knowledge, 3*(3), 513-524.
https://doi.org/10.3390/knowledge3030033

Peterson, C., & Barrett, L. C. (1987). Explanatory style and academic performance among university freshmen. *Journal of Personality and Social Psychology, 53*(3), 603-607. https://doi.org/10.1037/0022-3514.53.3.603

Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communication research. In A. F. Hayes, M. D. Slater & L. B., Snyder (Eds.), *Sourcebook of advanced data analysis methods for communication research* (pp. 13-54). Sage Publications, Inc. https://doi.org/10.4135/9781452272054.n2

Putwain, D., Chamberlain, S., Daly, A. L., & Sadreddini, S. (2014). Reducing test anxiety among school-aged adolescents: A field experiment. *Educational Psychology in Practice, 30*(4), 420-440.
https://doi.org/10.1080/02667363.2014.964392

Putwain, D. W., Pekrun, R., Rainbird, E., & Roberts, C. (2022). Cognitive-behavioral intervention for test anxiety: Could teachers deliver the STEPS program and what training would they require? In L. R. V. Gonzaga, L. L. Dellazzan-Zanon, & A. M. Becker da Silva (Eds.), *Handbook of stress and academic anxiety* (pp. 381-399). Springer.
https://doi.org/10.1007/978-3-031-12737-3_25

Putwain, D. W., & Pescod, M. (2018). Is reducing uncertain control the key to successful test anxiety intervention for secondary school students? Findings from a randomized control trial. *School Psychology Quarterly, 33*(2), 283-292.
https://doi.org/10.1037/spq0000228

Putwain, D. W., & von der Embse, N. P. (2021). Cognitive-behavioral intervention for test anxiety in adolescent students: do benefits extend to school-related wellbeing and clinical anxiety. *Anxiety, Stress and Coping, 34*(1), 22-36.
https://doi.org/10.1080/10615806.2020.1800656

Putwain, D. W., von der Embse, N. P., Rainbird, E. C., & West, G. (2020). The development and validation of a new Multidimensional Test Anxiety Scale (MTAS). *European Journal of Psychological Assessment, 37*(3), 236-246.
https://doi.org/10.1027/1015-5759/a000604

Quiroga, C. V., Janosz, M., Bisset, S., & Morin, A. J. S. (2013). Early adolescent depression symptoms and school dropout: Mediating processes involving self-reported Academic competence and achievement. *Journal of Educational Psychology, 105*(2), 552-560. https://doi.org/10.1037/a0031524

R Core Team. (n.d.). *The Comprehensive R Archive Network* [Computer software]. https://cran.r-project.org

Republic of France. (2024, September 9). *Code de l'éducation* [Education code]. https://bit.ly/4gndUYq

Rey, O., & Feyfant, A. (2014). *Évaluer pour (mieux) faire apprendre* [Assessing for (better) learning]. Dossier de veille de l'Ifé, 94. ENS de Lyon. https://bit.ly/4grof5x

Robson, D. A., Johnstone, S. J., Putwain, D. W., & Howard, S. (2023). Test anxiety in primary school children: A 20-year systematic review and meta-analysis. *Journal of School Psychology, 98*, 39-60.
https://doi.org/10.1016/j.jsp.2023.02.003

Rump, M., Esdar, W., & Wild, E. (2017). Individual differences in the effects of academic motivation on higher education students' intention to drop out. *European Journal of Higher Education, 7*(4), 341-355.
https://doi.org/10.1080/21568235.2017.1357481

Salmela-Aro, K., Kiuru, N., Leskinen, E., & Nurmi, J.-E. (2009). School-Burnout Inventory (SBI) reliability and validity. *European Journal of Psychological Assessment, 25*(1), 48-57. https://doi.org/10.1027/1015-5759.25.1.48

Salmela-Aro, K., Kiuru, N., Pietikäinen, M., & Jokela, J. (2008). Does school matter? The role of school context in adolescents' school-related burnout. *European Psychologist, 13*(1), 12-23. https://doi.org/10.1027/1016-9040.13.1.12

Samuel, R., & Burger, K. (2020). Negative life events, self-efficacy, and social support: risk and protective factors for school dropout intentions and dropout. *Journal of Educational Psychology, 112*(5), 973-986.
https://doi.org/10.1037/edu0000406

Scheunemann, A., Schnettler, T., Bobe, J., Fries, S., & Grunschel, C. (2022). A longitudinal analysis of the reciprocal relationship between academic procrastination, study satisfaction, and dropout intentions in higher education. *European Journal of Psychology of Education, 37*, 1141-1164. https://doi.org/10.1007/s10212-021-00571-z

Schnell, K., Ringeisen, T., Raufelder, D., & Rohrmann, S. (2015). The impact of adolescents' self-efficacy and self-regulated goal attainment processes on school performance—Do gender and test anxiety matter? *Learning and Individual Differences, 38*, 90-98. https://doi.org/10.1016/j.lindif.2014.12.008
Segool, N. K., von der Embse, N. P., Gallant, J., & Mata, A. D. (2013). Cognitive behavioral model of test anxiety in a high-stakes context: An exploratory study. *School Mental Health, 6*, 50-61. https://doi.org/10.1007/s12310-013-9111-7

Sellbom, M., & Tellegen, A. (2019). Factor analysis in psychological assessment research: Common pitfalls and recommendations. *Psychological Assessment, 31*(12), 1428-1441. https://doi.org/10.1037/pas0000623

Sim, M., Kim, S.-Y., & Suh, Y. (2022). Sample size requirements for simple and complex mediation models. *Educational and Psychological Measurement, 82*(1), 76-106. https://doi.org/10.1177/00131644211003261

Sinval, J., Oliveira, P., Novais, F., Almeida, C. M., & Telles-Correia, D. (2024). Correlates of burnout and dropout intentions in medical students: A cross-sectional study. *Journal of Affective Disorders, 36*, 221-230. https://doi.org/10.1016/j.jad.2024.08.003

Spalding, D. M., & Brown Nicholls, L. A. (2021). Towards understanding the impacts of anxiety on attention and visual working memory. *The Cognitive Psychology Bulletin, 6*(1), 34-41. https://doi.org/10.53841/bpscog.2021.1.6.34

Spielberger, C. D., & Vagg, R. P. (1995). Test anxiety: A transactional process model. In C.D. Spielberger, & P. R. Vagg (Eds.), *Test anxiety: Theory, assessment and treatment* (pp. 3-14). Taylor & Francis.

Steinmayr, R., Crede, J., McElvany, N., & Wirthwein, L. (2016). Subjective well-being, test anxiety, academic achievement: Testing for reciprocal effects. *Frontiers in Psychology, 6*, Article 1994. https://doi.org/10.3389/fpsyg.2015.01994

Vallerand, R. J., Fortier, M. S., & Guay, F. (1997). Self-determination and persistence in a real-life setting toward a motivational model of high school dropout. *Journal of Personality and Social Psychology, 72*(5), 1161-1176. https://doi.org/10.1037/0022-3514.72.5.1161

van Ginkel, J. R., Linting, M., Rippe, R. C. A., & van der Voort, A. (2020). Rebutting existing misconceptions about multiple imputation as a method for handling missing data. *Journal of Personality Assessment, 102*(3), 297-308. https://doi.org/10.1080/00223891.2018.1530680

Vansoeterstede, A., Lichté, J., Cappe, E., & Boujut, E. (2023). A systematic review of longitudinal changes in school burnout among adolescents: Trajectories, predictors, and outcomes. *Journal of Adolescence, 95*(2), 224-247. https://doi.org/10.1002/jad.12121

Vinciguerra, A., Nanty, I., Guillaumin, C., Rusch, E., Cornu, L., & Courtois, R. (2020). Les déterminants du décrochage dans l'enseignement secondaire: Une revue de littérature. [The determinants of dropping out in secondary education: A literature review]. *Psychologie Française, 66*(1), 15-40. https://doi.org/10.1016/j.psfr.2019.09.003

von der Embse, N., Barterian, J., & Segool, N. (2013). Test anxiety interventions for children and adolescents: a systematic review of treatment studies from 2000-2010. *Psychology in the Schools, 50*(1), 57-71. https://doi.org/10.1002/pits.21660

von der Embse, N., & Hasson, R. (2012). Test anxiety and high-stakes test performance between school settings: Implications for educators. *Preventing School Failure, 56*(3), 180-187. https://doi.org/10.1080/1045988X.2011.633285

von der Embse, N., Jester, D., Roy, D., & Post, J. (2018). Test anxiety effects, predictors, and correlates: A 30-year meta-analytic review. *Journal of Affective Disorders, 227*, 483-493. https://doi.org/10.1016/j.jad.2017.11.048

Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. *Electronic Commerce Research and Applications, 9*(6), 476-487. https://doi.org/10.1016/j.elerap.2010.07.003