Martin, A.J., Ginns, P., & Papworth, B. (2017). Motivation and engagement: Same or different?
Does it matter? Learning and Individual Differences, 55, 150-162. DOI:
This article may not exactly replicate the authoritative document published in the journal. It is not
the copy of record. The exact copy of record can be accessed via the DOI:
Motivation and Engagement: Same or Different? Does it Matter?
Andrew J. Martin
School of Education
University of New South Wales, Australia
Paul Ginns and Brad Papworth
Faculty of Education and Social Work
University of Sydney, Australia
Requests for further information about this investigation can be made to Professor Andrew J.
Martin, School of Education, University of New South Wales, NSW 2052, AUSTRALIA. E-Mail:
email@example.com. Phone: +61 2 9385 1952. Fax: +61 2 9385 1946.
The authors would like to thank all participating schools, the Australian Research Council (Grant #
LP0990853), and the Australian Boarding Schools Association for their assistance and support in
Motivation and Engagement:
Same or Different? Does it Matter?
Submission Date: February 2017
Based on a sample of 5,432 high school students, findings from CFA showed that well-established
constructs can be demarcated into adaptive and maladaptive motivation and engagement. SEM
showed there is commonality in the extent to which socio-demographic, prior achievement, and
personality variables predict motivation and engagement, but there are also differences in the extent
to which some motivation and engagement factors are predicted by these antecedents. There are
also commonalities in the extent to which motivation and engagement factors predict outcomes;
however, when considering outcomes in more thematic terms, some divergence in predictive
patterns emerges. Further, based on a longitudinal sub-sample of 2,002 high school students, model
fit and explained variance provide tentative support for the claim that prior motivation is an impetus
for subsequent engagement; however, there is also a role for prior engagement predicting
subsequent motivation, ultimately suggestive of a cyclical process. Discussion centers on how these
motivation and engagement alignments and distinctions matter.
Keywords: motivation; engagement; wellbeing
Motivation and Engagement: Same or Different? Does it Matter?
• This study explores alignment and differentiation between motivation and engagement
• Findings confirm adaptive and maladaptive motivation and engagement dimensions
• Alignments and differences exist in factors predicting motivation and engagement
• Alignments and differences exist in outcomes predicted by motivation and engagement
• It appears prior motivation is an impetus for subsequent engagement
Motivation and Engagement:
Same or Different? Does it Matter?
There has been ongoing debate as to whether and how motivation and engagement differ
(Reschly & Christenson, 2012). Major theoretical frameworks have been developed to articulate
what motivation and engagement factors exist and where they reside in the learning process (e.g.,
Fredricks, Blumenfeld, & Paris, 2004). Inappropriately or erroneously conflating (or differentiating)
motivation and engagement can perpetuate theoretical ambiguity, raise validity challenges for
measurement and research, and lay an inadequate foundation for educational intervention (Martin,
2012; Reschly & Christenson, 2012). There are many theories and conceptual frameworks that seek
to describe and explain academic motivation and engagement. With a view to gaining greater clarity
in this space, there have been calls for more integrative approaches to motivation and engagement
research and theorizing (Reschly & Christenson, 2012; Murphy & Alexander, 2000; Martin, 2007,
2009; Pintrich, 2003).
As integrative approaches are pursued, an important issue to address is the relative alignment
and diffusion of motivation and engagement constructs. Using an existing multidimensional
motivation and engagement model (the Motivation and Engagement Wheel; Martin, 2007, 2009) as
a guiding framework, exploring these alignments and distinctions is the major purpose of the
present study. In so doing, we seek to answer the following questions: How distinct are motivation
and engagement? Do different socio-demographic and individual differences predict motivation and
engagement constructs in aligned or distinct ways? Do motivation and engagement constructs predict
academic and personal outcomes in aligned or distinct ways? Is motivation more salient in predicting
subsequent engagement – or vice versa? In addressing these questions, we aim to further contribute to
the ongoing debate and deliberations playing out in the motivation and engagement domain. Through
addressing these questions, in numerous ways the study seeks to extend prior work including:
explicit attention to better understanding higher order motivation and engagement dimensions (most
prior attention has been given to first-order components), exploration of personality predictors,
attention to a wider set of outcome factors (e.g., memorization, elaboration), and examination of the
longitudinal ordering of motivation and engagement.
1. Theories Explaining and Describing Motivation and Engagement
For the purposes of this study, motivation is defined as the inclination, energy, emotion, and drive
relevant to learning, working effectively, and achieving; engagement is defined as the behaviors that
reflect this inclination, energy, emotion, and drive (Martin, 2007, 2009). The bulk of theorizing in this
space has focused on motivation. Engagement has been present in this research, but only recently has it
received more substantial theoretical attention (e.g., see Fredricks et al., 2004; Reschly & Christenson,
2012). Accordingly, we first review the majority concepts in motivational theorizing. We then
summarize the more recent ideas on academic engagement. In regard to motivation, Pintrich (2003)
emphasized the importance of considering and conceptualizing motivation in terms of salient and
seminal theorizing related to: self-efficacy (and related expectancies), valuing, need achievement, self-
worth, attributions, control, goal orientation, self-regulation, and self-determination. The present study
explores a framework seeking to integrate this theorizing and in doing so, examines the extent to which
motivation and engagement can be considered distinct – and if so, how this distinction is empirically
manifested. Following Pintrich’s (2003) emphasis on core concepts in motivational science (e.g., self-
efficacy, valuing, need achievement, etc.), we briefly review these theories as a core foundation for
understanding students’ motivation. We then review some recent work into engagement. Bringing key
elements of motivation and engagement perspectives together, we then describe an operational model
(the Motivation and Engagement Wheel; Martin, 2007, 2009) that seeks to synthesize these theoretical
1.1 Self-efficacy and Expectancy-value Theories
In most of the motivation and engagement literature, appraisals of one’s competence are
salient. These take the forms of self-concept, perceived competence, self-efficacy, and expectancies.
In this article, the focus is on self-efficacy (see Marsh, 2007 for a review of self-concept; Harter,
1999 for a review of perceived competence; Wigfield & Eccles, 2000 for a review of expectancies).
Self-efficacy refers to the appraisals students make about their task-related academic capacity.
According to Bandura (2001; Schunk & Mullen, 2012), students high in self-efficacy function
better in the classroom (usually by way of elevated effort and persistence) and more effectively
respond to challenge through enabling and agentic cognitive and emotional processes. Other
theoretical perspectives identify factors that operate in tandem with self-efficacy to yield desirable
academic outcomes. One salient ‘additive’ model is expectancy-value theory (Wigfield & Eccles,
2000). Here, alongside positive expectations based on one’s perceived competence, the important
role of ‘valuing’ is considered. Accordingly, students holding high task-related expectations (or,
self-efficacy) and who also value the task are more motivated to carry out that task and achieve at a
higher level (Martin, 2007, 2009; Wigfield & Eccles, 2000).
1.2 Need Achievement and Self-worth Motivation Theories
Need achievement and self-worth motivation theories characterize students in terms of how
they perceive and respond to success and avoid failure (Covington, 2000). Following from this,
three major student typologies emerge: success-oriented, failure-avoidant, and failure-accepting
(Martin & Marsh, 2003). Students who are success-oriented are proactively oriented towards their
studies, are optimistic and energetic when responding to academic setback, and reflect high self-
efficacy and sense of control or agency (Martin, Marsh, & Debus, 2003). The failure avoider tends
to be characterized by a fear of failure that can play out in many ways, including self-handicapping
(i.e., actively handicapping one’s chances of success to have an excuse in the event of poor
performance – e.g., through procrastination or insufficient study) (Covington, 2000; Martin &
Marsh, 2003). The failure accepter has abandoned effort and given up, often seen in terms of
disengagement or learned helplessness (Covington, 2000).
1.3 Attribution and Control Theories
The causes individuals attribute to events have a significant bearing on their subsequent
behavior, cognition, and affect (Weiner, 2010). Attribution theory formally conceptualizes the
causes individuals ascribe to past events. Under attribution theory, causes vary along three main
dimensions: stability, locus, and controllability (Weiner, 2010). Stability refers to the extent to
which the cause is temporary (vs. consistent across time), variable (vs. unable to change), and
situational (vs. global). Locus refers to the extent to which a cause is seen by the individual as
internal or external. The control dimension refers to students’ belief that they have significant
determination and influence in attaining success or avoiding failure. In contrast, an uncertain sense
of control reflects an uncertainty about one’s capacity to attain success or avoid failure (see Patrick,
Skinner, & Connell, 1993)
1.4 Goal Orientation and Self-regulation Theories
Goal (orientation) theory seeks to explain the reasons and underlying goals students have for
their achievement-related behaviors. The “classic” goal orientation framework focuses on mastery
and performance goals. Subsequent theorizing and research suggested an extension on this
perspective that incorporated avoidance and approach dimensions (Anderman & Patrick, 2012;
Elliot, 2005). Mastery approach reflects goal striving aimed at effort, skill development, and
learning. Performance approach reflects goal striving aimed at demonstrating relative ability and
outperforming others (Elliot, 2005). Performance avoidance reflects goal striving that is focused on
the need to avoid appearing incompetent or to disappoint others (Elliot, 2005). Mastery avoidance
reflects goal striving that is centered on a desire to avoid a loss of competence, mastery, skill, or
knowledge (Elliot, 2005). It is also useful to consider ways that goal orientations are operationalized
in students’ academic lives and the other motivation and engagement theories that this invokes.
Taking one of the more adaptive of the goals – mastery approach (Martin, 2013a) – as a case in
point, it has been proposed that self-regulatory theories have been influential in identifying key
factors and processes by which mastery goals are operationalized by students. For example,
Zimmerman and Campillo (2003; see also Zimmerman, 2002) developed a model reflecting the
phases and subprocesses of self-regulation. The model demarcated inner psychological factors such
as motivation from the more action-oriented engagement factors. Accordingly, it represented a
“forethought phase” comprising motivation (e.g., goals, self-efficacy) and a “performance phase”
comprising the efforts and engagement to act in a self-regulated way. Factors such as planning and
monitoring behavior, task management, and persistence (e.g., Martin, 2013b; Zimmerman, 2002)
1.5 Self-determination Theory
Self-determination theory (SDT; Deci & Ryan, 2012; Ryan & Deci, 2010) distinguishes
between intrinsic motivation (motivation reflecting an inherent interest in or satisfaction with an
activity) and extrinsic motivation (motivation with more of an external impetus such as reward,
approval, or grades). SDT also emphasizes individuals’ psychological needs and the importance of
meeting these needs for optimal wellbeing (Reeve, 2012). Three needs are particularly key: the need
for autonomy, the need for competence, and the need for relatedness. According to Ryan and Deci
(2010), “intrinsic motivation flourishes under conditions supporting autonomy and competence and
wanes when these needs are thwarted” (p. 174). In terms of recognizable motivational factors, two
of these needs are salient: the need for competence (including self-efficacy) and the need for
autonomy (including control).
1.6 Demarcation of Motivation and Engagement
As noted, the bulk of research over the past few decades has focused on motivation. Also as
noted, engagement has been present in this research, but not a source of major conceptualizing.
More recently, researchers have sought to provide clearer ideas on how motivation and engagement
are distinct. Here we explore some of these ideas as relevant to the present study.
Reeve (2012) has observed that motivation comprises “private, unobservable, psychological,
neural, and biological” factors, whereas engagement comprises “publicly observable behavior” (p.
151). Similarly, Cleary and Zimmerman (2012) identified engagement as representing observable
(behavioral) and internal (cognitive and affective) factors. Ainley (2012) positions motivation as an
inner psychological factor and engagement as reflecting one’s involvement in an activity. In similar
vein, Kuhl (1985) had earlier identified the post-decisional phase of learning (see above) as one that
“energizes the maintenance and enactment of intended actions" (p. 90). Anderman and Patrick
(2012) draw on the tripartite engagement framework of Fredricks and colleagues (2004) and
demarcate engagement into its emotional, cognitive and behavioral terms. Schunk and Mullen
(2012) also describe engagement in tripartite terms and identify motivation as an internal force that
energizes these. Voelkl (2012) emphasizes affective and behavioral factors, with motivation
aligning with the former and engagement with the latter. A more differentiated approach has been
proposed by Pekrun and Linnenbrink-Garcia (2012) who describe motivation and engagement in
motivational (e.g., goals), cognitive (e.g., attention, memory), behavioral (e.g., effort, persistence),
cognitive-behavioral (e.g., self-regulation), and social-behavioral (e.g., social on-task behavior)
Taken together, in their broadest forms, motivation is most typically represented as an internal
factor that has an energizing impetus and engagement is most typically represented as a factor
reflecting more observable, evident, or external constructs. In many cases this is in the form of a
cognitive-behavioral conceptualization, with motivation more often seen in cognitive terms and
engagement in behavioral terms. More recent research and thinking has included emotion and affect
in the frame. However, work on emotion and affect is ongoing and researchers are in the process of
better understanding their place on the motivation-engagement landscape. For example, some will
position affective-emotional factors (such as interest) in motivational terms (e.g., Ainley, 2012;
Voelkl, 2012), whereas others will place affective and emotional factors in the engagement domain
(e.g., Fredricks et al., 2004; Pekrun & Linnenbrink-Garcia, 2012; Reschly & Christenson, 2006).
2. An Integrative Adaptive and Maladaptive Motivation and Engagement Framework
Following from the above theories, the recently developed Motivation and Engagement
Wheel (Figure 1; Martin, 2007, 2009; see also Liem & Martin, 2012) is a framework that captures
all these dimensions. It is multidimensional, demarcated into cognitive and behavioral dimensions
(with an emerging presence of affect/emotion), reflects major theories salient in motivation and
engagement conceptualizing, comprises (first-order) factors subsumed under broader (higher order)
motivation and engagement dimensions, and differentiates factors into adaptive and maladaptive
2.1 The Motivation and Engagement Wheel
The Motivation and Engagement Wheel comprises four higher order factors that are
subsumed by eleven first-order factors. The higher-order and respective first-order factors are: (1)
adaptive motivation (or, adaptive cognition, with some affective representation), reflecting students’
positive attitudes and orientations to academic learning, comprising three first-order factors (i) self-
efficacy (cognitive), (ii) valuing (cognitive-affective; operationalized in terms of utility and importance),
and (iii) mastery orientation (cognitive); (2) adaptive engagement (or, adaptive behavior), reflecting
students’ positive behaviors and engagement in academic learning, comprising three first-order factors
(iv) planned behavior and monitoring (behavioral), (v) task management (behavioral), and (vi)
persistence (behavioral); (3) maladaptive motivation (or, maladaptive cognition, with some affective
representation), reflecting students’ attitudes and orientations inhibiting academic learning, comprising
three first-order factors (vii) anxiety (cognitive-affective), (viii) failure avoidance (cognitive), and (ix)
uncertain control (cognitive); and (4) maladaptive engagement (or, maladaptive behavior), reflecting
students’ more problematic learning behaviors, comprising two first-order factors (x) self-handicapping
(behavioral), and (xi) disengagement (behavioral). Factor analysis in prior research suggests that each of
these 11 factors weighs relatively equally on each of their higher order factors (see Liem & Martin,
2012 for a review).
The Wheel’s development followed calls to devise motivational research that advances
scientific understanding and which also has applied utility (Pintrich, 2003). Its representation was
driven by the need for greater attention to “use-inspired basic research” in the education and
motivation context (Stokes, 1997; see also Greeno, 1998; Pintrich, 2000, 2003). Thus, for example,
although the 11 motivation factors and their four higher order groupings could be characterized in a
2 x 2 framework, it has been presented in visual form (the Wheel) that assists communication with
educators, students, and parents/carers.
2.2 The Motivation and Engagement Scale
The Wheel is accompanied by an assessment tool; the Motivation and Engagement Scale
(MES) (Martin, 2010). It is the MES that is the focus of this study to more closely investigate
motivation and engagement. As described in Method, we do so via factor analysis that seeks to
explore the distinctiveness of hypothesized factors in the Wheel. Liem and Martin (2012)
summarized MES research across multiple studies. They showed that responses to the MES items
and subscales are normally distributed and internally consistent with average Cronbach’s alpha
ranging between .77 and .79 for each factor. Confirmatory factor analysis (CFA) performed at both
the first-order level (i.e., eleven MES first-order factors) and the higher-order level (i.e., the eleven
first-order factors explained by four higher-order factors representing the four higher-order
dimensions in the Wheel) demonstrate excellent fit (CFIs > .95; RMSEAs and SRMRs < .05).
Factor loadings of first-order factors and higher-order factors were statistically significant and
typically > .60. With regards to external validity, Liem and Martin (2012) showed that adaptive
motivation and engagement factors were positively associated with academic outcomes (e.g.,
achievement, homework completion, class participation) whereas maladaptive motivation and
engagement were generally negatively associated with these outcome factors. Higher-order factor
findings demonstrated much the same. Thus, in concert with the internal construct validity
properties, these data further support the MES as a psychometrically robust multidimensional form
of motivation and engagement instrumentation.
2.3 Some Qualifying Notes on the Wheel and MES
Although the MES is the focus of this study, we recognize there are other notable examples of
instrumentation reflecting diverse motivation- and engagement-related dimensions such as Patterns
of Adaptive Learning Survey, PALS, by Midgley et al. (2000); the Motivated Strategies for
Learning Questionnaire, MSLW, by Pintrich et al. (1991); the Student Engagement Instrument, SEI,
by Appleton, Christenson, Kim, and Reschly (2006); and the Inventory of School Motivation, ISM
by McInerney, Yeung, and McInerney (2001) - to name a few. We also make the point that
although major reviews such as that by Pintrich (2000, 2003) have provided guidance on the Wheel
and its conceptualizing, the Wheel does not explicitly reflect the entirety of their frameworks. For
example, Pintrich (2003) identified the roles of interest and intrinsic motivation but these are not
overtly represented in the Wheel. Also, the Wheel does not draw exclusively from Pintrich. For
example, the organization of first-order factors under their higher order themes goes beyond his
work. Also, as noted above, others (e.g., Greeno, 1998; Stokes, 1997) have influenced the Wheel’s
development to be more amenable to application by practitioners and students.
We also make the point that the Wheel’s demarcation into adaptive and maladaptive
dimensions does not ignore the possibility of effects that may be counter to these demarcations. For
example, anxiety (subsumed under maladaptive motivation) may trigger arousal or emotionality
more than worry (see Liebert & Morris, 1967) and this can influence academic outcomes in a
positive manner (Cassady & Johnson, 2002). On a related note, mastery orientation has been
included in the Wheel whereas performance orientation has been excluded. Although there are cases
where performance orientation can yield positive effects (e.g., Harackiewicz, Barron, Pintrich,
Elliott, & Thrash, 2002; Midgley, Kaplan, & Middleton, 2001), mastery orientation has been
included because it yields a body of evidence more consistently positive (Brophy, 2005). Taken
together, there are nuances and qualifications relevant to the Wheel and the MES that are important
to consider when interpreting findings in the present study.
3. Predictors and Outcomes of Motivation and Engagement
Alongside tests of factor structure, we also seek to differentiate motivation and engagement in
terms of their predictors and outcomes.
3.1 The Ordering of Motivation and Engagement
A first consideration of predictors and consequences is the ordering of motivation and
engagement themselves. Does motivation predict engagement or does engagement predict
motivation? For example, Reeve (2012) suggests that students’ inner motivational resources enable
them to better engage in the classroom. Indeed, in experimental work by Reeve and colleagues
(2004), teachers who were trained in motivational (autonomy supportive) practices displayed
significantly more autonomy-supportive behaviors and this in turn promoted students’ engagement.
Anderman and Patrick (2012) point out that goals (motivation) precede students’ cognitive (e.g.,
self-regulation), emotional (e.g., positive affect about school), and behavioral engagement (e.g.,
effort). Cleary and Zimmerman (2012) differentiate ‘will’ from ‘skill’ (see also Covington, 2000),
with ‘will’ reflecting motivation and ‘skill’ reflecting engagement. Schunk and Mullen (2012)
describe engagement as “the manifestation of students’ motivation”. Voelkl (2012) suggests
engagement mediates the relationship between motivation and achievement. Similarly, Pekrun and
Linnenbrink-Garcia (2012) suggest that engagement is a mediator between emotion and
achievement, while Ainley (2012) argues that motivation (by way of interest) leads to achievement
More recent research has suggested much the same, with Froiland and Worrell (2016)
showing that intrinsic motivation to learn was indirectly and positively related to academic
performance via classroom engagement. Moreover, they replicated this relationship among a
sample of African American and Latino students. In a longitudinal study, Froiland and Davison
(2016) found that intrinsic motivation for mathematics predicted taking higher-level mathematics
courses (an index of engagement), which predicted further mathematics achievement. Taken
together, when considering the various theories and recent commentaries on motivation and
engagement, there is suggested an operational process underlying motivation and engagement such
that motivation is the impetus for engagement. As Reschly and Christenson observe, “motivation is
necessary but not sufficient for engagement” (2012, p. 14). This is another issue that the present
study addresses in disentangling motivation and engagement.
3.2 Predictors of Motivation and Engagement
Alongside questions about the ordering of motivation and engagement, this study also
explores known and contended predictors of these constructs. Known predictors include gender
(Liem & Martin, 2012; females more motivated and engaged), age (Martin, 2007, 2009; younger
students more motivated and engaged), and prior achievement (Hattie, 2009; higher achievement
associated with higher motivation and engagement). Predictors with more equivocal findings are
socio-economic status and language background (Martin, Nejad, Colmar, & Liem, 2013) and are
included in this study in order to gain further clarity.
Individuals’ characteristic orientations and dispositions are also known to impact their
cognitive, behavioral, and emotional repertoire (Buss & Cantor, 1989; McCrae & Costa, 1996).
Thus, individual differences are a reasonable inclusion as predictors. However, there is relatively
little research linking personality with motivation and engagement. McCrae and Löckenhoff (2010)
have found that conscientiousness (positively) and neuroticism (negatively) are related to control,
persistence, and self-regulation. Hoyle (2010) has observed that conscientiousness is relevant to the
ways individuals characteristically manage their behavior, suggesting a link to self-regulation and
persistence. de Raad and Schouwenberg (1996) found that extraversion, conscientiousness and
openness are associated with adjustment of one’s personal resources as relevant to self-regulatory
functions. Martin et al. (2013) found that neuroticism was positively associated with self-
handicapping and disengagement, while agreeableness, openness and conscientiousness were
negativity associated with these maladaptive engagement factors. Ginns, Martin, & Papworth
(2014) found openness, agreeableness, and conscientiousness positively and negatively associated
with adaptive motivation and maladaptive motivation respectively. In terms of other research
investigating what might be considered less than optimal motivation factors, Clark and Schroth
(2010) found that agreeableness was the strongest (negative) predictor of amotivation. It is thus
reasonable to posit that personality factors – operationalized via extraversion, openness to
experience, neuroticism, conscientiousness, and agreeableness – have a role in predicting
motivation and engagement. Whereas socio-demographic and prior achievement are quite
commonly included as motivation and engagement predictors, personality is a more novel
contribution to the literature.
3.3 Motivation and Engagement Outcomes
Investigating student outcomes is another means of better understanding and disentangling
motivation and engagement. In considering outcomes by which to better understand motivation and
engagement, we suggest they capture a breadth of students’ educational and personal experience
and wellbeing. Indeed, school involves very specific academic tasks to which the individual must
attend, broader functions that involve class activities and interactions, a sense of place within the
school as a whole, as well as social and emotional development and wellbeing. Thus, to gain an
encompassing perspective on motivation and engagement connections to outcomes, we organize
outcomes along a continuum from specificity to generality.
At the most specific level is task-specific academic activity. This is comprised of elaboration
(the extent to which students employ learning strategies leading to meaningful, personally relevant
understanding of topics) and memorization (the extent to which students employ learning strategies
leading to verbatim representations of the learning materials in long-term memory; Ginns, Martin,
& Papworth, 2014; Marsh, Hau, Artelt, Baumert, & Peschar, 2006). Students’ use of memorization
strategies lead to verbatim representations of the learning materials in long-term memory. To
maintain this material in long-term memory, students must also employ learning strategies such as
linking new information to prior knowledge – elaboration – supporting a more meaningful
understanding of topics (Marsh et al., 2006). Memorization and elaboration, we suggest, thus cover
two distinct aspects of the learning process. Together, they allow for the processing and retention of
factual information and knowledge as well as richer engagement and application with that
information and knowledge.
We also assess classroom-based academic activity that take the forms of cooperation
(following Marsh et al., 2006) and class participation (following Green et al., 2007; Martin, 2007,
2009). Moving towards the more general, we assess out-of-class activity that comprise homework
completion, absenteeism, and extra-curricular activity (following Martin et al., 2013). More general
again, we include school-related wellbeing in the forms of school enjoyment and positive academic
intentions/aspirations (following Green et al., 2007; Martin, 2007, 2009). Finally, at the most global
level, we assess overall personal wellbeing that comprises general self-esteem (Marsh, 2007), life
satisfaction (Diener, Emmons, Larsen, & Griffin, 1985), and sense of meaning and purpose (World
Health Organisation Quality of Life Instrument; WHOQOL, 1998).
4. Aims of the Present Research
The present study seeks to more closely examine academic motivation and engagement, their
alignments, and potential areas of distinction. Based on prior research and theory and using the
Motivation and Engagement Wheel as the conceptual base and the Motivation and Engagement
Scale as the measurement base, we tentatively hypothesize that: (a) motivation and engagement are
distinct factors and can be demarcated into adaptive and maladaptive dimensions, (b) depending on the
factor, different socio-demographics and individual differences will predict different motivation and
engagement constructs in aligned and distinct ways, (c) depending on the factor, motivation and
engagement will predict different academic and personal outcomes in aligned and distinct ways, and (d)
motivation will be more salient in predicting subsequent engagement than engagement is in predicting
subsequent motivation. In testing these hypotheses, we aim to further contribute to the ongoing debate
and deliberations playing out in the motivation and engagement literature.
5.1 Sample and Procedure
Main sample. For the bulk of analyses, participants were 5,432 students from 12 independent
(non-government, non-systemic) high schools, surveyed in 2011. Just over half (57%) the
respondents were male and 43% were female. The sample spanned all high school year levels (13%
Year 7, approx. 12-13 years of age; 18% Year 8, approx.13-14 years of age; 18% Year 9, approx.
14-15 years of age; 21% Year 10, approx. 15-16 years of age; 17% Year 11, approx. 16-17 years of
age; 13% Year 12, approx. 17-18 years of age). The mean age of respondents was 14.39 (SD=1.61)
years. A total of 8% of the sample were from a non-English speaking background. Being from
independent schools, socio-economic status of students (M=1014; based on Australian Bureau of
Statistics index of relative disadvantage/advantage) was higher than the national average (M=1000).
Just under a quarter of students were boarders and the present data (including the longitudinal
sample below) are shared with a previous study exploring for differences between these and day
students (with findings showing predominant parity between the two groups; Martin, Papworth,
Ginns, & Liem, 2014). Because there is greater heterogeneity in academic motivation and
engagement in high school than in elementary school (Martin, 2009), the focus of this investigation
was on high school students. With few exceptions, students in attendance on the day of the testing
completed the survey. Teachers administered the survey during class time. Rating scales and survey
format were explained by the teacher. Students completed the survey on their own and returned it at
the end of class. The study received Human Research Ethics Committee approval from the host university.
Longitudinal subsidiary sample. Longitudinal motivation and engagement data were available
for 2,002 students from the main sample. This allowed for analyses to test the ordering of
motivation and engagement across time. These were pre-test data collected one year earlier (2010).
Testing was conducted one year apart in order to capture a full cycle of an academic year (4 school
terms in Australia). The subsidiary longitudinal sample spanned Years 8-12 (18% Year 8, 23% Year
9; 23% Year 10, 20% Year 11, 16% Year 12). They were from the same 12 high schools in the
main sample, above. Just over half (57%) were male and 43% were female. Respondents’ mean age
was 14.88 (SD=1.36) years. Eight percent spoke a language other than English at home.
The reduced longitudinal numbers is due to the fact a new Year 7 cohort joined schools at
Time 2 and thus could not be included in the longitudinal sample, Year 12 students at Time 1 had
graduated from the school by Time 2 and also could not be included in the longitudinal sample, and
other students had changed schools, were absent on the day of either testing, were engaged in co-
curricular activity at the precise time of testing, or did not have consent to participate at one of the
two time points. To check for any differences between the sample participating at both times and
the sample participating only at one time, Martin, Papworth, et al. (2014) performed tests of
invariance that compared the factor structure for matched and unmatched students on motivation
and engagement, including predictors and consequences in the present study, at both time points
(one year apart). There were comparable measurement properties for the two groups, suggesting
that the longitudinal sub-sample can be considered broadly representative of the overall sample.
There was also no notable difference in the key demographics of gender (matched FM=42%,
M=42%; unmatched FM=42%, M=58%) and age (matched mean=14.90 years; unmatched
Motivation and engagement. Motivation and engagement were measured using the
Motivation and Engagement Scale (MES; Martin, 2010). Whereas most studies using the MES
focus on the eleven first-order factors, the present study (that is centered on broader motivation and
engagement themes) is focused on its higher order factors. Adaptive motivation was assessed via
self-efficacy (e.g., If I try hard, I believe I can do my schoolwork well), mastery orientation (e.g., I
feel very pleased with myself when I do well at school by working hard), and valuing (e.g.,
Learning at school is important). Its intraclass correlation (ICC) as a function of school was .037.
Adaptive engagement was assessed via planned behavior and monitoring (e.g., I try to plan things
out before I start working on my homework or assignments), persistence (e.g., If I don’t give up, I
believe I can do difficult schoolwork), and task management (e.g., When I study, I usually try to
find a place where I can study well). Its ICC as a function of school was .050. Maladaptive
motivation was measured with anxiety (e.g., When exams and assignments are coming up, I worry a
lot), failure avoidance (e.g., Often the main reason I work at school is because I don’t want to
disappoint my parents), and uncertain control (e.g., I'm often unsure how I can avoid doing poorly
at school). Its ICC as a function of school was .032. Maladaptive engagement was assessed via
disengagement (e.g., I’ve pretty much given up being involved in things at school) and self-
handicapping (e.g., I sometimes put assignments and study off until the last moment, so I have an
excuse if I don’t do so well). Its ICC as a function of school was .077. Each factor is operationalized
with 4 items (hence, it is a 44-item instrument) rated on a scale of 1 (Strongly Disagree) to 7
(Strongly Agree). Prior research into the MES has shown a strong factor structure, reliable and
normally distributed dimensions, and significant associations with diverse academic outcomes
(Green et al., 2007; Liem & Martin, 2012). Factor structure, reliability, intraclass correlations (ICCs
as a function of school), skewness, and kurtosis for motivation and engagement factors in the
present sample are shown in Table 1. Reliability, ICCs, skewness, and kurtosis for other variates in
the study are reported below and in Martin, Papworth, et al. (2014).
Academic task activity. Task activity is assessed via memorization and elaboration. All items
were rated on a scale of 1 (Strongly Disagree) to 7 (Strongly Agree). Memorization is measured by
4 items (e.g., When I study, I try to memorize everything that might be covered; Cronbach’s α=.81;
ICC=.041), from the Organisation for Economic Co-operation and Development (OECD)
Approaches to Learning instrument (Marsh et al., 2006). Elaboration is assessed with 4 items (e.g.,
When I study, I try to understand the material better by relating it to things I already know;
Cronbach’s α=.81; ICC=.027), also from the OECD’s Approaches to Learning instrument.
Academic class activity. Class activity is assessed via cooperation (5 items; e.g., It is helpful
to put together everyone’s ideas when working on a project; Cronbach’s α=.82; ICC=.032) from the
OECD’s Approaches to Learning instrument (Marsh et al., 2006) and class participation (4 items;
e.g., I participate when we discuss things in class; Cronbach’s α=.90; ICC=.053) from Green et al.,
2007; Martin, 2007, 2009). Each is rated on a scale of 1 (Strongly Disagree) to 7 (Strongly Agree).
Out-of-class activity. Out-of-class activity was assessed via homework completion (How
often do you do and complete your homework/assignments?) and absenteeism (About how many
days were you absent from school last term?), each single-item indicators. For homework
completion, students rated themselves on a 1 (Never) to 5 (Always) rating scale and for absenteeism
students reported the number of days absent. Extracurricular activity was also assessed by asking
students to check one or more activities (following items from Martin, Papworth, et al., 2014) in the
areas of school involvement, academic activities/clubs, sports, prosocial activities, as well as self-
nominated activities. Students’ affirmative responses were summed to generate an extracurricular
School wellbeing. School-related wellbeing was assessed by way of school enjoyment (4
items; e.g., I enjoy being a student at this school; Cronbach’s α=.90; ICC=.191; Martin, 2007, 2009)
and positive academic intentions (4 items; e.g., I intend to complete school; Cronbach’s α=.82;
ICC=.146; Martin, 2007, 2009), each rated on a scale of 1 (Strongly Disagree) to 7 (Strongly
Personal wellbeing. Personal wellbeing was assessed via sense of meaning and purpose (4
items; e.g., My personal beliefs give meaning to my life; Cronbach’s α=.82; ICC=.052) from World
Health Organisation Quality of Life Instrument(WHOQOL; 1998), satisfaction with life (5 items;
e.g., In most ways my life is close to my ideal; Cronbach’s α=.80; ICC=.029) from the Satisfaction
with Life Scale (Diener et al., 1985), and self-esteem (4 items; e.g., Overall, most things I do turn
out well; Cronbach’s α=.80; ICC=.044) from the Self-Description Questionnaire II (SDQ-II; Marsh,
2007). Each was rated on a scale of 1 (Strongly Disagree) to 7 (Strongly Agree).
Socio-demographics and prior achievement. Socio-demographic data were collected on
gender (0 = female; 1 = male), age, language spoken at home (0 = English speaking; 1 = non-
English speaking), and socio-economic status (SES). SES is based on the Australian Bureau of
Statistics Index of Relative Socio-economic Advantage and Disadvantage drawn from students’
home postcode. This index is drawn from household income, educational qualifications, and
occupational skill level and aggregated at the neighborhood level. Prior achievement was based on
students’ reports of results in annual nation-wide assessment of literacy and numeracy (Cronbach’s
α=.83; ICC=.233; National Assessment Program in Literacy and Numeracy, NAPLAN)
administered by the Australian Curriculum and Assessment and Reporting Authority (ACARA).
Personality. Extraversion (Cronbach’s α=.83; ICC=.017), openness to experience
(Cronbach’s α=.75; ICC=.028), neuroticism (Cronbach’s α=.75; ICC=.005), conscientiousness
(Cronbach’s α=.86; ICC=.027), and agreeableness (Cronbach’s α=.80; ICC=.049) were assessed
using the 40-item (8 items per factor) International English Big-Five Mini-Markers instrument
(IEBM; Thompson, 2008). Items for the IEBM are each represented by one word in which
respondents rate themselves 1 (Very Inaccurate) to 7 (Very Accurate). Thompson (2008) has
previously demonstrated the reliability and predictive validity of the five factors amongst
adolescents. Due to known issues with fit for personality measures (e.g., Ginns, Martin, Liem, &
Papworth, 2014), we estimated the five latent factors through randomly assigned item parcels.
5.3 Data Analysis
Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were
conducted using Mplus 7.3 (L. K. Muthén & B. O. Muthén, 2014). CFA is used to explore factorial
and correlational distinctions between motivation and engagement. SEM is used to explore socio-
demographic and individual difference predictors of motivation and engagement, the role of motivation
and engagement in predicting academic and personal outcomes, and the relative ordering of motivation
and engagement. Maximum likelihood with robustness to non-normality (MLR; L. K. Muthén & B.
O. Muthén, 2014) was the method of estimation. We adjust for clustering of students within schools
through the ‘cluster’ command under the ‘complex’ method in Mplus. This adjusts standard errors
in order not to bias significance tests (L. K. Muthén & B. O Muthén, 2014). Less than 5% of data
were missing and these were imputed using the EM Algorithm as operationalized in LISREL 8.80
(Jöreskog & Sörbom, 2006). The root mean square error of approximation (RMSEA) the
standardized root mean square residual (SRMR), and the comparative fit index (CFI) are
emphasized as fit indices. RMSEA and SRMR values at or less than .08 and .05 are taken to reflect
close and excellent fits respectively (see Schumacker & Lomax, 2010); CFIs at or greater than .90
and .95 are taken to reflect acceptable and excellent fits respectively (McDonald & Marsh, 1990).
Keith (2006) proposes tentative benchmarks such that standardized beta coefficients (β) less than
.05 are too small to be meaningful, above .05 as small but meaningful, above .10 as moderate, and
above .25 to be large effects. These recommendations can be used to assess the magnitude and
relevance of standardized beta coefficients.
6.1 Adaptive and Maladaptive Motivation and Engagement
We first examined four possible higher order factor structures using CFA. Evidence of
motivation and engagement distinctiveness would be indicated by superior fit for multi-factor
models. Findings for fit were as follows: (a) adaptive motivation, adaptive engagement,
maladaptive motivation, maladaptive engagement factors (4-factor higher order model, in line with
the Wheel structure), χ2(885)=8357, CFI=.92, RMSEA=.039, SRMR=.060, (b) positive and
negative factors (2-factor higher order model), χ2(890)=10106, CFI=.90, RMSEA=.044,
SRMR=.074, (c) motivation and engagement factors (2-factor higher order model), χ2(890)=11659,
CFI=.88, RMSEA=.047, SRMR=.086, and (d) motivation/engagement factor (1-factor higher order
model), χ2(891)=11886, CFI=.88, RMSEA=.048, SRMR=.085. When using both fit indices as
criteria, the first two models (4-factor hypothesized model; 2-factor positive and negative model)
yielded acceptable fit (CFI ≥ .90 and RMSEA ≤ .08 and SRMR ≤ .08). Of these two models, the
hypothesized 4-factor higher order model fit best, differing significantly in chi square from the
other model (∆χ2=1749, df=5, p<.001). The factor loadings and reliabilities are presented in Table 1.
Latent correlations are presented for the 4-factor solution and shown in Table 1. As is evident, there
is substantial variance shared between motivation and engagement. Although this study’s focus is
on motivation and engagement as broad constructs (hence the focus on the higher order
framework), for completeness we conducted a first order CFA that estimated the 11 adaptive and
maladaptive motivation and engagement factors from the 44 MES items. This too yielded a good
fit, χ2(847)=6934, CFI=.93, RMSEA=.036, SRMR=.041.
6.2 Predictors of Motivation and Engagement
Structural equation modeling (χ2(1612)=12955, CFI=.90, RMSEA=.036, SRMR=.052) was
used to examine the extent to which socio-demographics (age, gender, SES, language background),
prior achievement, and individual differences (personality) predict each of adaptive motivation,
adaptive engagement, maladaptive motivation, and maladaptive engagement. Evidence of
motivation and engagement distinctiveness would be indicated by predictors statistically significant
for motivation, but not engagement (or vice versa). Findings are shown in Table 2. Findings reveal
that some background factors predict (significantly or non-significantly) rather uniformly across all
four dependent measures (e.g., prior achievement, SES, gender, extraversion, openness), while
other motivation and engagement measures are predicted in different ways by background factors.
For example, age significantly predicts adaptive motivation (β=-.10, p<.001), adaptive engagement
(β=-.09, p<.001), and maladaptive engagement (β=.15, p<.001), with older students less motivated
and engaged; but it does not significantly predict maladaptive motivation. Whereas language
background significantly predicts adaptive engagement (β=.08, p<.001; non-English speaking
background more engaged), it does not predict the other three dependent measures.
In terms of personality, agreeableness significantly predicts adaptive motivation (β=.21,
p<.001) and maladaptive engagement (β=-.21, p<.001), but it does not predict the other two forms
of motivation and engagement. Whereas conscientiousness strongly predicts adaptive engagement
(β=.50, p<.001), its significant associations with other dependent measures is less substantial
(β=.27, p<.001 for adaptive motivation; β=-.14, p<.001 for maladaptive motivation; β=-.31, p<.001
for maladaptive engagement). Neuroticism has a similarly strong predictive path to maladaptive
motivation (β=.38, p<.001), but not to the other factors (β=.03, p<.05 for adaptive motivation; β=-
.04, p<.01 for adaptive engagement; β=.09, p<.001 for maladaptive engagement). Extraversion has
no significant association with any of the four motivation and engagement factors.
6.3 Outcomes Predicted by Motivation and Engagement
SEM examined the extent to which each of adaptive motivation (χ2(2028)=16057, CFI=.91,
RMSEA=.036, SRMR=.048), adaptive engagement (χ2(2028)=16124, CFI=.91, RMSEA=.036,
SRMR=.048), maladaptive motivation (χ2(2028)=16573, CFI=.90, RMSEA=.036, SRMR=.052),
and maladaptive engagement (χ2(1768)=14543, CFI=.91, RMSEA=.036, SRMR=.049) predicted
academic and personal wellbeing outcomes (due to known collinearity, separate models were
necessary). Evidence of motivation and engagement distinctiveness would be indicated by
outcomes statistically significant for motivation, but not engagement (or vice versa). Findings are
shown in Table 3. In the main, adaptive motivation, adaptive engagement, and maladaptive
engagement are the most salient of predictors across the factor sets. However, there are some
patterns among the significant findings. For example, adaptive motivation and maladaptive
engagement are the stronger predictors of school wellbeing in the forms of school enjoyment
(β=.58, p<.001; β=-.55, p<.001 respectively) and positive academic intentions (β=.75, p<.001; β=-
.63, p<.001 respectively). Adaptive engagement is the stronger predictor of academic task activity
in the form of elaboration (β=.76, p<.001) and memorization (β=.76, p<.001) strategies. Adaptive
engagement is also the stronger predictor of out-of-class academic activity in the forms of
homework completion (β=.34, p<.001) and extra-curricular activity (β=.10, p<.001).
Relatively greater congruency between adaptive motivation and engagement tends to reside
with class activity in the forms of class participation (β=.54, p<.001; β=.54, p<.001 respectively)
and cooperation (β=.43, p<.001; β=.45, p<.001 respectively). Maladaptive engagement is also rather
uniform as a predictor of task activity, but at a lower predictive level. There is also congruency
between adaptive motivation and engagement on personal wellbeing in the forms of self-esteem
(β=.47, p<.001; β=.42, p<.001 respectively), sense of meaning (β=.41, p<.001; β=.47, p<.001
respectively), and life satisfaction (β=.45, p<.001; β=.42, p<.001 respectively). Maladaptive
engagement is also rather uniform as a predictor of personal wellbeing, but at a lower predictive
level. On balance, maladaptive motivation is less salient, but does significantly negatively predict
enjoyment of school (β=-.12, p<.001), positive academic intent (β=-.12, p<.001), and self-esteem
6.4 Motivation and Engagement as an Operational Process
Our final analyses sought to explore the often contended operational process: that motivation
is an impetus for engagement. Using SEM (including socio-demographic, prior achievement, and
personality covariates to partial out their influence), four models were tested (due to known
collinearity, separate models were necessary), as follows: (a) Time 1 (T1) adaptive motivation
predicting Time 2 (T2) adaptive and maladaptive engagement (χ2 (1004)=3750, CFI=.92,
RMSEA=.037, SRMR=.040), (b) T1 maladaptive motivation predicting T2 adaptive and
maladaptive engagement (χ2 (1004)=3961, CFI=.92, RMSEA=.038, SRMR=.047), (c) T1 adaptive
engagement predicting T2 adaptive and maladaptive motivation (χ2 (1004)=4723, CFI=.90,
RMSEA=.038, SRMR=.053), and (d) T1 maladaptive engagement predicting T2 adaptive and
maladaptive motivation (χ2 (1006)=4006, CFI=.91, RMSEA=.039, SRMR=.054). Predictive paths
are shown in Figure 2.
In terms of model fit, it appears that the motivation → engagement ordering reflects best fit,
with a significant difference in chi square (p<.001) between this ordering and the alternative
ordering (engagement → motivation). In terms of explained variance, the motivation →
engagement ordering evinces the highest aggregate r-square values (mean r-square = .33),
compared with the engagement → motivation ordering (mean r-square = .28). In terms of predictive
paths, the motivation → engagement ordering yields paths that are all statistically significant,
whereas there is no significant path between adaptive engagement and maladaptive motivation.
Taken together, there is tentative support for the claim that motivation is an impetus for
engagement. However, data also clearly show that prior engagement explains substantial variance in
subsequent motivation, ultimately suggestive of a cyclical process.
Following from previous literature and research, we sought to gain further clarity between
motivation and engagement. We did so among a sample of high school students. A number of
findings were illuminating. First, motivation and engagement were shown to be multidimensional
constructs, each comprising numerous first-order factors. Second, data supported a hierarchy in
which specific factors were subsumed under broader motivation and engagement dimensions.
Third, specific factors residing under motivation and engagement dimensions reflected salient
theorizing. Fourth, motivation and engagement could be demarcated into cognitive and behavioral
dimensions (emotional components were also implicated). Fifth, there are grounds on which to
differentiate each of motivation and engagement dimensions into adaptive and maladaptive
valences. Sixth, longitudinal data tentatively supported an operational ordering such that motivation
provides an impetus for subsequent engagement (though, prior engagement also explained
significant variance in subsequent motivation, ultimately suggestive of a cyclical process).
7.1 Findings and Implications of Note
Shedding light on each of these issues is important for theory, research, and practice. At a
conceptual level, there has been ongoing debate as to what factors are considered motivation and
what are considered engagement (Martin, 2012; Reschly & Christenson, 2012). So long as there
remains confusion along these lines, further theoretical development will be hampered. Following
from these conceptual ambiguities, measurement and research efforts will also be hampered. One’s
conceptual position on motivation and engagement will drive one’s research design. For example,
to the extent that a researcher considers motivation to be different from and deemed to precede or
energize engagement, instrumentation and data collection may be operationalized in ways that
reflect this distinction and ordering. As a result, there will be implications for educational practice.
For example, to the extent that a researcher argues for the influence of motivation on engagement in
the academic process, practitioners are likely to respond by focusing on motivation intervention as a
means to promote and sustain engagement. It is clear, then, that with respect to motivation and
engagement, theory, research, and practice are inextricably intertwined. The present findings
therefore further contribute to current understanding of academic motivation and engagement in
each of these three areas.
The positive and negative valence associated with different motivation and engagement
factors was noteworthy. It has been suggested that greater attention be given to maladaptive
constructs in the motivation and engagement literature (Martin, 2012; Martin, Anderson, Bobis,
Way, & Vellar, 2012). Accordingly, motivation and achievement require attention to both their
positive and negative dimensions. The present psychometric (factor analyses) and substantive
(predictors and outcomes) findings support this contention. Although adaptive and maladaptive
dimensions are strongly correlated, they also account for unique variance in the academic process.
Therefore, consistent with Martin et al. (2012), it is not a case of simply ensuring that students
‘switch on’, but also ensure that they are not ‘switched off’.
The high correlation among motivation and engagement factors is in keeping with the well-
known alignment between numerous constructs in this area. The overlap among constructs is widely
recognized (e.g., Fredricks et al., 2004; Martin, 2012) and begs the question as to how much
empirical overlap is reasonable before we deem motivation and engagement constructs
insufficiently distinct to consider (and model) as separate factors. Our correlations (Table 1) showed
that shared variance among motivation and engagement constructs ranged from 7% to 67%, with a
median shared variance of less than 50%; thus, leaving more than half the variance between
motivation and engagement constructs unexplained. Although there is no widely agreed benchmark,
we suggest that the present levels of unexplained variance reflect distinctiveness among motivation
and engagement constructs. Indeed, even in the most extreme case, there remains 33% variance
unexplained (between adaptive motivation and adaptive engagement). We further suggest that these
levels of shared variance are generally in keeping with the conceptual congruencies among
motivation and engagement constructs. As noted in our introduction, whilst identifying important
distinctions between motivation and engagement constructs, researchers also appropriately
recognize their conceptual and empirical overlap. Our correlations are in line with the conceptual
nuancing in this field.
It is also the case that these high correlations and conceptual alignments give rise to statistical
challenges – often in the form of collinearity. Indeed, this collinearity led us to estimate separate
SEMs when seeking to analyze motivation and engagement as predictors (Figure 2 and Table 3) in
order to avoid suppression effects and the like. Interestingly, whereas collinearity is often seen as
signaling problems in research design and data analysis (including in SEM), with regards to
motivation and engagement it is somewhat logical and meaningful. To the extent that definitional
parameters are overlapping, so will measurement and operational parameters, leading to high
correlations among many constructs. Collinearity, then, is in one sense empirical confirmation of
conceptual and applied realities in this particular field. We therefore posit collinearity as a
methodological reality inherent in the measurement and operationalization of motivation and
The finding regarding the operational ordering of motivation to engagement was illuminating
(albeit tentative). Major theorists appear to be in broad (though, typically circumspect) agreement
that motivation underpins engagement (e.g., Anderman & Patrick, 2012; Kuhl, 1985; Reeve, 2012;
Schunk & Mullen, 2012). Alongside these conceptual perspectives, there is empirical support for
this proposed ordering of motivation and engagement and that also resonates with our findings. This
includes experimental work by Reeve et al. (2004) and recent correlational work by Froiland and
Davison (2016) and Froiland and Worrell (2016). Motivation is thus positioned as an inner drive or
impetus that enables the student to optimally engage. This holds implications for intervention to the
extent that efforts to effect change in behavioral outcomes (such as engagement) would do well to
attend to aligned motivational factors. Notwithstanding this, findings also showed that prior
engagement explains variance in later motivation. Thus, although prior motivation seems to be a
point for intervention, both motivation and engagement are mutually reinforcing across time and
ultimately a cyclical process (Martin, 2012).
A more novel aspect of the study involved the predictive role of personality. Whereas socio-
demographic and prior achievement effects supported previous research (older students, non-
English speaking background, and higher prior achievement more motivated and engaged; e.g., see
Hattie, 2009; Martin, 2007, 2009; Martin, Nejad, et al., 2013), the personality effects were a more
substantial extension on prior work. Their effects also provided further insight into alignments and
differences between motivation and engagement. In terms of alignments, conscientiousness was
predictive of all motivation and engagement factors – interestingly, though, it more substantially
predicted adaptive engagement. This may be consistent with Hoyle (2010) who linked
conscientiousness to the ways individuals manage behavior, suggesting a connection also to their
engagement (the more behavioral dimension to the Wheel). It is also consistent with researchers
linking conscientiousness to self-regulatory functions (e.g., de Raad & Schouwenberg, 1996;
McCrae and Löckenhoff, 2010), again suggestive of a link to factors akin to engagement. It appears
that agreeableness was the next most salient personality factor, but mainly in terms of adaptive
motivation and maladaptive engagement. These latter findings are interesting in that research tends
to yield a mixed profile with respect to agreeableness. For example, Komarraju and Karau (2005)
generally found no significant association between agreeableness and motivation when controlling
for other personality factors, whereas Clark and Schroth (2010) found that agreeableness was the
strongest significant (negative) predictor of amotivation and a significant (positive) predictor of
intrinsic motivation. In addition, whereas maladaptive motivation was explained least by the study’s
predictors, it was the only factor that neuroticism substantially predicted, perhaps not surprising
given maladaptive motivation comprises anxiety and failure avoidance (fear of failure) that are
well-established correlates of neuroticism (McCrae & Costa, 1996). From an intervention
perspective, identification of students low in conscientiousness (for adaptive and maladaptive
motivation and engagement) and agreeableness (for adaptive motivation and maladaptive
engagement), and students high in neuroticism (for maladaptive motivation) may be informative for
early efforts aimed at assisting students at possible motivation and engagement risk.
Although socio-demographic and prior achievement covariates were included to control for
variance attributable to them, it is worth also noting their roles in predicting students’ motivation
and engagement. Interestingly, although females have tended to be more motivated and engaged in
prior research that includes other socio-demographics and prior achievement (see Liem & Martin,
2012 for a review), it seems that when also controlling for personality, the role of gender is not so
salient. In terms of age, motivation and engagement declines as a function of age were consistent
with prior work (Martin, 2007, 2009). In terms of language, students from a non-English speaking
language background tended to be higher in motivation. Given the schools were of relatively higher
SES may signal that the ethnic groups represented in them tended to be educationally aspirational
and advantaged (Martin, Way, Bobis, & Anderson, 2015), playing out in terms of higher academic
motivation and engagement. Notwithstanding this, the role of SES was (predominantly) not
significant which may be explained by the relatively narrow range of SES reflected in the sample
(leaving language background the source of greater variance). Finally, with regards to prior
achievement, findings conform to the well-established association with motivation and engagement
(e.g., Hattie, 2009). Taken together, these results not only better illuminate the unique roles of
motivation and engagement in predicting outcomes (having purged variance attributable to
covariates), they also shed light on the groups of students (e.g., males, lower achievers) for whom
motivation and engagement are an educational issue to address.
In terms of outcomes, it was useful to demarcate motivation and engagement effects into task,
class, out-of-class, school, and personal outcomes. Again, the dominant pattern is congruence in
motivation and engagement effects, but a more differentiated inspection suggested some
distinctions. For example, in the main, adaptive engagement explains most variance in task and
class activity, whereas it seems adaptive motivation and maladaptive engagement explain more
variance in school-related wellbeing. Also, maladaptive engagement (relative to maladaptive
motivation) is more strongly associated with factors that are more “engagement-like”, such as task-
specific and class activity. Both motivation and engagement explain substantial variance in personal
wellbeing. It seems, then, that concrete, specific, and ‘local’ activity (e.g., doing a specific task or
engaged in actual classwork) is better explained by engagement (with notable contributions from
motivation) and underlying school orientations and dispositions are explained by motivation (with
notable contributions from engagement). In part, this seems consistent with conceptualizing
relevant to the two constructs in that motivation tends to reflect an inner psychological factor
whereas engagement is more activity oriented (e.g., Ainley, 2012; Reeve, 2012). To the extent that
this is the case, the present findings offer further validity to motivation and engagement theorizing
in that the factors with which they are differentially correlated are similarly substantively
differentiated. These findings also have applied implications. For example, to the extent that
students’ school well-being is distinct from their academic behavior and activity, intervention and
educational practice would emphasize motivational support (in the case of school wellbeing) or
engagement support (in the case of academic behavior and activity).
7.2 Limitations and Future Directions
When interpreting findings, there are a number of limitations important to consider and
which provide direction for further research. First, the data here are self-reported. On the one hand,
this is a logical methodology given the study’s substantive focus. On the other hand, it increases
shared method variance and so it is important for future research to collect data from additional
sources. These might include attendance patterns from school records, level of course enrolment
also from school records, teacher ratings of homework completion and class activity, parent ratings
of time on homework or study, and achievement data from tests. Second, we employed a variable-
centered approach. Research might now look to employ person-centered approaches to identify
motivation and engagement profiles and the potentially differential nature of their academic and
personal wellbeing effects. Third, there would be further yield in multi-level approaches. We did
not have sufficient school numbers to disentangle student- and school-level motivation and
engagement “climate” and so future research would do well to purposefully sample with this aim.
Fourth, our analyses (and conclusions) were limited to what could be gleaned from two time points.
To test a fuller psychological process it is important to collect outcome data at a third time point in
order to explore the following models: (a) Time 1 motivation Time 2 engagement Time 3
outcomes vs. (b) Time 1 engagement Time 2 motivation Time 3 outcomes.
Fifth, although our sample was large, it was confined to independent (non-government, non-
systemic) schools of higher than average SES. This may have restricted the variance on some
factors, perhaps reducing effects where otherwise they may have been present. Thus, for example,
although SES negatively predicted maladaptive engagement, it did not predict other motivation and
engagement factors. A wider sampling frame in future research may illuminate this further. It is also
worth noting that our measure of SES was an index aggregating household income, educational
qualifications, and occupational skill level by neighborhood. Thus, we did not assess individual
students’ SES; rather, it was their SES context at the neighborhood level. Future research might
thus also include student-level SES. With regards to another covariate - gender - although it is the
case that findings in our study favored girls, this is to be interpreted in the context of our domain-
general data. Different patterns of gender effects can emerge in more domain-specific investigations
(e.g., with regards to motivation and engagement in literacy vs. numeracy). Yet another covariate -
language background - may warrant further research. We found positive associations between non-
English speaking background and motivation and speculated this may be because NESB parents are
educationally aspirational (at least in this study). However, this is an empirical question and
including measures of parent and student aspirations in future research would answer it.
Sixth, the longitudinal data were a matched subset of the fuller sample and although
invariance tests demonstrated broad equivalence between matched and unmatched samples, we can
only infer this when part of the sample is missing for longitudinal data analysis. Finally, the present
study was focused on individually-based covariates and predictors. We did not focus on contextual
and “external agent” factors that are known to impact student motivation and engagement, including
help from teachers (Ginns, Martin, & Papworth, 2014), teachers’ instructional techniques and
practices (Martin, 2016; Reeve et al., 2004), support from parents (Martin, Marsh, McInerney,
Green, & Dowson, 2007; Martin, Marsh, McInerney, & Green, 2009), relationships with peers
(Liem & Martin, 2011), and motivation and engagement classroom goal structures such as mastery
and performance climates in the classroom (Meece, Anderman, & Anderman, 2006). In similar
vein, sociocultural approaches consider the internalization of social phenomena (Nolen & Ward,
2008) and view motivation and engagement factors as both contextually and individually
determined. That is, depending on the individual student, a given environment can induce varying
motivational and engagement responses (Walker, 2010; Walker, Pressick-Kilborn, Arnold, &
Sainsbury, 2004). To better understand motivation and engagement, these contextual factors require
This study has summarized recent debates, deliberations and discussions about motivation and
engagement. It also closely examined a recently proposed multidimensional motivation and
engagement framework and instrumentation, along with substantial data elucidating the major
theoretical and applied themes emanating from prior theory and research. Taking theory,
measurement, and research into account, our study of high school students suggests there are major
alignments between motivation and engagement and there are also areas where the two constructs
are distinct. Based on present findings, it is also evident that these alignments and distinctions
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Table 1. CFA factor loadings for best-fitting motivation and engagement models
Plan & monitor
Failure (perf) avoid
ICC=Intraclass correlation (as a function of school)
Table 2. Socio-demographics, prior achievement, personality predicting motivation and engagement
* p < .05, ** p < .01, *** p < .001
NESB=non-English speaking background; SES=socio-economic status
Table 3. Motivation and engagement predicting task, class, out-of-class, school, and personal wellbeing outcomes
Motivation (β) →
Engagement (β) →
Motivation (β) →
Engagement (β) →
All predictive parameters controlled for age, gender, language background, SES, prior achievement, personality
* p < .05, ** p < .01, *** p < .001
Elab=Elaboration; Mem=Memorization; Coop=Cooperative Learning; Partic=Class Participation; Hwork=Homework Completion; XCurric=Extra-curricular Activity; Intent=Future
Intentions; Esteem=General Self-esteem; Meaning=Meaning and Purpose; Life Sat=Life Satisfaction
Motivation and Engagement Wheel (reproduced with permission from
Time 1 Adaptive
Time 2 Maladaptive
Time 2 Adaptive
Model Fit: χ2 (1004)=3750, CFI=.92, RMSEA=.037, SRMR=.040
Time 1 Maladaptive
Time 2 Maladaptive
Time 2 Adaptive
Model Fit: χ2 (1004)=3961, CFI=.92, RMSEA=.038, SRMR=.047
Time 1 Adaptive
Time 2 Maladaptive
Time 2 Adaptive
Model Fit: χ2 (1004)=4723, CFI=.90, RMSEA=.038, SRMR=.053
Time 1 Maladaptive
Time 2 Maladaptive
Time 2 Adaptive
Model Fit: χ2 (1006)=4006, CFI=.91, RMSEA=.039, SRMR=.054
Figure 2. Models exploring ordering of Time 1 motivation and engagement relative to Time
2 motivation and engagement.
Note. All analyses control for socio-demographics, prior achievement, and personality