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Mindset profiles of secondary school students: Associations with academic achievement, motivation and school burnout symptoms

March 2024
British Journal of Educational Psychology 94(3):1-21

DOI:10.1111/bjep.12676

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CC BY 4.0

Authors:

Sibel Altikulac

Vrije Universiteit Amsterdam

Tieme WP Janssen

Vrije Universiteit Amsterdam

Junlin Yu

University of Helsinki

Smiddy Nieuwenhuis

Vrije Universiteit Amsterdam

Show all 5 authorsHide

Background According to Dweck's mindset theory, implicit beliefs (a.k.a. mindset) have an organizing function, bringing together mindset, achievement goals and effort beliefs in a broader meaning system. Two commonly described meaning systems are a growth‐mindset meaning system with mastery goals and positive effort beliefs, and a fixed‐mindset meaning system with performance goals and negative effort beliefs. Aims Because of assumed heterogeneity within these two meaning systems, we aim to (1) examine multiple‐mindset profiles based on mindset, achievement goals and effort beliefs, by using a data‐driven person‐oriented approach, and (2) relate these different profiles to several outcome measures (academic achievement, motivation and school burnout symptoms). Sample Self‐report questionnaire data were collected from 724 students (11.0–14.7 y.o.; 46.7% girl; 53.3% boy; M age = 12.8 y.o.). Methods Latent profile analysis was conducted using mindset, achievement goals and effort beliefs. Results Four profiles were revealed: one fixed‐mindset profile and three growth‐mindset profiles, which differed in their performance goal levels (low, moderate and high). Growth‐mindset students with low‐ or moderate‐performance goals had more advantageous outcomes, for example, higher math grades and lower school burnout symptoms, compared to growth‐mindset students with high‐performance goals. Fixed‐mindset students had the least advantageous outcomes, for example, lower grades, less intrinsic motivation and more school burnout symptoms. Conclusions Our study emphasizes the importance of taking a holistic approach when examining mindset meaning systems, revealing the importance of the level of performance goals and including multiple academic outcomes.

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Br J Educ Psychol. 2024;00:1–21.

wileyon linelibrar y.com/journal/bjep

Received: 27 September 2022

Accepted : 28 February 2024

DOI: 10.1111/b jep. 12 676

ARTICLE

Mindset profiles of secondary school students:

Associations with academic achievement, motivation

and school burnout symptoms

Sibel Altikulaç1 | Tieme W. P. Janssen1 | Junl i n Yu 2 |

Smiddy Nieuwenhuis1 | Nienke M. Van Atteveldt1

This is an open access artic le under the ter ms of the Creative Commons At tribut ion Licen se, which permits use, dist ribution and reproduct ion

in any medium , provided the or igi nal wor k is properly cite d.

Sibel A ltikulaç and Tieme W. P. Janssen share d first authorship.

1Vrije Univer siteit A msterd am, A msterd am, T he

Netherlands

2Facult y of Educational Scienc es, Universit y of

Helsinki, Helsinki, Finland

Correspondence

Sibel A ltikulaç, Van der Bo echorst straat 7,

room MF- D526, 1081 BT Amste rdam , The

Netherlands.

Email: s.altikulac@vu.nl

Funding informat ion

Europe an Rese arch Counci l, Grant/Award

Number : 716736; Academy of Fi nland,

Grant/Award Number: 354742

Abstract

Background: According to Dweck's mindset theory, im-

plicit beliefs (a.k.a. mindset) have an organizing function,

bringing together mindset, achievement goals and effort

beliefs in a broader meaning system. Two commonly de-

scribed meaning systems are a growth- mindset meaning

system with mastery goals and positive effort beliefs, and a

fixed- mindset meaning system with performance goals and

negative effort beliefs.

Aims: Because of assumed heterogeneity within these two

meaning systems, we aim to (1) examine multiple- mindset

profiles based on mindset, achievement goals and effort be-

liefs, by using a data- driven person- oriented approach, and (2)

relate these different profiles to several outcome measures

(academic achievement, motivation and school burnout

symptoms).

Sample: Self- report questionnaire data were collected

from 724 students (11.0–14.7 y.o.; 46.7% girl; 53.3% boy;

Mage = 12.8 y.o.).

Methods: Latent profile analysis was conducted using

mindset, achievement goals and effort beliefs.

Results: Four profiles were revealed: one fixed- mindset

profile and three growth- mindset profiles, which dif-

fered in their performance goal levels (low, moderate and

high). Growth- mindset students with low- or moderate-

performance goals had more advantageous outcomes, for

example, higher math grades and lower school burnout

symptoms, compared to growth- mindset students with

ALTIKULAÇ et a l.

INTRODUCTION

The implicit beliefs students hold about the malleability of personal attributes, such as their intelligence,

can have a profound impact on how they respond to challenges and setbacks during learning situations,

ultimately hampering or enhancing flourishing in school (Burnette et al., 2022). On a continuum, some

students believe their abilities are unchangeable and ‘carved in stone’ (fixed mindset), while others

believe their abilities are more dynamic and can be developed (growth mindset). A crucial aspect of

these implicit theories (Dweck, 1999; Dweck & Leggett, 198 8) is that they have an organizing func-

tion, bringing together goals, beliefs and behaviours in a meaning system (Hong et al., 1999; Molden &

Dweck, 2006) – a lens through which students construe the meaning of events they encounter.

Integral to these meaning systems are achievement goals and effort beliefs (Dweck & Yeager, 2019). In

fact, mindset theory was born out of achievement goal theory and attribution theory – with the latter

describing attributions of success and failure to (lack of) effort or ability (Dweck, 2017). Students with a

fixed mindset f ind it important to validate or prove their ability (performance goals) and view effort as

an indicator of lacking ability (negative effort beliefs). In contrast, students with a growth mindset find

it important to develop and improve their ability (mastery goals) and see effort as a tool to realize this

(positive effort beliefs). However, recent evidence draws a more nuanced picture of how these meaning

systems are organized (e.g., Liu, 2021; Yu & McLellan, 2020), needing further investigation.

Person- oriented approach to study mindset meaning systems

Several variable- centred studies, which describe relations between variables (Laursen & Hoff, 2006),

show heterogeneity in the associations between the core constructs in the meaning systems. Generally,

consensus is found for the association between mindset and effort beliefs, in which a growth mindset

is associated with positive effort beliefs and a fixed mindset with negative effort beliefs (e.g., Blackwell

et al., 2007; Dweck & Yeager, 2019; Miele et al., 2013; Tempelaar et al., 2015). Less consensus is found

for the association between mindset and achievement goals. First of all, mindset and achievement goals

are found to be only weakly correlated (e.g., rs = .19 and − .15 for mastery and performance goals with

growth mindset, respectively; Burnette et al., 2 013; Burgoyne et al., 2020). Another study showed that

achievement goals only mediated the relation between mindset and academic achievement for fixed-

mindset students, but not for growth- mindset students (Cury et al., 2006). Additionally, it has been

suggested that growth- mindset students may not solely endorse mastery goals, but can simultaneously

endorse performance- approach goals (e.g., Chen & Wong, 2015), which relate to better academic per-

formance (e.g., Bouffard et al., 1995; Dupeyrat & Mariné, 2001).

One possible method to investigate these inconsistencies is a data- driven person- oriented approach. With

this approach, participants are classified into more homogeneous subgroups of individuals with similar

high- performance goals. Fixed- mindset students had the

least advantageous outcomes, for example, lower grades, less

intrinsic motivation and more school burnout symptoms.

Conclusions: Our study emphasizes the importance of tak-

ing a holistic approach when examining mindset meaning

systems, revealing the importance of the level of perfor-

mance goals and including multiple academic outcomes.

KE YWORDS

academic achievement, latent prof ile analysis, mindset, motivation,

school burnout symptoms

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MIN DSET PROF ILES OF SECON DARY SCHOOL STU DENT S

responses on multiple constructs (Lanza & Rhoades, 2013), allowing for uncovering unexpected ‘pro-

files’, which can help to further develop and refine a theory ( Vansteenkiste et al., 2009). It can be argued

that this better fits with the notion that mindset is part of a complex meaning system with intercon-

nected beliefs that jointly affect learning outcomes (Hiver & Papi, 2 019).

Only a few studies have used a data- driven person- oriented approach to identify mindset meaning

system profiles. A study of secondary school students (Chen & Tutwiler, 2017 ) revealed two profiles in

the context of a science class: a growth- mindset profile and a moderate profile instead of a fixed profile.

Those in the growth- mindset profile were more confident in self- regulating their learning and were less

anxious about science. Another study of secondary school students (Yu & McLellan, 2020) found not

only two mindset profiles in line with the dichotomous growth and fixed meaning systems but also two

formerly hidden profiles: a growth- mindset profile in which students simultaneously endorsed mastery

and performance goals, and a disengaged profile in which students had a fixed mindset with lower levels

of performance goals. Girls were more commonly found in the two growth profiles. Furthermore, a

study into mindset profiles among university- level foreign language learners (Lou et al., 2021) revealed

three mindset profiles: a growth- mindset profile, a fixed- mindset profile and a mixed- mindset profile

in which students endorsed both mindsets and multiple achievement goals. Self- reported course en-

gagement decreased stepwise among growth, mixed and fixed profiles, but gender was not related to

the profiles. Finally, a growth- mindset intervention study in secondary school students ( Janssen & van

Atteveldt, 2022) revealed three profiles: a fixed- mindset profile and two growth profiles. Students in the

growth competitive profile endorsed mastery and performance goals, and students in the growth non-

competitive profile endorsed only mastery goals. All four studies found that students in more growth-

oriented profiles received higher grades.

A few intermediate conclusions can be drawn from these studies: (1) mindset- meaning systems are

more complex and diverse than theorized; (2) mindset- meaning systems may be context dependent,

considering the variation in the number and types of profiles among different person- oriented studies;

and (3) a profile in which multiple achievement goals are endorsed is consistently found, which is in line

with similar studies within the achievement goal theory literature (Niemivirta et al., 2019; Wormington

& Linnenbrink- Garcia, 2017 ). Additional person- oriented research is needed to get a better understand-

ing of the context specificity and replicability of mindset meaning system profiles, and their relations

with school outcomes. Associations between mindset profiles and school outcomes may be stronger

than previously found in variable- centred research, since unresolved heterogeneity in meaning systems

may mask associations with outcomes in variable- centred analyses, or even lead to inconsistent results.

This may be amplified by the possibility that complex meaning systems as a whole predict outcomes,

rather than any constituent part by itself (for an example, see Lou et al., 2021). In the following section,

we will briefly discuss what is currently known about relations between mindset and relevant school

outcomes: academic achievement, motivation and well- being.

Heterogeneity in the relations between mindset and academic achievement

In their meta- analysis, Sisk et al. (2018) found that the associations between mindsets and academic

achievement are weak and inconsistent, yet slightly higher for at- risk populations, such as low achievers

and students from lower socioeconomic status (SES) backgrounds. Important to note in the context of

the current study is that in the Netherlands, students' SES backgrounds are related to their educational

track. Students from lower SES backgrounds are more often pursuing pre- vocational education, while

students from higher SES backgrounds are more often pursuing pre- university education (e.g., Bol

et al., 2 014). Thus, when examining associations between mindset and academic achievement, educa-

tional track should be taken into account as a proxy for SES.

Another source of heterogeneity in the mindset literature might be school subjects. Although stu-

dents with a growth mindset and mastery goals tend to achieve better grades than students with a fixed

mindset and performance goals (e.g., De Castella & Byrne, 2015; Dweck & Yeager, 2019), the positive

ALTIKULAÇ et a l.

effects of having a growth mindset appear to be more pronounced in challenging subjects, such as math

(e.g., Blackwell et al., 2007). This is likely because math achievement is typically perceived as a result of

‘raw’ or ‘innate’ abilities (Leslie et al., 2015). Gender may play a role here as well, as girls are often ste-

reotyped to be less capable than boys in STEM subjects (Law et al., 2021), which in turn can affect the

type of feedback they receive and the attributions they make (Dweck & Bush, 1976; Mok et al., 2011).

Additionally, the study of Janssen et al. (2022) showed that although growth- mindset endorsement did

not differ between boys and girls on average, the relation between growth mindset and mental effort

was dependent on gender during an effortful math task.

The need for considering school- related outcome measures beyond grades

Mindset research, including person- oriented research, has primarily focused on differences in academic

outcomes and less on motivational and well- being outcomes. Students' psychological need satisfaction is an

important factor for development at school and in life (Duraiappah et al., 2022; Organisation for Economic

Co- operation and Development Staff, 20 17 ), which affects motivation. A growth mindset is associated

with motivation, with a slightly stronger association for at- risk students (Sarrasin et al., 2018). More spe-

cifically, intrinsic motivation is positively related, and external regulation – the most controlled form of

extrinsic motivation – is negatively related to growth mindset (e.g., Liu, 2021; Renaud- Dubé et al., 2015;

Zhao et al., 2018). However, intrinsic motivation can be both negatively or positively related to performance

goals, depending on the meaning system they are embedded in (e.g., Molden & Dweck, 2000). Most Western

schooling systems use performance- based assessment (Biesta, 2015; Hattie & Donoghue, 2016), which in

part affects the decline in students' motivation in their education (Scherrer & Preckel, 2019).

Students' development at school and in life is also dependent on their f lourishing or well- being

(Duraiappah et al., 2022; Organisation for Economic Co- operation and Development Staff, 2017 ),

which is a state ‘in which every individual realizes his or her own potential, can cope with the normal

stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his com-

munity’ (World Health Organization, 2005). Academic well- being is related to more adaptive behaviour

(Dweck & Leggett, 1988), especially when facing setbacks (Burnette et al., 2020). However, when facing

continually high demands at school, some students might consequently have to deal with school- related

burnout symptoms (Slivar, 2001). School burnout is a state of negative well- being of students regarding

school, with diverse symptoms categorized as ‘exhaustion at school’, ‘cynicism towards the meaning of

school’ and ‘sense of inadequacy at school’ (Salmela- Aro et al., 2009). By measuring the extent to which

students can (not) cope with the stress imposed by school, which is a regular form of stress in adolescent

life, students' state of well- being can be determined. School burnout symptoms can therefore be used

as a proxy for academic well- being. Previous studies show that a growth mindset could be a protective

factor against school burnout (Kim, 2020; Nieuwenhuis et al., 2023) and other types of psychological

distress (Burnette et al., 2020).

Study overview and hypotheses

In the current study, we aim to (1) examine mindset profiles in a sample of 724 young adolescents based

on mindset (De Castella & Byrne, 2015; Dweck, 1999), effort beliefs (Blackwell et al., 2007) and achievement

goals (Elliot & Murayama, 2008), by using latent profile analysis (LPA; Muthén & Muthén, 1998 –2 017 );

and (2) examine the associations between these profiles and school outcomes, including academic

achievement (GPA and math grades), extrinsic and intrinsic motivation (Deci et al., 1992 ; Ryan &

Connell, 1989) and academic well- being (school burnout symptoms; Salmela- Aro et al., 2009), and co-

variates gender and educational track (as a proxy for SES).

Based on prior variable- centred and person- oriented research we expect to find multiple data- driven

profiles (Hypothesis 1), of which two profiles correspond with the two ‘classic’ fixed- and growth- mindset

MIN DSET PROF ILES OF SECON DARY SCHOOL STU DENT S

meaning systems (Hypothesis 1a), and one or more hidden profiles that do not correspond with these

meaning systems (Hypothesis 1b):

• Fixed- mindset prof ile (Hypothesis 1a): relatively lower e ndorsement of growth- mi ndset, higher- per formance

approach and avoidance goals, lower mastery approach and avoidance goals and negative effort be-

liefs (Dweck, 2017; Dweck & Yeager, 2019; Janssen & van Atteveldt, 2022; Lou et al., 2021; Yu &

McLellan, 2020)

• Growth- mindset profile (Hypothesis 1a): relatively higher endorsement of growth- mindset, lower-

performance approach and avoidance goals, higher mastery approach and avoidance goals and pos-

itive effort beliefs (Chen & Tutwiler, 2017; Dweck, 2017; Dweck & Yeager, 2019; Janssen & van

Atteveldt, 2022; Lou et al., 2021; Yu & McLellan, 2020)

• One or more hidden profiles (Hypothesis 1b), for example, but not exclusively;

• Growth- mindset profile with multiple achievement goals: relatively higher endorsement of growth- mindset,

higher- performance approach and avoidance goals, higher mastery approach and avoidance goals,

and positive effort beliefs (Janssen & van Atteveldt, 2022; Yu & McLellan, 2020)

• Mixed profile: no clear endorsement of growth- mindset, higher- performance approach and avoid-

ance goals, higher mastery approach and avoidance goals, and no clear endorsement of positive

effort beliefs (Lou et al., 2021)

Next, we expect the profiles to differ in school outcomes (Hypothesis 2). A consistent finding in pre-

vious person- oriented studies is higher grades in growth profiles compared to other profiles (Chen &

Tut w i l e r , 2017; Lou et al., 2021; Yu & McLellan, 2020; Hypothesis 2a). The literature suggests that the

direction of the association between mindset and motivation may depend on the meaning system it is

embedded in (Molden & Dweck, 2000). Therefore, although we expect differences between profiles,

we have no specific expectations regarding the direction of the relations between the profiles and ac-

ademic motivation (Hypothesis 2b). Concerning academic well- being, we expect more school burnout

symptoms not only in a fixed- mindset profile but also in a potential mixed profile, or profile with mul-

tiple achievement goals (including performance goals) (Hypothesis 2c), which might reflect adaptation to

high- performance demands experienced in schools (Niemivirta et al., 2019). Finally, we expect gender

differences in profile membership (Hypothesis 2d), with more girls in a growth- mindset profile (Yu &

McLellan, 2020), and a potential effect of educational track (Hypothesis 2e), which is related to SES in the

Netherlands (e.g., Bol et al., 2 014).

METHOD

Participants and procedure

First- year secondary school students between 11.0 and 14.7 years old from seven schools in the

Netherlands participated in this study (N = 771). To increase generalizability, we included students

from several educational tracks. In Dutch secondary schools, educational tracks that are present are

(1) VMBO, which refers to pre- vocational education; (2) HAVO, which refers to higher general sec-

ondary education; and (3) VWO and V WO+, which refers to pre- university education. In the first

year(s) of secondary school, some educational track classes are combined. We therefore categorized

students' educational track based on their class; (1) students in a VMBO class (n = 119), (2) students in

a HAVO class (n = 311) and (3) students in a VWO(+) class (n = 294). The students participated in the

BRAINBELIEFS project. Active consent was provided by the participants and their parents before

completing an online questionnaire with Qualtrics software (Qualtrics, Provo, UT, USA. https:// www.

qualt rics. com) at home or school between April 2018 and June 2019. Participants who did not complete

the full questionnaire (6% of the total sample) were excluded from the analyses (n = 47; 46 .8% girl;

53.2% boy; Mage = 13.2 year s; SDage = 0.5 years). The final analyses were conducted with 724 participants

ALTIKULAÇ et a l.

(46.7% girl; 53.3% boy; Mage = 12.8 years; SDage = 0.5 ye ars), see Table 1 for descriptive statistics. All

procedures were approved by the local ethics committee of the Faculty of Behavioural and Movement

Sciences, Vrije Universiteit Amsterdam. This study was conducted in accordance with the Declaration

of Helsinki.

Materials

Students completed measures of mindset, effort beliefs, achievement goals, extrinsic and intrinsic moti-

vation and school burnout symptoms. Items assessing extrinsic regulation and intrinsic motivation were

rated on a 4- point Likert scale (1 = Totally disagree to 4 = Totally agree), and all other items were rated on

a 6- point Likert scale (1 = Totally disagree to 6 = Totally agree). All the original and Dutch- translated items

can be found in Appendix S1: Tables S1–S5. To indicate the reliabilities of the scales, omega coefficients

were estimated because they are robust to varying factor loadings (Flora, 2020).

Indicator variables

Mindset

Students' mindset about the malleability of their intelligence was measured using the translated

self- theory scale of the 8- item Implicit Theories of Intelligence Scale (De Castella & Byrne, 2015;

Dweck, 1999). The scale consists of four growth- oriented and four recoded fixed- oriented items. A

higher mean score on this scale corresponded to a stronger growth mindset. The scale had a good in-

ternal consistency, ω = .84.

Effort beliefs

Whether students believe that their abilities can improve with increased effort (positive effort), or that

effort is negatively related to one's ability (negative effort), was measured using the translated, nine- item

Effort Beliefs scale (Blackwell, 2002; Blackwell et al., 2007). The scale consists of four positive effort

items and five recoded negative effort items. A higher mean score indicated that students believed that

their abilities could improve with increased effort. Based on the CFA, we excluded one positive effort

item and one negative effort item, see Appendix S1. The internal consistency of the seven- item scale was

acceptable, ω = .69, and comparable to the reliabilities reported in the original study (α = .60 and .79;

Blackwell, 2002).

Achievement goals

An adapted a nd translated ver sion of the rev ised 2 × 2 Achievement Goal S cale (El liot & Murayama, 2008)

was used to examine students' achievement goals. In total, 12 items were used to represent the four

TABL E 1 Descript ive stat istics of age in years, and number of participants per educational t rack, per gender (boys

n = 386, g irls n = 338 ).

Boys Girls Tota l

Age mean (SD) in years 12.8 (0.47) 12.8 (0.42) 12.8 (0.45)

Education track

VMBO (pre- vocational education) 55 64 119

HAVO (higher general educat ion) 167 14 4 311

VWO (pre- university educat ion) 164 130 294

Note: The Dutch schooling system at secondary sc hool is divided into V MBO ( pre- vocational education), H AVO (school of higher gener al

seconda ry educ ation) a nd VWO ( pre- un iversity education). In t he first year(s), combinat ion classes of VM BO/HAVO or HAVO/VWO are

present as well.

MIN DSET PROF ILES OF SECON DARY SCHOOL STU DENT S

subscales with three items each: performance approach, performance avoidance, mastery approach and

mastery avoidance. A higher mean score on the subscales indicated that students were more oriented

towards the represented goal. The internal consistency of the subscales was moderate to good, ω = .79,

.89, .70 and .53 for performance approach, performance avoidance, mastery approach and mastery

avoidance goals respectively. The moderate internal consistency of the mastery avoidance goals subscale

is comparable to the reliability of the subscale in the meta- analysis of Strunk et al. (2021), ω = .54.

Outcome variables

Academic achievement

Students' academic achievement was measured by the retrieval of their grade point average (GPA),

including Dutch, English, math, geography and history. The GPA scale ranges from 1 (lowest) to 10

(highest). Students' mean grades are based on students' grades from the start of the school year until the

period around students' completion of the questionnaire.

Extrinsic and intrinsic motivation

Students' motivation was measured by a translated, short version of the translated Self- Regulation

Questionnaire – Academic (SRQ- A; Deci et al., 1992; Ryan & Connell, 1989). The scale consists of four

subscales, of which only the ‘external regulation’ subscale (five items; most controlled (extrinsic) form

of motivation) and ‘intrinsic motivation’ subscale (three items; most autonomous form of motivation;

Ryan & Deci, 2000) were used in this study. A higher mean score on the subscales indicates that students

endorsed the represented motivational strategy. The internal consistency was moderate for external

regulation (ω = .58) and acceptable for intrinsic motivation (ω = .78). The moderate internal consistency

of the external regulation subscale is comparable to the reliability reported in the original study of Ryan

and Connell (1989); α = .62 .

School burnout

The translated nine- item School Burnout Inventory (SBI; Salmela- Aro et al., 2009) was used to examine

students' school burnout symptoms, as a negative indicator of students' well- being at school. The scale

consists of four ‘exhaustion at school’ items, three ‘cynicism towards the meaning of school’ items and

two ‘sense of inadequacy at school’ items. As suggested by Salmela- Aro et al. (2009), a summary score

of the three subscales can be used to measure overall school burnout and has been used as such (e.g.,

Fiorilli et al., 2020; Janssen & van Atteveldt, 2023; Nieuwenhuis et al., 2023). Given our interest in the

overall level of burnout rather than differences across the three symptoms, a total mean score was com-

puted in which a higher score indicated that students experienced more school burnout symptoms (and

thus less well- being). The internal consistency was good, ω = .84.

Covariates

Since gender differences in mindset studies have been found (e.g., Dweck, 1999; Yu & McLellan, 2020)

and no consensus has yet been reached about the effect of students' educational track on mindset (e.g.,

Glerum et al., 2020; Pozo Sánchez et al., 2019), students' gender and educational track were added in the

final model as covariates. This information was collected in the online questionnaire.

Statistical analyses

Before conducting the analyses, data exploration was done using IBM SPSS Statistics 26 (IBM

Corp, 2019). All other analyses were conducted in Mplus Version 8.4 (Muthén & Muthén, 1998 –2017),

ALTIKULAÇ et a l.

with α = .05. First, factor structures of the motivational variables were verified using confirmatory fac-

tor analysis (CFA; e.g., Thompson & Daniel, 1996 ). Second, latent profile analyses (LPAs; Muthén &

Muthén, 1998 –2017) were conducted, testing the optimal number of profiles based on mindset, effort

beliefs and the four achievement goals. Since the sample consists of students from different classes and

schools, the intraclass correlations (ICCs) of the variables with class and school as grouping variables

were computed. For the purpose of our study, the ICCs should be close to 0, since the LPA variables

should not correlate to students' classes or school. Therefore, values below 0.05 will be preferable and

values above 0.05 will be considered as non- negligible. Next, differences between profiles in school

outcomes – academic achievement, school burnout symptoms and academic motivation – were analysed

using the automatic BCH method (Bolck et al., 2004). Gender and educational track were incorporated

as predictors of the mindset profiles.

Confirmatory factor analysis (CFA)

The factor structure of the variables was verified using CFA (e.g., Thompson & Daniel, 1996) for each

construct separately. A good model fit was based on a Comparative Fit Index (CFI) value of 0.95 or

higher, a 0.06 or lower value of root mean square error of approximation (RMSEA) and a value of 0.08

or lower of the standardized root mean square residual (SRMR; Hu & Bentler, 1999). Additionally,

items' factor loadings were used to check items' contribution to the model. Even though statistical

theory suggests removing all factor loadings below .400 (Hair Jr. et al., 1998), we also tried to stay as true

as possible to the validated questionnaire structures of our variables. Therefore, we decided to remove

only the items with low (<.300) loadings on the assigned component, since these items had very little

contribution to the model.

Latent profile analyses (LPA)

To examine the first two hypotheses, the model with the optimal number of latent profiles was deter-

mined by taking the statistical indicators (Nylund et al., 2007), elbow points (Petras & Masyn, 2010),

meaningfulness (profiles with <5% of the cases were left out) and interpretability of the solutions into

account. Additionally, when a solution with one additional profile did not add meaningful information

to the previous solution, the solution with one less profile was preferred (Morin et al., 2016 ). A better

fitting model has lower values on the Akaike information criteria (AIC), Bayesian information criteria

(BIC) and sample- size- adjusted BIC (SABIC); the p- value of the Bootstrap likelihood ratio test ( pBLRT)

should be significant; and the entropy value is as close to 1, representing greater precision (Celeux &

Soromenho, 1996). We tested multiple models, ranging from two to seven profiles, to determine the

optimal profile solution. For visualization purposes, we transformed the data based on the proportion

of maximum scaling (‘POMS’) method, maintaining the proportions of the absolute distances between

the observed responses (Moeller, 2 015). With this method, each scale is transformed from 0 (=minimal

possible) to 1 (=maximum possible), by first ranging the scale from 0 to the highest value and then di-

viding the scores by the highest value (Moeller, 2015).

Outcomes and predictors of latent profiles

To examine the third and fourth hypotheses regarding the associations of different profiles with well-

being, motivation and academic achievement, the automatic BCH method was used (Bolck et al., 2004).

Additionally, to examine whether certain student characteristics were associated with a certain profile

membership, we compared the profiles based on gender and educational track, using the R3STEP com-

mand (Asparouhov & Muthén, 2 014).

MIN DSET PROF ILES OF SECON DARY SCHOOL STU DENT S

RESULTS

Mindset- related profiles

First, multiple CFAs were conducted to verify the factor structure of the variables, and the fit indices

supported the hypothesized factor structures, see Appendix S1 and Tables S6 and S7 for the results.

Next, ICCs of the LPA variables (mindset, effort beliefs and the four achievement goals) with class

and school as grouping variables were computed. The ICCs on the class level were between 0.006 and

0.026, and were between 0.003 and 0.023 on the school level, suggesting only small effects of class

and school membership. To determine the best- fitting LPA model, solutions with two to seven latent

profiles were examined. The AIC, BIC and SABIC values continued to decrease with each additional

profile, the BLRT was significant for all solutions and the entropy was highest for the two- profile solu-

tion, see Table 2. Examination of the elbow plots (AIC, BIC, SABIC and loglikelihood) showed that the

increased model fit first f lattened at three and again at five profiles (see Appendix S1, Figure S1). We

therefore explored the meaningfulness and the interpretability of three- to f ive- profile solutions (see

Appendix S1, Figure S2). The fourth profile was distinct from the three previous profiles, whereas add-

ing the fifth profile resulted in splitting one profile into two profiles, creating a profile with less than 5%

of the cases. Therefore, the four- profile solution was adopted, with an acceptable level of classification

accurac y (entropy = 0.79).

The four profiles were labelled as (1) fixed- mindset (n = 62) goals; (2) growth- mindset – low-

performance goals (n = 194); (3) growth- mindset – moderate- performance goals (n = 30 0); and (4)

growth- mindset – high- performance goals (n = 168), see Figure 1 (and see Figure S3 in Appendix S1

for a bar graph with z- scores). In line with our first hypothesis, we found a fixed- mindset profile

showing below- average levels of growth mindset, positive effort beliefs and all four achievement

goals ( fixed mindset). Next, we found three profiles with a growth mindset. The first growth- mindset

profile is in line with the first hypothesis, in which students displayed a distinct preference for

mastery goals compared to performance goals (growth- mindset – low- performance goals). In line with

our second hypothesis, profiles in which students endorsed multiple achievement goals were found.

Students in the growth- mindset – moderate- performance goals profile showed a growth mindset, positive

effort beliefs, mastery goals and moderate levels of performance goals. In the final profile, students

had a growth mindset, average positive effort beliefs and high levels of all four achievement goals

(growth- mindset – high- performance goals).

Outcomes of latent profiles

The next aim was to examine the associations of these different profiles with academic achievement,

motivation and well- being (school burnout symptoms), see Table 3 and Figure 2 for all means across

profiles. First, the ICCs for the outcome variables with class and school as grouping variables were com-

puted. The ICCs on the class level for GPA and math grade were non- negligible (0.113 and 0.174, respec-

tively), whereas the ICCs of school burnout and academic motivation were modest (between 0.009 and

0.028). Due to the non- negligible ICCs of academic achievement, we accounted for class membership in

the analysis using the T YPE = COMPLEX command.

For academic achievement, the omnibus test for an overall difference across profiles on academic

achievement (GPA) was significant, χ2 (3) = 8.04, p < .05. In line with our third hypothesis, suggest-

ing higher grades for growth- mindset profiles compared to other profiles, pairwise comparisons

showed that the fixed- mindset profile had lower GPA scores compared to the growth- mindset – low-

performance goals profile and the growth- mindset – moderate- performance goals profile. Next, the omnibus

test for an overall difference across profiles on math grade was significant as well, χ2 ( 3) = 18.36,

p < .001. Pairwise comparison showed that the growth- mindset – low- performance goals and growth- mindset

– moderate- performance goals profiles had higher math grades compared to the fixed- mindset profile, and

ALTIKULAÇ et a l.

TABL E 2 Fit indices for latent profile analysis.

Number of profiles Loglikelihood # Free parameters AIC BIC SABIC pBLRT Entropy

2−5370.637 19 10 779. 274 10866.385 10806.054 <.001 .822

3−5259.330 26 10570.660 10689.864 10607.306 <.001 .819

4−5186 .910 33 10439.820 10591.118 10486.334 <.001 .793

5−5129.59 9 40 10339.197 10522.589 10395.577 <.0 01 .809

6−5093.94 0 47 10281.881 10 497. 366 10348.12 7 <.001 .808

7−50 63.714 54 10235.428 104 83. 007 10311.5 41 <.001 .808

Note: Values i n bold indicate the sele cted model.

Abbreviation s: AIC , Aka ike information cr iteria; BIC, Bayesia n infor mation criter ia; SABIC, sample- size- adjusted BIC; pB LR T, p- value Bootstrapped likeli hood rat io test.

MIN DSET PROF ILES OF SECON DARY SCHOOL STU DENT S

that the growth- mindset – low- performance goals profile had higher math grades compared to the growth-

mindset – high- performance goals profile.

Regarding motivation, we first ran an omnibus test for an overall difference of external regula-

tion across profiles, which was significant, χ2 (3) = 55.73, p < .001. As hypothesized, the relation be-

tween the profiles and motivation is ambiguous. Pairwise comparisons showed that students in the

growth- mindset – low- performance goals profile reported less external regulation compared to students in

the fixed- mindset profile and the growth- mindset – high- performance goals profile, and that students in the

growth- mindset – moderate- performance goals profile also had less external regulation compared to students

in the growth- mindset – high- performance goals profile. Next, an omnibus test for an overall difference

in intrinsic motivation was significant, χ2 (3) = 36 .17, p < .001. Pairwise comparisons showed that the

fixed- mindset profile had less intrinsic motivation compared to the other three profiles and that the

growth- mindset – low- performance goals profile reported less intrinsic motivation compared to the growth-

mindset – high- performance goals profile.

With regard to school burnout symptoms, an omnibus test for an overall difference across profiles

was significant, χ2 (3) = 60.10, p < .001. Pairwise comparisons of the SBI showed that all profiles were

significantly different from each other. As expected, students in the growth- mindset – low- performance goals

profile had the lowest school burnout symptoms, followed by the growth- mindset – moderate- performance

goals profile, growth- mindset – high- performance goals profile and students in the fixed- mindset profile had the

highest SBI scores.

Finally, we checked whether gender and educational track differences were found between profiles.

As hypothesized, we found differences for both gender and educational tracks. For gender, girls were

less likely to belong to the fixed- mindset profile compared to boys. For educational track, pre- vocational

students were more likely to belong to the fixed- mindset group compared to the higher general educa-

tion students, and pre- university students were less likely to belong to the growth- mindset – low- performance

goals profile compared to the higher general education students (see Appendix S1, Table S8 for more

details). Because of these differences, we checked whether similar profiles emerged with a more ho-

mogeneous group of our sample, in which we only included students of our sample who were pursuing

higher education. This resulted again in a four- profile solution with comparable profiles and outcomes.

The profiles are therefore robust across educational tracks.

FIGUR E 1 Final four- prof ile solution LPA. Total n = 724. Data on t he Y- ax is were transfor med based on the proportion

of maxi mum sca ling (‘POMS’ ) method, transform ing each scale to a range from 0 (= mi nimal possible) to 1 (= maximum

possible) to mainta in the proportions of the absolute distances between the observed response options ( Moel ler, 2015).

POMS = [(observed −minimum)/(maximum−minimum)].

ALTIKULAÇ et a l.

TABL E 3 Means and st andard errors in GPA, math, motivation a nd SBI across profiles.

Var i abl e

Fixed

mi nd set (a)

Growt h- mindset – low-

perfor mance goals ( b)

Growt h- mindset – moderate-

perfor mance goals (c)

Growt h- mindset – hi gh-

perfor mance goals (d) Omnibus test

Post- hoc comparisonn = 62 n = 194 n = 300 n = 168 χ2p

GPA 6.48 (0. 13 )6.79 (0.07)6.81 (0.05)6 .70 (0.09)8.04 <.05 a < b,c*

Math 6.19 (0 .19 )6.88 (0. 12 )6.79 (0 .10)6.54 (0.16 )18. 36 <.0 01 a < b**& c* ; d < b*

External regulation 2.60 (0.08) 2.34 (0.04) 2.44 (0.04)2 .76 (0.05 )55.73 <.0 01 b < a*& d**; c < d* *

Intrinsic mot ivation 1.86 (0.10) 2.24 (0.05)2.35 (0.05 ) 2.38 (0.06 )3 6.17 <.001 a < b,c,d* *; b < d*

SBI 33.12 (1.07 ) 24.60 (0.60 )26.41 (0 .47 )28.16 (0.90)60.10 <.001 b < a ,d**& c*; c < a**&

d*; d < a **

Note: The sign ificance values for t he compa risons are indicated by *p < .05; * * p < .01. GPA and mat h values are between 1 a nd 10. SBI values are b etween 9 and 54 . External r egu lation and intri nsic mot ivation values

are bet ween 1 and 4. a = values are equ al to the fixed- mindset profile, b = values are eq ual to t he growth- mindset – Low - performance goal s profi le, c = values are equal to the growth- mindset – Mo derate- performa nce goals profi le and

d = values are equal to the growth- mindset – high- performance goals prof ile. The italic va lues represent sta ndard errors.

MIN DSET PROF ILES OF SECON DARY SCHOOL STU DENT S

DISCUSSION

This study aimed to (1) use a person- oriented approach to investigate possible heterogeneity in mindset,

achievement goals and effort beliefs within mindset meaning systems, by creating mindset profiles;

and (2) extend on previous person- oriented studies by linking these profiles to a wider range of school

outcomes, including students' academic achievement, motivation and well- being. We found four distinct

profiles based on mindset and mindset- related constructs. When comparing these profiles on school

outcomes, differences were found in students' academic achievement, motivation and academic well-

being. These results confirmed our hypotheses that multiple- mindset meaning systems exist and that

they relate differently to school outcomes.

Mindset profiles

Whereas previous research has linked growth mindset to mastery goals and fixed mindset to per-

formance goals (e.g., Burnette et al., 2013), we found four different combinations of mindsets and

mindset- related constructs by using a person- oriented approach. We hypothesized that we would find

multiple- mindset meaning systems, including the two ‘classic’ mindset profiles (fixed mindset and

growth mindset). First, we found one profile with relatively low levels of growth mindset, positive effort

beliefs and learning goals, which we named fixed- mindset profile. Next, we found three growth- mindset

profiles. The first was consistent with the classic growth- mindset meaning system, showing relatively

more growth mindset and positive effort beliefs and predominantly mastery goals (growth- mindset – low-

performance goals). We also found two hidden profiles. The second growth- mindset profile showed in ad-

dition to the first growth- mindset profile, somewhat higher levels of performance goals ( growth- mindset

– moderate- performance goals). The third profile embraced equally high levels of performance and mastery

goals ( growth- mindset – high- performance goals), which is in line with the hypothesized growth- mindset profile

with multiple achievement goals profile. This suggests that earlier variable- oriented findings, supporting two

distinct mindset meaning systems, are not representative of the entire population.

FIGUR E 2 Outcome variables on the four- profile solut ion. Total n = 724. Data on the Y- a xis were transformed based

on the proportion of maxi mum sca ling (‘POMS’ ) method, transform ing each scale to a range from 0 (= mi nimal possible)

to 1 (= maximum possible) to maintain the proportions of the absolute distances between the obser ved response options

(Moeller, 2015 ). POMS = [(observed −minimum)/(max imum−min imum)]. Class membership was taken into accou nt in the

model, and gender and educational track were added in the model as covariates. *p < .05; * * p < .01.

ALTIKULAÇ et a l.

As stated before, these different combinations of constructs may explain the heterogeneity found

in the literature (e.g., Yu & McLellan, 2020). Our study shows multiple growth- mindset profiles, in

which the performance goals are low, moderate or high. The combination of a growth mindset with

varying degrees of performance goals could explain the inconsistent support for the effect of mindset

on achievement goals (e.g., Burgoyne et al., 2020). Furthermore, different levels of performance goals

seem to account for why certain profiles are more adaptive than others. Growth- mindset students with

moderate levels of performance goals seem to be able to f lexibly use different strategies, perhaps to

adapt themselves to different contexts (Hattie & Donoghue, 2016). Possibly, these students adapt them-

selves to the performance- focused schooling system in the Netherlands (as is the case in most Western

educational systems, Biesta, 2015; Butera et al., 2023; Hattie & Donoghue, 2016) by adding perfor-

mance goals to their natural preference for mastery goals. In line with previous literature (Crouzevialle

& Butera, 2013), our study also shows that focusing ‘too much’ on performance goals may harm the

advantageous character of being able to use both mastery and performance goals (growth- mindset – high-

performance goals).

Linking mindset profiles to school outcomes

Academic achievement

When examining students' academic achievement based on their assigned profile, we hypothesized that

growth- mindset students would have higher grades compared to fixed- mindset students. We found that

students with the fixed- mindset profile had lower grades, both for their GPA and math achievement,

compared to students with a growth mindset with low- or moderate- performance goals, but not com-

pared to those with high- performance goals. These results are in line with previous studies, showing

higher grades for students with a growth mindset compared to a fixed mindset (Chen & Tutwiler, 2017;

Lou et al., 2021; Yu & McLellan, 2020). Additionally, for students' math achievement, growth- mindset

students with high- performance goals had lower math grades compared to growth- mindset students

with low- performance goals. The distinct outcome that is revealed in math achievement, but not in

GPA, is in line with previous research showing that the effects of having a growth mindset are more

pronounced in a challenging subject such as math (e.g., Blackwell et al., 2007). Our results on academic

achievement underline that the effect of embracing a growth mindset with high- performance goals

might be undesirable, but that low- to- moderate levels might be advantageous for students' achievement.

Motivation

Based on prior literature, suggesting that the direction of the association between mindset and motiva-

tion may be dependent on the meaning system it is embedded in ( Molden & Dweck, 2000), no clear

expectations were formed on differences between mindset profiles regarding motivation (external

regulation and intrinsic motivation). First, the external regulation subscale had low reliability, and

therefore, only the results of this subscale are summarized. More self- reported external regulation

was found for fixed- mindset students and growth- mindset students with high- performance goals,

compared to growth- mindset students with low- or moderate- performance goals. Second, for intrinsic

motivation, we found that fixed- mindset profile students reported less intrinsic motivation compared

to the three growth- mindset profiles. Additionally, we found that growth- mindset students with low-

performance goals reported less intrinsic motivation compared to growth- mindset students with high-

performance goals.

The results on intrinsic motivation suggest that intrinsic motivation is higher for growth- mindset

students, especially when they are also adopting performance goals. This is in line with prior research,

indicating that intrinsic motivation is found to be positively related to growth mindset (Liu, 2021) and

MIN DSET PROF ILES OF SECON DARY SCHOOL STU DENT S

that performance- approach goals could enhance intrinsic motivation (e.g., Molden & Dweck, 2000).

Adopting higher levels of performance goals alongside higher levels of mastery goals could enhance

students' internal motivation even further, whereas lower levels of mastery goals result in diminishing

the positive effect on intrinsic motivation. Finally, a previous longitudinal study shows that having a

higher interest in school is related to more success later in life (Spengler et al., 2 018), showing the im-

portance and impact of student motivation in developing at school and in life (Duraiappah et al., 2022;

Organisation for Economic Co- operation and Development Staff, 2 017 ).

Wel l- being

The potential impact of mindset on academic well- being is an important factor that is often overlooked

in the literature. We hypothesized that fixed- mindset students or students adopting multiple achieve-

ment goals would report the highest levels of school burnout symptoms. We found that students' aca-

demic well- being was different across all profiles, with the lowest level of school burnout symptoms

for students with a growth- mindset profile with low- performance goals, and the highest level of school

burnout symptoms for students with a fixed mindset. Additionally, the profiles found in our study re-

veal that the levels of performance goals found for the growth- mindset profiles accounted for the level

of school burnout symptoms, adding significant insight to the literature.

Our results are in line with variable- centred studies showing that growth- mindset students had

fewer school burnout symptoms compared to fixed- mindset students ( Janssen & van Atteveldt, 2023;

Kim, 2020; Nieuwenhuis et al., 2023). Additionally, students with a growth mindset and high-

performance goals may experience external pressure to achieve at school, causing more school burnout

symptoms (Crouzevialle & Butera, 2013; Salmela- Aro et al., 2009). School burnout is related to worse

academic achievement (Madigan & Curran, 2021), dropout (Bask & Salmela- Aro, 2013), mental health

problems (Fiorilli et al., 2017) and reduced quality of life (Kasen et al., 1990). The impact of school

burnout may be detrimental to students' current and future lives, emphasizing the importance of keep-

ing track of student well- being. Adopting a growth mindset may be a way to help students enjoy school

and protect them from school burnout. However, when students adopt a growth mindset together with

high- performance goals, the protective effect may be lower.

Gender and educational track

Previous person- oriented mindset research found that girls more often adopt a growth mindset ( Yu &

McLellan, 2020), whereas variable- centred mindset literature is inconclusive regarding gender differ-

ences (Burnette et al., 2013; de Kraker- Pauw et al., 2022). In line with person- oriented research, our

study demonstrated that girls were less likely to adopt a fixed mindset compared to boys. Interestingly,

gender differences were not apparent in the observed variables, only in the profile memberships.

However, global differences in motivational patterns can become apparent when observed variables

with small differences are correlated and combined (Giudice et al., 2012). This emphasizes the impor-

tance of studying mindset from a person- oriented perspective. Furthermore, our results are in line with

the literature on motivational theory, showing that girls are trying to improve their abilities to a greater

extent and have more adaptive learning behaviours compared to boys (Butler, 2014; Litalien et al., 2017;

Vansteenkiste et al., 2009).

Furthermore, educational track is linked to SES in the Netherlands (Bol et al., 2014), and especially

at- risk students, such as students with lower SES backgrounds, benefit from adopting a growth mindset

(Sisk et al., 2 018). In line with literature from the United States (Destin et al., 2019), our study shows that

students in the pre- vocational track (with a lower SES background) more often adopt a fixed mindset

compared to students in the higher general track. Additionally, students in the higher general track more

often adopt a growth mindset with low- performance goals compared to students in the pre- university

ALTIKULAÇ et a l.

track. As the educational track in the Netherlands becomes increasingly academic- oriented from pre-

vocational to higher general to pre- university education, this result suggests that students following

more academic tracks also increase in the degree of performance goals. The increased performance goal

levels of pre- university track students compared to higher general track students may be explained by the

increased pressure students may experience in academic environments (e.g., Nordmo & Samara, 2009).

This is, for example, ref lected by the more school burnout symptoms students experience when on an

academic track compared to those on a vocational track (Salmela- Aro et al., 2008). The higher pressure

may come at a cost, as high- achieving students have relatively high levels of substance use, depres-

sion, anxiety and rule- breaking behaviour, which are likely linked to the pressure to excel (National

Academies of Science, Engineering, and Medicine, 2019).

Implications, limitations and future research

Previous research showed that a growth- mindset profile with high mastery and performance goals

was as beneficial for student achievement as a growth- mindset profile with dominant mastery goals

(e.g., Lou et al., 2021; Yu & McLellan, 2020). Our study adds to the literature by showing that adopt-

ing high levels of performance goals alongside a growth mindset can still be unfavourable to other

outcomes. Since most Western schooling systems use performance- based assessment (Biesta, 2015;

Hattie & Donoghue, 2016), which in part affects the decline in students' motivation in their educa-

tion (Scherrer & Preckel, 2019), it can be beneficial for students to adopt low- to- moderate levels of

performance goals alongside a growth mindset and mastery goals. Growth- mindset interventions, in

which students experience the malleability of the brain, can help to adopt a more favourable mindset,

resulting in more overall school enjoyment. A promising intervention in this regard is of Janssen and

van Atteveldt (2022), in which students experienced being in control over their own brain activity using

mobile EEG neurofeedback devices. In addition, we argue that how we evaluate students should not

only be focused on students' performance (e.g., grades) but can be considered as one of many desirable

learning outcomes (Hattie & Donoghue, 2016 ), including motivation and well- being.

The current study has some limitations. First, the mastery avoidance goals subscale and the external

regulation subscale used in our study had relatively low internal consistency, even after excluding some

items to improve the model fit. Additional analyses without these subscales were done to determine the

robustness of our results, showing identical results. Also, the internal consistency of these subscales is

comparable with the reliability reported in previous studies (Ryan & Connell, 1989; Strunk et al., 2021).

Using the originally planned design in this study may help other researchers take the low internal con-

sistency of these subscales into account when planning their future studies, pointing out that the results

regarding these subscales should be interpreted with caution. Second, this study is a cross- sectional

study, and the causality of mindset on academic achievement and well- being cannot be inferred. As

outlined by Vu et al. (2022), motivation and achievement are cyclical and influence each other. Future

studies should focus on longitudinal effects of mindset meaning systems and academic achievement

to disentangle these effects, and examine the stability or change in the profiles over time. Third, our

sample had a relatively small group of pre- vocational students compared to the other tracks. Therefore,

our results are not generalizable to all first- year secondary school students.

We argue that future studies should focus on and tailor mindset interventions to high- risk sub-

groups since several studies showed that mindset interventions are especially effective for high- risk

students (e.g., Sisk et al., 2 018). Our results show that these mindset interventions would probably

be most effective for fixed- mindset students. However, students with a growth- mindset and high-

performance goals might need another type of intervention to reduce the potential negative down-

stream effects of high- performance goals on school outcomes. Furthermore, future studies should

focus on examining the stability of mindset profile membership and how these are associated with

development. The study by Janssen and van Atteveldt (2022) shows that mindset profiles can be

influenced by the use of an intervention, transitioning students of the fixed- mindset profile to one

MIN DSET PROF ILES OF SECON DARY SCHOOL STU DENT S

of the two growth- mindset profiles. Finally, we suggest that future studies should further explore

whether it is favourable for students to adopt a growth mindset with high- performance goals since

our results show advantageous results for motivation, but less advantageous results for academic

achievement and well- being.

CONCLUSION

This study uncovered reasons that might contribute to the heterogeneous associations found in the lit-

erature between mindset and mindset- related constructs, and their relations to academic achievement,

motivation and well- being. We found four mindset profiles, revealing hidden growth- mindset profiles

with moderate and high levels of performance goals. Our study shows that multiple pathways from

mindset to academic achievement, motivation and well- being are possible. Overall, more advantageous

outcomes were found for growth- mindset students with low- or moderate- performance goals compared

to fixed- mindset students and growth- mindset students with high levels of performance goals. This

study shows the importance of including students' motivation and academic well- being as outcome

variables in mindset studies.

AUTHOR CONTRIBUTIONS

Sibel Altikulaç: Conceptualization; formal analysis; investigation; methodology; project administra-

tion; writing – original draft; writing – review and editing. Tieme W. P. Janssen: Conceptualization;

investigation; methodology; project administration; writing – original draft; writing – review and edit-

ing. Jun lin Yu: Methodology; writing – review and editing. Smiddy Nieuwenhuis: Conceptualization;

methodology; project administration; software. Nienke M. van Atteveldt: Conceptualization; funding

acquisition; methodology; writing – original draft; writing – review and editing.

ACKNOWLEDGEMENTS

We are very grateful to the participating schools, parents and adolescents in our study. This research

was supported by the European Research Council 716736 (BRAINBELIEFS) to N. v. A. and J. Y. was

supported by the Research Council of Finland through an Academy Research Fellowship (354742).

CONFLICT OF INTEREST STATEMENT

The authors declare no conf licts of interest.

DATA AVAILABILITY STATEMENT

The data that supports the findings of this study are available on request from the corresponding author.

ORCID

Tieme W. P. Janssen https://orcid.org/0000-0001-8375-8762

Junlin Yu https://orcid.org/0000-0002-6267-5789

Smiddy Nieuwenhuis https://orcid.org/0000-0002-7930-2866

Nienke M. Van Atteveldt https://orcid.org/0000-0002-3387-6151

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How to cite this article: Altikulaç, S., Janssen, T. W. P., Yu, J., Nieuwenhuis, S., & Van

Atteveldt, N. M. (2024). Mindset profiles of secondary school students: Associations with

academic achievement, motivation and school burnout symptoms. British Journal of Educational

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Mindset and effort during a self-adapted arithmetic task: Variable- and person-oriented approaches

Article

Full-text available

Nov 2022
LEARN MOTIV

Background Most of the literature on the relation between mindset and effort depends on subjective self-reports, which may not reliably capture the actual investment of effort. In the current study we (1) operationalized mental effort as the chosen and executed difficulty level in a self-adapted arithmetic task, and (2) combined variable-oriented and person-oriented analytic approaches, with the latter allowing us to explore qualitatively different profiles of effort investment. Methods First-year Dutch high-school students (n = 299; aged 11–14 yrs) chose difficulty levels of arithmetic problems in 20 rounds. Linear Mixed Modeling (variable-oriented approach) and Latent-Profile Analysis (person-oriented approach) were used and associations with mindset, errors, gender, and school achievement (standardized arithmetic test, and math grades) were explored. Results For male students, mindset affected their choices independently of errors, while for female students, mindset only played a role when they experienced the setback of errors. Only for males, effort mediated the relation between mindset and standardized arithmetic scores. Additionally, we identified five effort profiles: (1) Avoiders, (2) Exploring challengers, (3) Challengers, (4) Explorers and (5) Steady. Two profiles were more growth-oriented (2 and 3), and two more fixed-oriented (1 and 5). Conclusion This study adds to the literature by demonstrating a gender-moderated relation between mindset and an objective measure of effort, but also important nuances as indicated by individual differences in effort strategies.

Children’s Gender Stereotypes in STEM Following a One-Shot Growth Mindset Intervention in a Science Museum

Article

Full-text available

May 2021

Women are drastically underrepresented in science, technology, engineering, and mathematics (STEM) and this underrepresentation has been linked to gender stereotypes and ability related beliefs. One way to remedy this may be to challenge male bias gender stereotypes around STEM by cultivating equitable beliefs that both female and male can excel in STEM. The present study implemented a growth mindset intervention to promote children’s incremental ability beliefs and investigate the relation between the intervention and children’s gender stereotypes in an informal science learning site. Participants (n = 143, female n = 77, male n = 66, 5–12-years-old, Mage = 8.6, SD = 1.7) were visitors to a science museum who took part in an interactive space science show. Participants who were exposed to a growth mindset intervention, compared to the participants in the control condition, reported significantly less gender stereotyping around STEM by reporting equitably in the stereotype awareness measure. Relatedly, participants in the control condition reported male bias gender stereotype in the stereotype awareness measure. Further, children between 5 and 8-years-old reported greater male bias stereotypes awareness and stereotype flexibility in space science compared to children between 9 and 12-years-old. Lastly, children demonstrated in-group bias in STEM ability. Male participants reported gender bias favoring males’ ability in stereotype flexibility and awareness measures, while female participants reported bias toward females’ ability in stereotype flexibility and awareness measures. These findings document the importance of a growth mindset intervention in buffering against STEM gender stereotyping amongst children, as well as the significant role a growth mindset intervention can play within an informal science learning site.

Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda

Article

Full-text available

Mar 2022
EDUC PSYCHOL REV

The question of how learners’ motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education. Here, we present our understanding of how influential theories of academic motivation have conceptualized reciprocal interactions between motivation and achievement and the kinds of evidence that support this reciprocity. While the reciprocal nature of the relationship between motivation and academic achievement has been established in the literature, further insights into several features of this relationship are still lacking. We therefore present a research agenda where we identify theoretical and methodological challenges that could inspire further understanding of the reciprocal relationship between motivation and achievement as well as inform future interventions. Specifically, the research agenda includes the recommendation that future research considers (1) multiple motivation constructs, (2) behavioral mediators, (3) a network approach, (4) alignment of intervals of measurement and the short vs. long time scales of motivation constructs, (5) designs that meet the criteria for making causal, reciprocal inferences, (6) appropriate statistical models, (7) alternatives to self-reports, (8) different ways of measuring achievement, and (9) generalizability of the reciprocal relations to various developmental, ethnic, and sociocultural groups.

Harnessing growth mindsets to help individuals flourish

Article

Jan 2022

Psychologists are uniquely positioned to help with our collective obligation to advance scientific knowledge in ways that help individuals to flourish. Growth mindsets may offer one such tool for improving lives, yet some research questions the potential to replicate key findings. The aims in the current work are to help explain mixed results and outline ways to improve intervention impact. To reach these goals, we first offer a brief overview of the links between growth mindsets and psychological flourishing. Second, we outline key theories of causal mechanisms and summarize sources of meaningful heterogeneity in growth mindset interventions, with a focus on those designed to improve mental health. Third, we provide cautionary notes that highlight nuances of growth mindset messaging in contexts with stigmatized social identities. Fourth, to conclude, we suggest areas for future research aimed at understanding how to most powerfully harness growth mindsets to help individuals reach optimal psychological functioning.

Growth, fixed, and mixed mindsets: Mindset system profiles in foreign language learners and their role in engagement and achievement

Article

Aug 2021
STUD SECOND LANG ACQ

Language learners' mindsets -- their beliefs about whether language is a fixed aptitude that is immutable or a malleable capacity that can be developed -- are associated with achievement goals, language-use anxiety, reappraisals of challenges, and persistence. This study integrates these mindset-related constructs to identify mindset-system profiles among foreign language learners. A latent profile analysis of 234 university students in foreign language courses revealed three distinct profiles. The fixed (21.8%) and growth (20.5%) profiles showed distinct and contrasting patterns of goals, reappraisals, anxiety, and persistence. However, most learners (57.7%) endorsed a mixed profile. Although mindsets alone did not predict grades, students in the growth profile were consistently most engaged and achieved the highest grades, suggesting that mindsets function as a system, in concert with related factors. This person-centered approach enhances our understanding of the complexity and functions of the mindset system, as well as the motivation of learners with mixed mindsets.

Good Education in an Age of Measurement

Book

Nov 2015

Gert Biesta

Mindset profiles of secondary school students: Associations with academic achievement, motivation and school burnout symptoms

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