1042426
research-article2021

RERXXX10.3102/00346543211042426Bureau et al.Pathways to Student Motivation

Review of Educational ResearchMonth 202X, Vol. XX, No. X, pp. 1­–27
DOI: 10.3102/00346543211042426
Article reuse guidelines: sagepub.com/journals-permissions© 2021 AERA. https://journals.sagepub.com/home/rer
https://doi.org/

Pathways to Student Motivation: A Meta-Analysisof Antecedents of Autonomous and Controlled
Motivations
Julien S. Bureau
Université Laval
Joshua L. Howard
Monash University
Jane X. Y. Chong
Curtin University
Frédéric Guay
Université Laval
Students’ self-determined motivation (acting out of interest, curiosity, andabiding values) is associated with higher academic well-being, persistence,
and achievement. Self-determination theory posits that self-determined
motivation is dependent on the satisfaction of three psychological needs(relatedness, competence, and autonomy), which are in turn facilitatedthrough need-supportive behaviors from notable others. In this meta-analysis, conducted over 144 studies and more than 79,000 students, we sought
to overview pathways to student motivation in order to verify (1) how do
psychological needs rank in the strength of their prediction of self-determined motivation and (2) which autonomy-support providers (parents orteachers) are the most relevant for psychological need satisfaction in students and self-determined motivation. Results show that teacher autonomy
support predicts students’ need satisfaction and self-determined motivation
more strongly than parental autonomy support. In addition, competence isthe most positive predictor of self-determined motivation, followed byautonomy and then by relatedness.

Keywords:

academic motivation, psychological needs, autonomy support,
self-determination theory, meta-analysis

1Bureau et al.

Students’ motivation is relevant for the quality of their learning experience(Pintrich & De Groot, 2003). Self-determination theory (SDT) is a comprehensive
motivational framework that has been used to explain how individuals thrive
within many life domains (Ryan & Deci, 2017). Specifically, SDT has proveneffective at qualitatively and quantitatively describing types of motivation andtheir potential impact on achievement and other desirable school outcomes(Howard et al., 2021; Vasconcellos et al., 2020). The degree to which students
experience these types of motivation is expected to vary according to situationaland environmental factors (Vallerand, 1997), making motivation a primary target
for intervention. According to SDT, students will experience self-determined
motivation when behaviors from people they interact with satisfy their psychological needs of autonomy, competence, and relatedness (Ryan & Deci, 2017).
However, the theory makes no proposition about whether each need contributes
equally or whether some will be more relevant than others.
In this meta-analysis, we sought to verify (1) which psychological need is thestrongest predictor of self-determined motivation and (2) which autonomy-support provider (parents, teacher) is the most relevant for psychological need satisfaction and self-determined motivation. Consequently, this study seeks to test
core principles in SDT to advance our understanding of the different pathways inwhich teachers and parents may affect students’ psychological need satisfaction
and self-determined motivation.
Student Motivation According to Self-Determination Theory
Student motivation is defined within SDT along a continuum of motivation
quality that brings varying levels of self-determination (i.e., being at the origin of
one’s action; Ryan & Deci, 2017). In decreasing levels of self-determination,
there is intrinsic motivation followed by three types of extrinsic motivation,
namely, identified, introjected, and external regulations. When acting out of
intrinsic motivation, the reasons for engaging in learning are tied to inherent
enjoyment and interest in doing academic tasks. This represents the highest levelof self-determination. In contrast, with extrinsic motivation, the reasons forengaging in learning are tangential to learning such that it is rather instrumental
for achieving other benefits. Identified regulation represents a state that drives
students to engage in school activities that they consider personally important andmeaningful. It implies a high level of self-determination, albeit not as high as
when intrinsically motivated. Identified and intrinsic motives together represent
autonomous motivation. Introjected regulation, in turn, depicts a state that drives
students to participate in their schooling to avoid feelings of shame and guilt or toassert pride. This regulation implies a moderate level of self-determination.
External regulation represents a state where students’ involvement in school
activities is driven by factors outside individuals’ control, such as external rewards
or sanctions to avoid. External regulation conveys low levels of self-determination. External and introjected regulations together represent controlled motivation. Last, a final form of (non-)motivation is sometimes assessed within academic
motivation scales, namely, amotivation. It represents a state where students see noclear reason to pursue school activities, and it is considered a non-self-determined
form of motivation (Vallerand et al., 1992). Integrated regulation is an additional
2

Pathways to Student Motivation

motivation type representing a state of significant congruence between school
engagement and one’s values and needs. While it is theoretically situated between
intrinsic and identified motives in terms of self-determination, researchers agree
that this form of regulation requires individuals to have formed a coherent identity(Deci et al., 1996) before developing. Consequently, this type of extrinsic motivation is rarely assessed in studies on children and adolescents. Consequently, very
few student motivation scales include it.
Recent meta-analyses have demonstrated that motivation types related differently to a wide range of outcomes across education and physical education fieldsof research (Howard et al., 2020; Howard et al., 2021; Toste et al., 2020;
Vasconcellos et al., 2020). For example, intrinsic and identified motives (i.e.,autonomous motivation) were found to be strong positive predictors of students’achievement, engagement, positive affect, goals, and self-esteem. Introjected regulation demonstrated mixed effects, relating positively to desirable and undesirable outcomes, while external regulation demonstrated primarily nonsignificant
results or positive associations with maladaptive outcomes. Amotivation related
positively to undesirable outcomes. Given the consistent relations between thevarious motivation types and student outcomes, and acknowledgement that student motivation can benefit from outside interventions (Lazowski & Hulleman,2016), it becomes important to identify factors with the potential to foster or hinder the various types of motivation.
Psychological Needs According to Self-Determination Theory
Self-determination theory details three fundamental psychological needs thatare purported to be essential for individuals’ daily functioning, including a role incultivating self-determined motivation types. The specified needs are those of
autonomy, competence, and relatedness (Ryan & Deci, 2000). Students experiencing autonomy perceive that they are engaging in learning tasks freely and voluntarily, without perceived coercion. Students experiencing competence areconfident that their actions are impactful in shaping their academic experience.
Students experiencing relatedness feel connected with important others in theirschool (e.g., teachers, friends; Baumeister & Leary, 1995; Chen et al., 2015). The
three psychological needs are best satisfied when students benefit from need-supportive contexts. SDT asserts the importance of supportive interpersonal relationship (e.g., with teachers and parents) for fostering students’ need satisfaction(Chirkov & Ryan, 2001; Ryan & Deci, 2020).
Although need support can be directed toward the satisfaction of either autonomy, competence, or relatedness needs, the bulk of research on need support has
focused on autonomy support as it has been found to be important for fostering
not only autonomy need satisfaction but also competence and relatedness (Sheldon
et al., 2009). Autonomy-supportive behaviors promote students’ volitional functioning (Carpentier & Mageau, 2019; Reeve & Jang, 2006): these are empathy,
providing information, and supporting active participation. Teachers and parents
acting with empathy take the student’s/child’s perspective and acknowledge theirindividuality and feelings. Teachers and parents who provide information give
explanatory rationales for rules and demands, supporting internalization of theimportance of schoolwork. Teachers and parents who support an active
3

Bureau et al.

participation provide meaningful self-relevant decision-making opportunities,
cultivating intrinsic motivation (Mageau et al., 2015; Reeve & Cheon, 2021).
These behaviors encourage self-determination in students/children, and makethem feel competent and connected to the teacher/parent, catering to all threepsychological needs. Meta-analyses on autonomy-supportive teaching interventions (Reeve & Cheon, 2021) and on correlates of autonomy-supportive teachingin the physical education context (Vasconcellos et al., 2020) point toward thenumerous benefits of this teaching style for students in terms of affective, cognitive, and behavioral outcomes, in addition to their positive effects on need satisfaction. For example, autonomy-supportive teaching increases classroom
engagement (Cheon et al., 2019), initiative (Reeve et al., 2020), and achievement(Cheon et al., 2020), as well as motivation for out-of-school activities (Hagger &Chatzisarantis, 2016). A meta-analysis on parental autonomy support and its relations to educational and psychosocial outcomes in children (Vasquez et al., 2016)has associated it with positive educational outcomes such as students’ achievement, perceived competence, positive attitudes toward school, and self-regulation. Other autonomy-supportive sources such as peers or school administration
have also been examined in past research (Hang et al., 2015; Zhou et al., 2019),
showing that they generally have an incremental positive effect on motivation andadjustment, but they have received less scholarly attention.
Taken together, previous research has demonstrated the extended benefits ofteacher and parental autonomy support for students. A central premise of SDT
asserts a causal sequence that links autonomy support with enhanced psychological need satisfaction, which in turn fosters high-quality motivation, which in turn
fosters adaptive outcomes. While the motivation-to-outcomes part of this sequence
was recently meta-analyzed in the field of education (Howard et al., 2021), a systematic and simultaneous test of the first part of the sequence from autonomysupport to psychological needs, and then from needs to motivation, has neverbeen conducted.
Prediction of Student Motivation by Psychological Needs and Autonomy Support
Previous research shows consistent support for need satisfaction at school
being a paramount factor in predicting and understanding self-determined student motivation (Deci et al., 1996). As students experience psychological needsatisfaction, they begin an internalization process where reasons for engaging inschool tasks shift from less self-determined (e.g., because it is mandatory) tomore self-determined (e.g., because they enjoy it; Deci et al., 1994; Ryan & Deci,
2017). This internalization process explains why experiencing need satisfaction
in a setting will ultimately translate into valuing the activity more (identified
regulation) and showing interest for it (intrinsic motivation). Results from a previous meta-analysis in physical education (Vasconcellos et al., 2020) showedthat all needs related to motivation types in highly similar ways: they had strong
positive relations with intrinsic motivation and identified regulation, moderate
positive relations with introjected regulation, weak negative relations with external regulation, and moderate negative relations with amotivation. Using metaanalytic path analysis to uncover unique variance predicted by individual needs,
this study showed that competence was the strongest predictor of self-determined4

Pathways to Student Motivation

motivation followed by autonomy, whereas relatedness had only a small contribution. Although this study implies that competence plays a somewhat stronger
role in fostering self-determined motivation for physical education, this is nottrue for all domains. A recent meta-analysis of need satisfaction outcomes in theworkplace demonstrated that autonomy was the most influential of the threeneeds in predicting work motivation, explaining as much as 42% of the total variance in intrinsic motivation (Van den Broeck et al., 2016). It is therefore important to explore the education literature more broadly and investigate how
psychological needs will differentially predict motivation types for students.
In addition to basic psychological needs, autonomy support from teachers has
also been positively associated with self-determined types of motivation (intrinsic, identified) and negatively associated to motivation types closer to the nonself-determined pole (introjected, external, amotivation; Reeve & Cheon, 2021).
With regard to parental autonomy support, it has been linked positively to autonomous motivation, but to more controlled forms of motivation as well (Guay et al.,2013; Vasquez et al., 2016). In general, autonomy-supportive environments created by teachers and parents bring students to value and enjoy schoolwork, fostering self-determined school motivation types (Aelterman et al., 2019; Chirkov &Ryan, 2001). However, SDT does not specify whether one provider of autonomy
support should be more important than the other in predicting motivation, or if
their pathways in doing so vary. Finally, while SDT and the hierarchical model ofmotivation (Vallerand & Ratelle, 2002) predict a sequence from autonomy support to needs, and then from needs to motivation, there is no specific indication
regarding the relative strength of these mediated effects.
The Present Study
The main objective of this research was to meta-analytically test the motivational sequence theorized by SDT linking autonomy support, need satisfaction,and types of student motivation. In doing so, we want to achieve a better understanding of the associations proposed by SDT and ultimately lead to a refined
understanding on how to foster adaptive educational outcomes. To do so, we
gathered and documented all available studies set within the educational psychology literature linking academic motivation and its antecedents as specified
by SDT, namely, need satisfaction and autonomy support. We first calculated
meta-analytically derived correlations for all associations between motivation
types, needs, and autonomy support variables. Second, we used relative weight
analysis to evaluate the unique predictive strength of individual needs. Third,
we applied meta-analytic structural equation modeling to test a mediation modelfrom autonomy support to need satisfaction, and then from needs to motivation
types, allowing for comparing the predictive strength of both autonomy support
types, as well as testing the relative strength of the mediation effects. Last, toinvestigate how the studied associations were shaped by individual characteristics, the moderating influences of age and gender are investigated. Both have
explained variations in how motivation types predict adaptive and maladaptive
outcomes in previous research (Howard et al., 2021; Vasconcellos et al., 2020)
implying that associations between academic motivation and educational variables is gender- and age-dependent. Education context (classroom or physical5

Bureau et al.

education), scale used, and publication status were also included as moderators
to understand if any contextual or methodological bias could increase or
decrease the strength of the relations tested.
MethodInclusion Criteria
We employed three inclusion criteria while collecting data: (1) data must be
based on students ranging from primary school to university (inclusive); (2)authors must have reported at least one correlation between a motivation type anda need satisfaction or autonomy support variable1; (3) data must be based on validated scales measuring motivation types, meaning that the scale used had to referto a validation paper or present reliability statistics as well as evaluate different
types of extrinsic motivation. Three validated scale “families” were identified: theSelf-Regulation Questionnaire (SRQ, PLOC, ASRQ; Ryan & Connell, 1989), theAcademic Motivation Scale (AMS; Vallerand et al., 1992), and the Behavioral
Regulation Questionnaire (BREQ, BREQ-2, BREQ-2r, BRSQ; Mullan et al.,1997). In addition, two other relevant validated scales were found: the Situational
Motivation Scale (SIMS; Guay et al., 2000) and the Exercise Motivation Scale(EMS; Li, 1999). These have been used in numerous studies and are validated
across multiple cultural contexts, age groups, and grade levels. Articles that usedtheir own adapted measure of motivation were included if they met the previously
mentioned criteria.
Literature Search
In searching the literature, we applied four primary methods to obtain all relevantpublished and unpublished data. Gray literature was included at all stages, including
dissertation, conference presentations, and other unpublished work. The first, second, and third authors first conducted forward searches collecting all publications
which had cited the scale validation studies for each of the included scales through
Google Scholar. Second, the EBSCO and PsycINFO databases were searched by
the second author, using the search terms “self-determination” and “student” as well
as by searching for the specific scales (see aforementioned inclusion criteria). Third,
the second author used the same search terms to search the Proquest Dissertation
and Thesis Global database. Finally, the first and second authors advertised for
unpublished data through several mailing lists (i.e., those administrated by theCenter for Self-Determination Theory, the American Educational Research
Association, the Society of Personality and Social Psychology, and the Society for
the Study of Motivation). Each potential sample identified through these procedures
was examined by the second and third authors, as well as by a research assistant.
When studies did not contain all relevant effect size information, corresponding
authors were contacted directly by the second author. Studies not meeting the inclusion criteria were excluded, and duplicate samples were removed at this stage.
Figure 1 depicts this process. The complete list of references used in the metaanalysis is presented in the online supplemental materials.
The final database consisted of 144 samples (98 published, 47 unpublished),
including a total of 1,718 correlation coefficients from 79,079 participants (each6

Figure 1. Flow diagram representing literature search procedures and application of
exclusion criteria.
Note. n denotes the number of articles; k denotes number of samples.

sample ranging from 26 to 4,566 participants, mean = 543). The mean age ofstudents across samples was 16.55 years old, and the average proportion of males
in each sample was 45.46%. Sample characteristics are presented in Table S1 in
the online supplement.
CodingA coding spreadsheet was developed by the second author including all
information concerning motivation variables and covariates (i.e., type of motivation or covariate, scale used to collect data, and reliability of measures).
Correlations between motivation types and predictors were coded as the primary effect size data. Sample size and demographic information pertaining to7

Bureau et al.

country and language in which data were collected, participant age, and percentage of males in the sample were also recorded. Publication status was also
coded where theses, dissertations, and conference presentations were coded as
unpublished. In order to assess and ensure the reliability of coding, the second
and third authors double-coded approximately 10% of data. Interrater agreement was very high (Cohen’s κ = .95; McHugh, 2012), and disagreements werediscussed by the second and third authors. Data were examined for coding errors
by the first and second authors prior to analysis.
Meta-Analytic Procedures
Meta-analytic calculations were conducted using the “robumeta” package
within the R software (Fisher & Tipton, 2015), and random-effects models wereapplied throughout (Hunter & Schmidt, 2000). Corrections for reliability were
performed prior to any analysis. When alpha coefficients were missing, meancoefficients were imputed. Non-independent data were adjusted through robust
variance estimator (Hedges et al., 2010). This method adjusts scores in non-independent samples to avoid sample size inflation. Cumulative analysis and onestudy-removed analyses were conducted in order to examine potential outliers(Borenstein et al., 2009). As no study was found to substantially change any
parameter, all studies were kept in the analyses. At this stage, predictor variables
with too few samples (<5) to analyze reliably were removed from the study. This
included the variables of peer autonomy support, relationship quality (between
student and teacher, peers, and parents), and need frustration variables (autonomy,competence, and relatedness frustration).
Meta-analytic correlations (ρ) are reported along with the associated 95%
confidence intervals, number of samples included in analysis (k), standard errorof the estimate, and heterogeneity statistics. Specifically, T2 and I2 statistics areprovided to examine heterogeneity in which T2 estimates the variance of effectsize parameters and I2 estimates the proportion of this variance that is likely to
be attributable to true moderating influences and not due to sampling error,
statistical artifacts, or chance (Higgins et al., 2003). An I2 score greater than
75% indicates a high likelihood of unaccounted moderating factors, whereas ascore lower than 25% indicates a relatively low likelihood of meaningful moderating factors (Higgins et al., 2003).
Relative weight analysis (RWA) was conducted with the R software package(Tonidandel & LeBreton, 2015). Using meta-analytic associations between needs
and motivation types, this analysis estimates how much variance in outcomes
could be uniquely attributed to each predictor if multicollinearity was suppressed.
Specifically, RWA involves the creation of a new set of orthogonal predictors as
similar as possible to the original set of predictors. The outcome variable is thenregressed onto these new orthogonal predictors, and that regression is weighted
according to the similarity between the original and orthogonal predictors, yielding an estimate of the unique variance explained by predictors.
We further tested our hypothesized model (see Figure 2), through meta-analytic path analysis following a weighted-covariance generalized least squares
procedure with multiple imputation (W-COV GLS with MI; Lv & Maeda,2020). This multivariate procedure was preferred over univariate MASEM
8

Figure 2. Hypothesized path model.

(meta-analytic structural equation modeling) because of its capacity to consider
the dependency among same-study correlations when pooling the covariancematrix for the MASEM (Lv & Maeda, 2020). In contrast, univariate MASEM or
meta-analytic path analysis will consist of generating a pooled correlation
matrix from meta-analytic correlations directly, yielding biased estimates andhigher chances of having a nonpositive definite correlation matrix (Cheung,2015). The W-COV GLS method was also preferred over other techniques(TSSEM, OSMASEM; Cheung & Chan, 2009; Jak & Cheung, 2020) because of
its ability to fit a structural model when there are multiple missing correlations(73.3% missing correlations in the present case; Jak & Cheung, 2018; Lv &Maeda, 2020). The W-COV GLS procedure with MI consists of gathering all
correlation matrices from the included studies and using averaged multiple
imputation to replace the missing correlations in each matrix.2 Then, a pooled
correlation matrix is computed on which the structural model is tested. In thisstudy, the structural model was fully identified, which yields no fit statistic.
Finally, several potential moderating variables were examined. Moderating
influences of both the educational context of the study (i.e., classroom education or physical education) and scale used (AMS, PLOC, BREQ) were examined through subgroup analyses. Age and gender were investigated throughmeta-regression. Meta-regression indicates how much of the unexplained heterogeneity (I2) can be accounted for by the potential moderating variable. The
9Bureau et al.

regression coefficient associated with this analysis indicates the estimatedchange in effect size for a one-point increase in the moderator. A R2 statistic aswell as the statistical significance estimate associated with the regression coefficient are also provided. A last potential moderator, publication bias, wasexamined through multiple approaches. Trim and Fill procedures were applied
to examine the symmetry of observed effects and determine if studies werelikely to be missing due to publication bias (Duval & Tweedie, 2000). This
analysis indicates the number of studies estimated to be missing and provides acorrected estimate of the effect size, thereby indicating the likelihood of publication bias (Duval & Tweedie, 2000). Egger’s test of the intercept was also
applied (Egger et al., 1997). This test also examines the symmetry of observed
effects and, through regression, determines if there is significant publication
bias. Last, subgroup analyses were also conducted to directly compare the estimated effect sizes from published and unpublished data. In these analyses, similar estimated effects and overlapping confidence interval would indicate thatpublished and unpublished samples are similar (Cumming & Finch, 2005) andtherefore do not present evidence for publication bias.
Results
Correlation analyses (see Tables 1 and 2) indicated that each psychologicalneed generally showed stronger correlations with more self-determined types ofmotivation. Specifically, autonomy related negatively with amotivation (ρ =−.34, k = 29), was unrelated to external regulation (ρ = −.04, k = 46), and correlated positively with introjected (ρ = .23, k = 42), identified (ρ = .48, k = 42),and intrinsic motivation types (ρ = .57, k = 52). Furthermore, correlations
between autonomy and motivation types were all significantly different fromeach other as indicated by non-overlapping confidence intervals (Cumming &Finch, 2005). A similar pattern arose for competence with a negative association
to amotivation (ρ = −.38, k = 37), no relation to external regulation (ρ = −.01,
k = 57), and positive correlations to introjected, identified, and intrinsic motivation types (ρ = .23, .53, and .58, respectively, k = 58, 57, 69). Confidence intervals of associations between competence and the various motivation types did
not overlap, indicating significant differences throughout. The need for relatedness was also found to demonstrate similar patterns (in the same ordering: ρ =−.38, −.02, .23, .53, and .58, respectively, k = 29, 45, 45, 44, 56), and only associations between relatedness and identified and intrinsic motivation types had
overlapping confidence intervals.
Autonomy support from teachers followed the same general pattern relating
more positively with self-determined types of motivation. Specifically, whereas
teacher autonomy support was significantly and negatively related to amotivation (ρ = −.32, k = 19) and external regulation (ρ = −.10, k = 34), it had positive associations to introjected (ρ = .17, k = 35), identified (ρ = .44, k = 44),and intrinsic motivation types (ρ = .48, k = 47). Autonomy support from parents displayed similar results with a negative correlation with amotivation (ρ= −.23, k = 6), but no relation to external regulation (ρ = .05, k = 15).
Relations connecting parental autonomy support to introjected (ρ = .15, k =15), identified (ρ = .28, k = 15), and intrinsic motivation (ρ = .23, k = 12)10

Table 1
Meta-analytic correlations between basic psychological needs, autonomy support andmotivation types
Covariate
Motivation

95% Confidence intervalkNAutonomy
Amotivation
29 13,439
External
46 18,842
Introjected
42 17,920
Identified
42 18,395
Intrinsic
52 23,759
Competence
Amotivation
37 20,579
External
57 34,558
Introjected
58 34,781
Identified
57 34,705
Intrinsic
69 40,789
Relatedness
Amotivation
29 13,885
External
45 20,175
Introjected
45 20,175
Identified
44 20,338
Intrinsic
56 27,209
Teacher Autonomy Support
Amotivation
19 8,640
External
34 21,792
Introjected
35 22,103
Identified
44 28,515
Intrinsic
47 33,517
Parent Autonomy Support
Amotivation
6 4,810External
15 8,859
Introjected
15 8,859
Identified
15 8,859
Intrinsic
12 5,549

ρ

Lower

Higher

SE

T2

I2

−.34
−.04
.23
.48
.57

−.44
−.12
.17
.41
.50

−.23
.04
.29
.56
.63

.05
.04
.03
.04
.03

.10
.09
.04
.04
.04

97.81
97.17
94.86
94.50
94.88

−.38
−.01
.23
.53
.58

−.47
−.08
.19
.47
.53

−.30
.05
.28
.58
.63

.04
.03
.02
.03
.02

.06
.06
.02
.03
.03

97.07
97.02
93.49
94.61
94.05

−.30
.01
.21
.44
.44

−.38
−.05
.15
.39
.39

−.23
.07
.26
.49
.48

.04
.03
.03
.02
.02

.04
.06
.04
.02
.03

95.04
96.29
94.37
91.73
93.15

−.32
−.10
.17
.44
.48

−.38
−.17
.13
.39
.43

−.26
−.02
.21
.49
.54

.03
.04
.02
.02
.03

.02
.03
.01
.03
.05

86.90
95.15
87.82
95.22
96.91

−.23
.05
.15
.28
.23

−.34
−.03
.06
.22
.15

−.12
.13
.24
.34
.31

.04
.04
.04
.03
.04

.01
.01
.02
.01
.01

83.25
88.81
92.49
83.52
79.32

Note. k = number of samples; ρ = correlation after correction for reliability and weighted bysamples size. As some studies reported autonomy support from both mothers and fathers, the numberof correlations entered into RVE analyses was greater than the number of samples; amotivation(8), external (19), introjected (19), identified (19), and intrinsic (16). However, the number of
independent effect analyzed is congruent with k.

were all positive and significant. Although the point estimates of the correlations differ, only the association between parental autonomy support and amotivation was different from the associations with all other motivation types.
11

Table 2
Correlations between basic psychological needs
AutonomyCompetence

Relatedness

—
.768
.648

58
—
.638

55
56
—

AutonomyCompetence
Relatedness

Note. Meta-analytic correlations below the diagonal. Number of included samples above thediagonal.

Table 3
Relative weight (RW) analysis of need satisfaction predicting motivation
OutcomeR2

Amotivation
External
Introjected
Identified
Intrinsic

.15
.004
.06
.30
.38

Average (weighted by R2)Autonomy, RW Competence, RW Relatedness, RW
(%)
(%)
(%)
30.34
62.43
35.16
29.96
39.30

46.90
14.27
35.58
44.38
42.33

22.77
23.30
29.27
25.66
18.38

34.49

43.21

22.31

The association with external regulation was also different from those withidentified and intrinsic motivation types.
Relative weight analysis was used to estimate how psychological needs predictedunique variance in motivation types once the shared variance among needs was
accounted for (Table 3). With regard to amotivation (R2 = .15; all negative associations), about half of explained variance was weighted on competence (47%), whileautonomy (30%) and relatedness (23%) shared the remaining variance. For external
regulation, variance explained by needs was negligible (R2 = .004), making the relative weight analysis meaningless. The three needs equally predicted introjected regulation (35%, 36%, and 29%, respectively, for autonomy, competence, and relatedness;
R2 = .06; all positive associations). Identified regulation (R2 = .30) was most strongly
predicted by competence (44%), followed by autonomy (30%) and relatedness(26%). Finally, intrinsic motivation (R2 = .38) was predicted in a similar way byautonomy (39%) and competence (42%), but relatedness was less important (18%).
We computed a weighted-covariance generalized least squares meta-analytic
structural equation modeling with multiple imputation (W-COV GLS MASEM
with MI; Lv & Maeda, 2020). This analysis tested this model: autonomy support
from teachers and parents → need satisfaction → academic motivation types.
Results are presented in Figure 3. The direct links from autonomy-supportive
behaviors to motivation types were also tested. Unfortunately, the number of12

Figure 3. Obtained path model.
Note. All paths shown are averages between the three models, with minimum and maximum values
shown. Direct paths from need support to motivation variables were left out for clarity purposes.
Paths below .10 in all models are shown in lighter gray.

samples that included parental autonomy support were insufficient (Mk = 8.89) tocompute imputed values for these correlations (van Buuren & GroothuisOudshoorn, 2011). Since it was not possible to run a model with all 10 variables
using MI, three models were computed. A first model (Model 1; kmin = 2, kmax =71, Mk = 38.4, SDk = 21.5) included all 10 variables, used the same W-COV GLSMASEM approach, but used pairwise deletion (PD; Lv & Maeda, 2020) instead ofmultiple imputation. A second model (Model 2; kmin = 7, kmax = 71, Mk = 45.8,
SDk = 17.1) used the W-COV GLS MASEM with MI approach but did not include
parental autonomy support. For comparison purposes, a third model (Model 3; kmin
= 7, kmax = 71, Mk = 45.8, SDk = 17.1) used the W-COV GLS MASEM with PD,
as in Model 1, and did not include parental autonomy support, as in Model 2.
Model results are presented in Table 4.
Across the three models, intrinsic motivation was the motivation type with themost explained variance (mean R2 = .37), followed by identified regulation (mean
R2 = .32) and amotivation (mean R2 = .14). Mean R2 for introjected (.07) andexternal regulation (.01) were much smaller. Competence was the strongest predictor of amotivation (β = −.23 to −.30), identified regulation (β = .31 to .34),
and intrinsic motivation (β = .33). Autonomy also predicted intrinsic motivation(β = .25 to .27). Teacher autonomy support, in turn, was a moderately strong
predictor of autonomy (β = .42 to .62), competence (β = .34 to .45), and relatedness (β = .27 to .50). In terms of predicting motivation types, teacher autonomy
13

14

AutonomyCompetence
Relatedness
Amotivation
External
Introjected
Identified
Intrinsic
AutonomyCompetence
Relatedness
Amotivation
External
Introjected
Identified
Intrinsic
AutonomyCompetence
Relatedness
Amotivation
External
Introjected
Identified
Intrinsic

Outcome
.33
.24
.26
.14
.01
.07
.32
.36
.38
.20
.25
.16
.01
.07
.33
.40
.28
.20
.16
.13
.01
.06
.32
.36

.42
.34
.27
−.13 (−.10)−.05 (−.03).03 (.08).19 (.17).07 (.24)
.62
.45
.50
−.18 (−.14)−.08 (.02).04 (.14).19 (.24).13 (.32)
.53
.44
.41
−.12 (−.12)−.03 (−.02).05 (.12).18 (.23).06 (.30)

Teacher AS
.26
.24
.33
.05 (−.08).08 (−.01).07 (.05)−.01n.s. (.13)−.06 (.16)
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—

Parent AS

.07
−.04
.10
.11
.26

.01
.01n.s.
.11
.05
.25

.06
−.05
.09
.12
.27

Autonomy

Antecedent

−.30
−.07
.14
.31
.33

−.23
−.05
.12
.34
.33

−.30
−.07
.13
.31
.33

Competence

−.07
.07
.01n.s.
.07
.03

−.08
.07
.05
.11
.03

−.08
.05
−.01n.s.
.08.05

Relatedness

Note. All models use a weighted covariance generalized least squares pooling method for the correlation matrix and a ML estimator for fitting the SEM model. Models are fully
identified (no fit indices provided). N for Models 1 and 3 are harmonic mean (9,079 and 13,812, respectively), N for Model 2 is the sum of total participants (76,477; Lv & Maeda,2020). Coefficients are direct effects. Indirect effects through all needs globally are in parentheses. Unless otherwise stated, all coefficients are significant at the p < .01 level. AS =autonomy support; PD = pairwise deletion; MI = multiple imputation.

3 PD—No
parent AS

2 MI—No
parent AS

1 PD—All
variables

Model

R2

Table 4
Meta-analytic structural equation modeling results

Pathways to Student Motivation

support predicted, with both direct and indirect effects through the three needs,
amotivation (β = −.12 to −.18; βindirect = −.10 to .14), identified regulation (β =.18 to .19; βindirect = .17 to .24) and intrinsic motivation (β = .07 to .13; βindirect =.24 to .32). Prediction of introjected and external regulation from teacher autonomy support were weaker, although there were consistent small positive indirect
effects on introjected regulation (βindirect = .08 to .14). In the model including bothteachers and parental autonomy support, direct and indirect paths from teacher
autonomy support to self-determined motivation types and amotivation werestronger than those from parental autonomy support. In fact, all direct predictions
from parental autonomy support to motivation types were close to null whenteacher autonomy support and needs were taken into account. Parental autonomy
support only accounted for some indirect prediction in identified and intrinsic
motivation types (βindirect = .13 and .16, respectively).
Moderation analyses were conducted on meta-analytic correlations to examine the role of student age, gender, educational context, and scale used on the
results, as well as the possibility of publication bias (see Tables S2 to S5 in the
supplemental materials for detailed results). Two moderating effects were noticed
based on student age. First, it appeared that associations between autonomy support from parents and amotivation, identified regulation and intrinsic motivation
diminished as students grew older. A second pattern concerns the prediction ofamotivation by the needs for autonomy and competence. For these two relationships, the correlation became more negative as mean age in the sample increased,
indicating that needs became more predictive of amotivation as students’ age
increased. Only one notable effect was moderated by gender. When the proportion of males in a sample increased, the correlation between competence andboth identified and intrinsic motivation types increased. This indicates that competence may relate to identified and intrinsic motives more strongly for males
than for females.
Regarding education context (i.e., classroom education vs. physical education), the correlations between amotivation and both autonomy and relatedness issubstantially more negative in the classroom samples as compared to the physicaleducation samples. However, the relationship between competence and amotivation is consistent across these groups. It is also worth noting that the associations
between predictors and identified and intrinsic motives were generally higher in
PE classes. When comparing results from various motivation scales (the AMS,
the PLOC, and the BREQ), identified and intrinsic motivation types measured
through the AMS had generally weaker positive relations with psychologicalneeds and autonomy support than when measured with the PLOC or BREQ.
Furthermore, the strength of the relations between external regulation and various
antecedents were different across scales. While external regulation measured with
the AMS shows weak positive associations with needs and autonomy support, theother scales rather show negative associations between needs or autonomy support and external regulation.
Finally, tests of publication bias were conducted which showed some evidence of bias. Specifically, several relationships involving teacher autonomy
support may be influenced by publication bias. For this variable, trim and fill
analysis as well as Eggar’s regression test both indicated potential missing
15

Bureau et al.

studies that could have found smaller or nonsignificant relationships between
teacher autonomy support and three types of motivation (introjected, identified,and intrinsic). It was estimated that their true effect sizes be potentially closer to.09, .29, and .29, as opposed to results from the collected data which estimate
these effects at .17, .44, and .48. Subgroup analysis found similar differenceswith published studies reporting on average higher effects sizes than unpublishedstudies in regard to these three correlations, though differences were not as large
in this analysis. Although this potential case of publication bias suggests smaller
effect sizes, it should be noted that effects would remain in the same direction
and lead to similar interpretation.
Discussion
Self-determination theory specifies that need-supportive contexts should leadto highly self-determined student motivation. Autonomy supportive behaviors
from teachers and parents are thus expected to foster self-determined motivation
through the satisfaction of students’ psychological needs. This sequence has been
supported by a large body of research that has been synthesized and meta-analyzed in this research. The current research aimed to comprehensively test theneed-based pathways through which teachers and parents are able to influence
student motivation in order to identify intervention targets through which various
types of motivation could be facilitated and sustained. It complemented previous
meta-analytic investigations on student motivation that were either restricted tophysical education (Vasconcellos et al., 2020) or focused on outcomes of student
motivation (Howard et al., 2021). Results globally show that autonomy-supportive behaviors from teachers and parents and individual needs relate in similar
ways to motivation types. However, their unique predictive strength also varies
greatly, leading to interesting implications.
The Roles of Competence and Autonomy
With regard to needs and motivation, competence seems to be the driving factor in predicting intrinsic motivation, identified regulation, and amotivation. A
previous meta-analysis (Howard et al., 2021) has shown that intrinsic motivation,
identified regulation, and amotivation were the motivation types with the strongest predictive power in their associations with student achievement, engagement,
well-being, and self-evaluation outcomes. The current results thus concur withrecent studies positioning the need for competence as the central and most important need for students (Levesque-Bristol et al., 2020). Furthermore, these results
concur with Vasconcellos et al.’s (2020) findings that competence need satisfaction has the strongest association with students’ autonomous motivation and amotivation in the physical education context. The current results drawn from both
RWA and MASEM showed the need for autonomy to be mostly effective in predicting intrinsic motivation, to a similar degree as the need for competence, butmuch less in predicting other motivation types. This is notable as a recent metaanalysis in the work context (Van den Broeck et al., 2016) showed that the needfor autonomy was the driving factor in explaining motivation types and work
outcomes. This does not seem to be the case in education as both our results andthose of Vasconcellos et al. (2020) show autonomy to be secondary to competence
16

Pathways to Student Motivationin predicting academic motivation. This difference between the two contexts may
be due to the saliency of achievement in the education context. Despite work output also being subject to evaluations by organizations, those appraisals remain
secondary to working tasks. In contrast, evaluations in school are central in almost
every aspect of students’ experience, serving the multiple purposes of motivating
schoolwork, determining how much learning occurred, ranking students, andestablishing their potential for future academic and professional endeavors. Thosecontext-differing results could also be due to a greater personal weight awarded to
the need for autonomy as we age, a hypothesis that has been advanced in both theeducational (Ratelle et al., 2007) and work contexts (Truxillo et al., 2012).
However, such a moderation effect was not found in our results.
The Role of Relatedness
When compared with students’ autonomy and competence, students’ relatedness was a weaker factor in its association with all motivation types. Even thoughrelatedness remains a positive factor in explaining types of self-determined motivation, it seems that feelings of belongingness and meaningful connections at
school may not be as important for student motivation as it could be for otherfacets of the student experience (e.g., intention to leave the program; Hilts et al.,2018). This finding also aligns with meta-analyses from other contexts which
consistently find relatedness to be a positive antecedent of motivation types withsmaller effect sizes compared with needs for autonomy and competence (Nget al., 2012; Van den Broeck et al., 2016; Vasconcellos et al., 2020). The importance of relatedness, or of one need over others, may be a question of context.
While achievement contexts (i.e., school, work) may make our fundamental connection to others secondary to our own experience of competence and performance, perhaps this is not the case in other contexts such as leisure. It is also
possible that the cultural context moderates the importance of relatedness, as
results have shown belongingness to school being particularly important for students of color (Gray et al., 2018).
The Role of Autonomy Support
Even though the need for autonomy was not the strongest driving factor in
predicting self-determined academic motivation, teachers and parents who created an environment that supported students’ autonomy still contributed positively to the experience of self-determined academic motivation. Teacher
autonomy support generally showed small but significant predictions of students’amotivation, introjected regulation, identified regulation, and intrinsic motivation. These effects were largely mediated through the satisfaction of basic psychological needs. Still, significant small direct effects to identified regulation andamotivation were also recorded, suggesting that teachers’ autonomy-supportive
behaviors may contribute to the development of those motives through other pathways. When teachers explain why a topic is important for students, it is possiblethat the latter will value this topic better (i.e., identified regulation) before they
start feeling competent or autonomous in this class, which would explain the
direct association between teacher autonomy support and identified regulation.
When considering that identified regulation is a primary predictor of17

Bureau et al.

persistence-based outcomes (Howard et al., 2021), the current results indicate thatteachers play a particularly important role in promoting persistence in educational
contexts through facilitating perceived meaningfulness of educational tasks.
Teacher autonomy support also directly predicts amotivation, meaning that a lackof autonomy support could be demotivating even for students who feel competentand autonomous in a subject.
In contrast with the important role of teacher autonomy support, parental
autonomy support showed minor associations with student motivation. Althougha moderate predictor of needs, parental autonomy support was only a weak predictor of identified and intrinsic motivation. Moreover, the association between
parental autonomy support and academic motivation types were almost entirely
mediated by need satisfaction. These results show that autonomy support has alargely different effect on student motivation depending on its source, which
could be explained by the differing roles teachers and parents play when being
autonomy-supportive. Indeed, teacher autonomy support is enacted to directly
improve students’ academic experience and learning, while parental autonomy
support has a more general goal of helping children grow as volitional individuals
and is generally not specifically focused on their academic experience. This holistic parental influence may determine the degree of need satisfaction students
begin their schooling with but ultimately loses part of its influence as students
age, as shown by significant moderation effects in the current study. These expectations are in line with Vallerand’s (1997) hierarchical model of motivation thatwould predict school factors to be more predictive of school motivation and outcomes compared to factors occurring outside of this environment.
Taken broadly, these results validate SDT’s claim that need satisfaction inschool can be a strong global predictor of student motivation. Therefore, any
agent that can contribute to students’ need satisfaction at school are possibly goodtargets for fostering motivation in students. However, the motivational role ofteachers goes beyond merely satisfying students’ psychological needs at school as
indicated by the significant direct effects from teacher autonomy support to motivation types. These results concur with Hattie’s (2009) meta-analysis of 800+
student achievement predictors showing that teachers are at the forefront of learning experiences for students and are likely to have the strongest influence on student motivation.
Introjected and External Regulation
The current results show minor associations between psychological need satisfaction and introjection. Furthermore, despite meta-analytic correlations showing
a small association between both autonomy support sources and introjected regulation, these associations did not hold when needs and autonomy support predicted introjection within a single model. Since introjected regulation is associatedwith both adaptive and maladaptive educational outcomes (Howard et al., 2021),
our results imply that need satisfaction may also carry some “hidden” negativeeducational consequences when it is associated with introjected regulation,although this effect is most likely very minor.
However, autonomy support and needs did not predict external regulation. Because being self-determined means not only having high levels of18

Pathways to Student Motivation

autonomous motivation, but also low levels of controlled motivation, thefact that the main antecedents identified by the theory do not relate to acentral type of motivation begs the question: What situations influence
students to become more or less externally regulated? A first possibility isthat need satisfaction and autonomy support scales are not measuring thefull extent of the construct. In other words, need frustration and autonomy
controlling teacher/parent behaviors may be important aspects in predicting external, and possibly introjected, regulations. Need frustration variables (i.e., autonomy frustration, competence frustration and relatedness
frustration) are increasingly being seen as important in SDT (Chen et al.,2015; Vansteenkiste et al., 2020). Recent results in the field of physicaleducation indicate that they are distinct from need satisfaction and contribute to the prediction of the controlled forms of motivation (Warburton
et al., 2020). However, controlling behaviors from parents and teachers
have not been identified as reliable predictors of students’ external regulation (Noels et al., 1999).
Moderating Influences
Associations between motivational types and psychological needs varied
depending on participants’ characteristics. Older students seem to benefit lessfrom parental autonomy support, showing lower associations with intrinsic andidentified motivation types. This indicates that parental support might bear its
highest importance as students navigate the early stages of their educational
career. However, a more negative association between parental autonomy support
and amotivation as students get older indicates that the importance of parental
support in this regard increases over time. Amotivation is also more strongly negatively predicted by autonomy and competence as students grow older. This finding might be particularly important considering the general decline in school
motivation that occurs as students progress from one grade to the next (Scherrer& Preckel, 2019). With regard to gender, samples with more male participants
showed stronger associations between competence and autonomous motivation
types (intrinsic and identified). This may indicate a stronger need for boys, compared to girls, to satisfy their competence in order to develop and cultivate autonomous motivation types.
Furthermore, the context, whether classroom-based or in physical education, seems to be an important moderating factor for associations between
some psychological needs and motivation types. While competence in general was similarly linked to motivation types in both contexts, this was not
the case for autonomy and relatedness. Their respective negative predictionof amotivation was much stronger in classrooms than in physical education.
In classroom-based education, high levels of autonomy, relatedness, andcompetence seem important to avoid amotivation. This might not be the casein physical education where competence could be the most important factor
for motivation quality. This interpretation is corroborated by Vasconcellos
et al.’s (2020) meta-analytic results showing competence to outweigh autonomy and relatedness in predicting most motivation types in physicaleducation.
19

Bureau et al.

Another contextual moderation was that associations between psychologicalneeds/autonomy support and autonomous motivation types (intrinsic, identified) were much stronger in physical rather than classroom-based education.
However, a similar distinction was also noted depending on scale used, where
studies using the AMS had weaker associations between needs/autonomy support and autonomous motivation types compared to those using the BREQ (andPLOC). It is possible that this distinction between the two contexts is a methodological artefact derived from varying scale properties. In the same vein, theAMS shows eerily positive associations between external regulation and antecedents, an abnormal phenomenon already stressed in previous research (seeHoward et al., 2017, Figure 5).
Practical Implications
Results of this meta-analysis reify the importance of teacher autonomy
support for students’ autonomous motivation. Specifically, they show thatregardless of age, school level, nationality, or gender, autonomy support predicts autonomous types of motivation, thereby providing support for existing
interventions designed to increase student need satisfaction and motivation
through autonomy-supportive practices (Su & Reeve, 2011; Vasquez et al.,2016). In line with our results, these interventions will be especially effective
if they target teachers in the first place instead of parents. However, solely
focusing on more training is not enough. There is a need for a more carefulattention on the features of schooling that constrain the autonomy support thatteachers can give to students. It is well-known that teachers face various pressures from employers and students that may limit their capacity to support
student’s autonomy (Pelletier et al., 2002). For example, high-stakes testing or
pay-by-performance for teachers may put pressure on teachers that could limit
their capacity to provide autonomy support: They may be more tempted to use
controlling strategies such as rewards and punishment to try and coerce students into attaining various imposed educational goals. Thus, even if professional development programs have demonstrated their usefulness in shaping
teacher autonomy-supportive practices (Guay et al., 2020), we argue that further improvements are needed to create the conditions in which teachers possess the effective tool to bring change to their classroom. Implementing these
programs in a context of high pressure is likely to be a waste of resources
given the structural inability for teachers to implement the acquired qualifications. Rather, reforms from bottom-up helping teachers adapt their practice(Darling-Hammond, 1994) would allow the necessary margin needed to integrate autonomy-supportive teaching practices.
Our results also emphasize the importance of the need for competence. In thisregard, teachers may need to go beyond autonomy-support to embrace specificbehaviors known to increase student competence, such as high-quality feedback(Hattie & Timperley, 2007). According to Hattie and Timperley (2007), the main
purpose of feedback is to diminish the gap between students’ current performanceand their expected performance. Whether it helps students develop their skills orreach more realistic expectations, both situations are expected to increase the needfor competence (Ryan & Deci, 2017). While this suggestion is speculative because
20Pathways to Student Motivation

our findings do not focus on teacher feedback, it is well anchored in the field of
motivation (Wisniewski et al., 2020).
Limitations and Conclusion
A major limitation of this meta-analysis is that it is based on correlations, limiting conclusions regarding causality. Specifically, even though each part of thetested model (Figure 2) has previously been tested and some causal links verified(Froiland, 2011; Guay et al., 2016; Hausmann et al., 2009; Patall et al., 2010), thecurrent study cannot speak definitively on the issue of causality as our results may
be attributed to other variables. Future research should thus move away fromcross-sectional data and employ more sophisticated methodological designs
including longitudinal, experience sampling, and quasi-experimental designs.
It must be noted that our search found relatively few studies examining the association between parental autonomy support and academic motivation types. Likewise,
autonomy support from peers was studied in very few articles falling within our literature review and was therefore not included in main analyses. This shortcoming
represents a gap in the literature. Additionally, the limited body of research addressing teacher and parental control, as well as other need-supportive behaviors (competence support and relatedness support; Ratelle et al., 2021) restricted our ability to
implement these variables in the current analysis, even though they fall within thetheoretical scope of motivational antecedents theorized by SDT. This was also thecase for need frustration variables (Vansteenkiste & Ryan, 2013). A further limitation
concerns possible publication bias. Even though steps were taken to collect unpublished work, and estimates correcting for publication bias indicate only minor influences, the fact remains that estimates relating to teacher autonomy support werepossibly inflated in the current study due to this bias. As such, results concerning thisvariable should be interpreted with caution. Further meta-analytic research buildingon this study in the future may be helpful in clarifying this issue.
In conclusion, the present investigation comprehensively reviewed the theoretical antecedents of student motivation through the perspective of SDT. As expected,
results highlight that autonomy-supportive behaviors from teachers and parents, aswell as autonomy, competence, and relatedness satisfaction, are all generally associated with students’ self-determined motivation. More specifically, satisfaction of
the competence need was found to be particularly highly related with intrinsic andidentified motives, indicating its centrality in the education context. Teachers’autonomy support was the strongest predictor of motivation through need satisfaction, although parental autonomy support was a positive but weaker antecedent,
ultimately implying the importance of proximal sources of support.
ORCID iD
Julien S. Bureau

https://orcid.org/0000-0001-7105-2500

Notes
The full dataset and all associated supplementary materials are made available throughOpen Science Framework. All files can be found with the following link: https://osf.io/
pje9t/?view_only=b8028e21a5f3450383e368f3668b9245

21

Bureau et al.
This study was made possible through funding from the Social Sciences and Humanities
Research Council of Canada to the first and fourth authors and from the Fonds de recherche
du Québec–Société et culture to the first author, and internal funding from Monash
Business School awarded to the second author. We thank Geneviève Morneau-Vaillancourtfor her help in gathering part of these data.
1 Other candidate antecedents such as need frustration as well as competence/relatedness support were also included in the literature search. However, their low rate of inclusion in studies measuring student motivation prevented us from including them in theanalysis as antecedents.
2 In the Lv and Maeda (2020) appendix showing full code for this procedure, there is atypo that short-circuits the multiple imputation. This was corrected in the present analysis.
See supplemental material for the full R code for this analysis.

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Authors
JULIEN S. BUREAU is an assistant professor at the Faculty of Education at Université
Laval, 2320 rue des Bibliothèques, Quebec City, Quebec, Canada G1V 0A6; email:julien.bureau@fse.ulaval.ca. His research program is focused on studying motivation,
integrity, and equity in education with the use of advanced quantitative methods. He
teaches research methods and educational equity.
JOSHUA L. HOWARD is a senior lecturer in the Department of Management at Monash
University, 900 Dandenong Road, Caulfield East, Victoria 3145, Australia; email: josh.
howard@monash.edu. He studies the fundamentals of motivation through the application of psychometric and meta-analytic tools.
JANE X. Y. CHONG is a postdoctoral research associate at the Future of Work Institute at
Curtin University, 78 Murray St, Perth, Western Australia 6000, Australia; email: jane.
chong@curtin.edu.au. Her research program is centered on work motivation in newcomers in both work and volunteer settings, with additional interest in motivational
functioning in wider contexts.
FRÉDÉRIC GUAY holds the Canada research chair in motivation, perseverance, andschool success and is a full professor at the Faculty of Education at Université Laval,
2320 rue des Bibliothèques, Quebec City, Quebec, Canada G1V 0A6; email:frederic.guay@fse.ulaval.ca. His research program has been organized by self-determination theory and self-concept theory. He is currently the director of a professional
training program for elementary school teachers (i.e., CASIS) aiming to motivate teachers to use pedagogical practices that support students’ motivational resources.

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