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Adaptability, engagement and academic achievement at university


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University entry is a time of great change for students. The extent to which students are able to effectively navigate such change likely has an impact on their success in university. In the current study, we examined this by way of adaptability, the extent to which students’ adaptability is associated with their behavioural engagement at university, and the extent to which both are associated with subsequent academic achievement. A conceptual model reflecting this pattern of predicted relations was developed and tested using structural equation modelling. First-year undergraduate students (N = 186) were surveyed for their adaptability and behavioural engagement at the beginning of their first year. Following this, students’ academic achievement was obtained from university records at the end of Semester 1 and 2 of first-year university. Findings showed that adaptability was associated with greater positive behavioural engagement (persistence, planning, and task management) and lower negative behavioural engagement (disengagement and self-handicapping). Moreover, negative behavioural engagement was found to inversely predict academic achievement in Semester 1, which predicted academic achievement in Semester 2. The educational implications of the findings are discussed.
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Adaptability and University Students 1
Collie, R.J., Holliman, A.J., & Martin, A.J. (in press, accepted August 2016). Adaptability,
engagement, and academic achievement at university. Educational Psychology. 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:
Adaptability and University Students 2
Adaptability, Engagement, and Academic Achievement at University
Rebecca J. Collie
School of Education, University of New South Wales, Australia
Andrew J. Holliman
School of Psychological, Social and Behavioural Sciences, Coventry University, UK
Andrew J. Martin
School of Education, University of New South Wales, Australia
June 2016
Requests for further information about this investigation can be made to Dr Andrew J.
Holliman, School of Psychological, Social and Behavioural Sciences, Coventry University,
Coventry, CV1 5FB, United Kingdom. E-Mail: Tel: +44 (0) 24
7765 8208. Fax: +44 (0) 24 7688 8300.
Adaptability and University Students 3
Adaptability, Engagement, and Academic Achievement at University
University entry is a time of great change for students. The extent to which students are able
to effectively navigate such change likely has an impact on their success in university. In the
current study, we examined this by way of adaptability, the extent to which students’
adaptability is associated with their behavioral engagement at university, and the extent to
which both are associated with subsequent academic achievement. A conceptual model
reflecting this pattern of predicted relations was developed and tested using structural
equation modelling. First-year undergraduate students (N = 186) were surveyed for their
adaptability and behavioral engagement at the beginning of their first year. Following this,
students’ academic achievement was obtained from university records at the end of Semester
1 and 2 of first year university. Findings showed that adaptability was associated with greater
positive behavioral engagement (persistence, planning, and task management) and lower
negative behavioral engagement (disengagement and self-handicapping). Moreover, negative
behavioral engagement was found to inversely predict academic achievement in Semester 1,
which predicted academic achievement in Semester 2. The educational implications of the
findings are discussed.
Keywords: adaptability; engagement; achievement; university/college students
Adaptability and University Students 4
The start of university marks a period of immense change for students. It involves
navigating a very different learning environment, a significant increase in personal autonomy
and responsibility, a change in social networks, and often occurs concomitantly with other
major life events (e.g., moving out of home, supporting oneself through part-time work; Gall,
Evans, & Bellerose, 2000; Pittman & Richmond, 2008). The extent to which students are able
to manage the transition to university plays a key role in their academic success in the first
year of university and beyond (Pittman & Richmond, 2008; Stoeber, Childs, Hayward, &
Feast, 2011). The successful navigation of such changes has been examined under a variety
of approaches (for a review, see Richardson, Abraham, & Bond, 2012). The current study
focuses on one additional approach that may help to shed further light on this important issue:
students’ adaptability. Adaptability refers to the extent to which individuals are able to
modify their thoughts, actions, and emotions to appropriately respond to and manage the
changing, new, and uncertain demands of university (Martin, Nejad, Colmar, & Liem, 2012).
Given the new experiences that first-year university students typically encounter, we suggest
that adaptability is particularly pertinent and may play a role in assisting positive academic
outcomes among first-year university students.
Although recent research is documenting the positive impact of adaptability (e.g., on
academic engagement and achievement outcomes), this work has focused on secondary
school students (Author et al., 2016; Martin, Nejad, Colmar, & Liem, 2012, 2013; Martin,
Nejad, Colmar, Liem, & Collie, 2015). The extent to which adaptability has a similarly
positive impact among university students remains an empirical question. We suggest that a
focus on university students is important because it will help to advance knowledge of
university entry and first-year studies, which can be tumultuous times for students (e.g., Gall
et al., 2000; Johnson, Taasoobshirazi, Kestler, & Cordova, 2015). The likelihood of failure is
Adaptability and University Students 5
heightened when individuals undertake new tasks, experience major transitions, and are faced
with uncertainty (Heckhausen & Schulz, 1995)—experiences common to new university
students. Moreover, given that university differs from secondary school in significant ways
(degree of autonomy, level of teacher support, physical size, etc.), and given the financial and
intellectual costs associated with degree non-completion (Grebennikov & Shah, 2012), a
focus on adaptability among university students is timely and important.
In the current study, we sought to examine adaptability among first-year university
students by testing a model of adaptability, engagement, and achievement. This model is
shown in Figure 1. More precisely, we examined the impact of adaptability on university
students’ positive and negative behavioral engagement (where behavioral engagement refers
to actions undertaken by students that promote active academic and social involvement in
university; Fredricks, Paris, & Blumenfeld, 2004). We focused on behavioral engagement as
this reflects the outward manifestations of student motivation (Skinner, Kindermann, &
Furrer, 2009) that have been shown to be highly important for university success (e.g., Credé
& Kuncel, 2008; Johnson et al., 2015). We also examined the impact of adaptability and the
(positive and negative) engagement factors on subsequent academic achievement (Semester 1
and Semester 2 achievement in first-year university). Findings have the potential to advance
knowledge of how first-year students manage the new demands they face and may provide
information relevant to intervention efforts aiming to promote positive academic outcomes
among university students.
As noted above, adaptability refers to cognitive, behavioral, and emotions adjustments
that individuals make to manage changing, novel, and uncertain situations and events
(Martin, Nejad et al., 2012, 2013). This is known as the tripartite model of adaptability and
Adaptability and University Students 6
was recently developed by Martin, Nejad et al. (2012, 2013, 2015). The present study follows
from their work to investigate their adaptability framework in the higher education context.
Cognitive adjustment refers to modifying one’s thoughts, behavioral adjustment refers to
modifying one’s actions, and emotional adjustment refers to modifying one’s affective
responses, all to manage changing, new, or uncertain events (Martin, Nejad et al., 2012,
2013). This focus on novelty and change means that adaptability is different from cognate
constructs that are concerned with individuals’ responses to adversity such as coping
(Lazarus & Folkman, 1984), resilience (Howard & Johnson, 2000), and buoyancy (Martin &
Marsh, 2009; Putwain, Connors, Symes, & Douglas-Osborn, 2012).
Of note, adaptability has been linked with important outcomes in research. For
example, adaptability has been associated with higher levels of positive behavioral
engagement (e.g., greater class participation) and lower levels of negative behavioral
engagement at secondary school (e.g., lower disengagement; Martin, Nejad et al., 2013,
2015). Moreover, it has been associated with greater academic well-being (e.g., greater
academic buoyancy, lower anxiety) and non-academic wellbeing among adolescents (e.g.,
greater life satisfaction, higher sense of meaning and purpose; Martin, Nejad et al., 2012,
2013, 2015). More recently, positive links between adaptability and secondary school
students’ academic achievement have emerged (Author et al., 2016; Martin, Nejad et al.,
2012). Together, therefore, there is a growing body of work highlighting the significance of
adaptability for students’ engagement and achievement outcomes. However, research among
university students remains limited. Moreover, the extent to which adaptability uniquely
influences engagement and achievement when examined simultaneously (and appropriate
controls for shared variance are applied) has not been examined. In sum, therefore, examining
adaptability among university students is important for developing knowledge of the
Adaptability and University Students 7
adaptability construct, its unique associations with positive outcomes, and how it functions at
different educational stages.
Conceptual Underpinnings for the Current Study
Conceptualizing from several theoretical approaches is harnessed alongside the
tripartite model of adaptability in order to describe the conceptual basis for the current
study’s investigation. In turn below, we describe these conceptual underpinnings including
the lifespan theory of control (Heckhausen, Wrosch, & Schulz, 2010), theorizing in the area
of self-regulated learning (e.g., Winne & Hadwin, 2008), and Buss and Cantor’s (1989)
model of individual functioning (for a more complete review of this theorizing with respect to
adaptability see Martin, Nejad et al., 2012, 2013). Together, the three frameworks
demonstrate the foundations of the adaptability construct and provide an idea of how it
functions in relation to students’ outcomes.
Lifespan theory of control. The lifespan theory of control proposes that individual
development across the lifespan is impacted by individuals’ capacity to actively and
effectively adapt to the opportunities and constraints they face in their social environment
(Heckhausen et al., 2010). A central aspect in determining positive development is the extent
to which the individual experiences control. Of particular pertinence to adaptability is the
idea of compensatory control, which refers to adjusting one’s thoughts or actions to
effectively deal with environmental demands or situations (Tomasik, Silbereisen, &
Heckhausen, 2010). Whereas the lifespan theory concerns cognitive and behavioral
adjustments, however, adaptability also encompasses emotional adjustments. Despite this
difference, the lifespan theory of control provides important understanding of the underlying
mechanisms of adaptability via the concept of compensatory control.
Self-regulated learning. Self-regulated learning involves monitoring, directing, and
controlling one’s thoughts and actions to meet learning goals, build expertise, and improve
Adaptability and University Students 8
skills (Zimmerman, 2002). With respect to adaptability, models of self-regulation tend to
include a final phase where individuals self-evaluate their performance and identify thoughts
and actions needed to improve in future (e.g., the adaptation phase; Winne & Hadwin, 2008).
The tripartite model of adaptability is relevant to this phase of self-regulation (see Martin,
Nejad et al., 2013). At the same time, adaptability extends prior conceptualizing on self-
regulation by also considering the affective domain—identifying emotions that need to be
adjusted in order to improve performance (Martin, Nejad et al., 2013). Moreover, whereas
self-regulation is more broadly concerned with academic tasks and demands (that may
involve success or adversity), adaptability is relevant to situations involving change, novelty,
and uncertainty. Thus, adaptability has been positioned as a specific type of self-regulation
(Martin, Nejad et al., 2013)—and this conceptual grounding provides helpful insight into how
adaptability functions in students’ lives.
Individual functioning. Buss and Cantor (1989) propose a model where individuals’
personal characteristics influence the strategies they apply to negotiate demands in their
environment and, in turn, their outcomes in that context. In the current study, we focus on the
role of strategies and their impact on outcomes. Consistent with Martin, Nejad and colleagues
(2013), adaptability is proposed to act as a strategy given that it enables individuals to better
negotiate (changing, new, or uncertain) demands in their environment. Indeed, on this
conceptual basis, Martin, Nejad et al. (2013) proposed and tested a conceptual model where
adaptability was positioned as a predictor of academic and non-academic outcomes including
behavioral engagement, self-esteem, life satisfaction, and sense of meaning and purpose.
With respect to engagement, Martin, Nejad and colleagues (2013) argue that adaptability
plays a role in assisting engagement because of its enabling capacity. More precisely,
adaptable students are more likely to be engaged in their learning because being able to adjust
to effectively manage change, novelty, and uncertainty helps students to keep up with lesson
Adaptability and University Students 9
pacing and content (e.g., changes in topic, quick progression in lessons, new task demands)
and means students are less likely to experience low self-efficacy and poor performance
(Martin, Nejad et al., 2013). These assertions have been supported empirically.
With a longitudinal design conducted among 969 secondary school students, Martin,
Nejad and colleagues (2013) showed that adaptability was associated with greater positive
behavioral engagement (operationalized as class participation), greater self-esteem, life
satisfaction, and sense of meaning and purpose. Thus, with conceptual support from Buss and
Cantor’s (1994) model, it appears that adaptability plays an important role in assisting
positive outcomes and that this may occur because adaptability is a strategy that enables
individuals to better manage environmental demands. In the current study, we extended prior
understanding by examining adaptability alongside both engagement and achievement.
Academic Engagement
Academic engagement refers to the extent and manner in which students are actively
involved in their schooling (Skinner et al., 2009) and is generally considered a multifaceted
construct comprising cognitive, behavioral, and emotional engagement (Fredricks et al.,
2004). In the current study, we focused on behavioral engagement, which refers to the actions
that students undertake to be actively involved both academically and socially in their
education (Fredricks et al., 2004). We made the decision to focus on behavioral engagement
because this is a salient aspect of many definitions of engagement. For example, engagement
is the “outward manifestation of a motivated student” (Skinner et al., 2009, p.494), and
reflects “behaviors aligned with the energy and drive” (Liem & Martin, 2012, p.3). In
addition, behavioral engagement has been shown to be highly pertinent to success in
university (e.g., Credé & Kuncel, 2008; Johnson et al., 2015).
In the current examination, we consider positive and negative behavioral engagement.
We operationalize positive behavioral engagement by way of three behaviors that stem from
Adaptability and University Students 10
self-regulatory theories of motivation (Zimmerman, 2002) and that are associated with
positive academic habits and outcomes (e.g., Fredricks et al., 2004; Martin, 2007a):
persistence, planning (and monitoring), and task management. Persistence refers to sustained
efforts to solve questions or deal with challenges (rather than giving up; Miller, Greene,
Montalvo, Ravindran, & Nichols, 1996). Planning (and monitoring) refers to active efforts to
plan schoolwork and keep track of progress on schoolwork (Miller et al., 1996). Task
management refers to how self-management of study time is utilized, how a study timetable
is organized, and the locations chosen for completing schoolwork (Martin, 2007a).
In contrast, negative behavioral engagement refers to behaviors that are associated
with poor academic habits and outcomes (e.g., Fredricks et al., 2004; Martin, 2007a). We
employ two constructs that stem from need achievement and self-worth motivation theories
(Covington, 1992) in our operationalization of negative behavioral engagement: self-
handicapping and disengagement. Self-handicapping occurs when students undertake actions
to reduce their chances of academic success in order to have an excuse in case of poor
outcomes (e.g., putting off studying for an exam), and disengagement occurs when students
no longer care about their education or feel like giving up in their efforts (Martin, 2007a).
Various factors and processes have been identified in the literature as predictors of
engagement such as interpersonal relationships, the classroom structure and climate, and
teacher support (e.g., Collie, Martin, Papworth, & Ginns, 2016; Jang, Reeve, & Deci, 2010;
Virtanen, Lerkkanen, Poikkeus, & Kuorelahti, 2015; Wang & Eccles, 2012). As noted above,
we examine the role of adaptability. Using Buss and Cantor’s (1989) model, adaptability is
identified as a strategy for managing environmental demands and is examined as a predictor
of engagement. Among secondary school students, evidence is emerging for the relevance of
adaptability with respect to positive and negative behavioral engagement (Martin, Nejad et
al., 2012, 2013, 2015). For example, adaptability has been shown to be important for positive
Adaptability and University Students 11
behavioral engagement as assessed via classroom participation (Martin, Nejad et al., 2013).
In other research, adaptability has been directly linked with lower negative behavioral
engagement (as assessed via self-handicapping and disengagement; Martin, Nejad et al.,
It is feasible to consider that students’ persistence, planning, and task management are
due in part to their adaptability. With respect to persistence, students’ capacity to engage in
sustained efforts to solve new challenges requires adjusting the way they think about the
problem, modifying the strategies they use, and perhaps even minimizing frustration.
Regarding planning (and monitoring), students’ ability to actively plan and keep track of
schoolwork depends on their capacity to effectively modify their thoughts, behaviors, and/or
emotions—such as changing priorities or actions to deal with new deadlines. Turning to task
management, more adaptable students are likely to better adjust their responses in order to
manage learning tasks, their study time, and the best locations for getting the work done.
Conceptual links are also apparent for negative behavioral engagement. Students who are low
in adaptability are more likely to experience low self-efficacy and poor performance, which
may lead them to react to new circumstances defensively (self-handicapping) or give up
trying (disengagement; Martin, Nejad et al., 2013). In combination, therefore, there is
empirical and conceptual support for examining adaptability as a predictor of positive and
negative behavioral engagement.
Academic Achievement
Academic achievement is a central aim of schooling and is associated with important
long-term outcomes (Rothman & McMillan, 2003). Thus, researchers have focused
substantial attention towards understanding how best to promote students’ achievement. In
the current study, we focus on two predictors: adaptability and behavioral engagement.
Evidence of a link between adaptability and achievement is emerging (e.g., Author et al.,
Adaptability and University Students 12
2016; Martin, Nejad et al., 2012) and likely occurs because adaptable students are better at
self-regulating their responses, which is central for academic performance (Johnson et al.,
2015; Zimmerman, 2002). Turning to positive behavioral engagement, researchers have
linked persistence, planning, and task management with greater academic achievement (e.g.,
Caprara et al., 2008; Collie, Martin, & Curwood, 2016; Liem & Martin, 2012; Miller et al.,
1996). With respect to negative behavioral engagement, although the link between self-
handicapping and academic achievement has been disputed, a recent meta-analysis of 36 field
studies among (elementary, middle, and secondary) school and university students found a
mean effect size between self-handicapping and academic achievement of r = -.23
(Schwinger, Wirthwein, Lemmer, & Steinmayr, 2014). Moreover, increasing disengagement
(e.g., Appleton, Christenson, & Furlong, 2008) has been found to coincide with lower
academic performance.
In sum, there is support for the role of adaptability and both positive and negative
behavioral engagement as predictors of academic achievement. In addition to examining this
in the current study, we also investigated the extent to which behavioral engagement plays a
linking role between adaptability and achievement. Although this relationship has yet to be
examined, prior research suggests an indirect association is plausible (e.g., Caprara et al.,
2008; Martin, Nejad et al., 2015). Of note, achievement was examined at two time points:
Semester 1 and Semester 2. By examining the two time points, this enabled us to ascertain the
role of adaptability and engagement as assessed at the beginning of Semester 1 on subsequent
achievement at the end of Semesters 1 and 2 to understand the influence of adaptability and
engagement over an academic year. Having two measures of achievement also enabled us to
estimate the indirect effect of adaptability and engagement on distal achievement (Semester
2) via proximal achievement (Semester 1) as examined through bootstrapping analyses.
Other Relevant Constructs
Adaptability and University Students 13
To best understand the unique associations among the substantive constructs in the
study, it is important to control for personal characteristics that may influence the substantive
constructs and their associations. In the current study, we controlled for three covariates: age,
gender, and prior achievement. These covariates have all been linked with adaptability,
engagement, and achievement in prior research. Male students (though not necessarily
“adult” male students) tend to report lower levels of positive behavioral engagement and
higher negative behavioral engagement than females (e.g., Martin, Nejad et al., 2015; Wang
& Eccles, 2012; Yu & Martin, 2014). Among adolescents, older students tend to report lower
adaptability and positive behavioral engagement than younger students (e.g., Martin, Nejad et
al., 2012), and higher negative behavioral engagement (e.g., Collie et al., 2016). Prior
achievement is positively associated with both adaptability and future achievement (e.g.,
Author et al., 2016; Martin, Nejad et al., 2013). Thus, given the associations established
between the covariates in prior research, we also controlled for them in the current study.
Moreover, given that most prior research has involved secondary school students, the current
study provides an opportunity to examine the role of these covariates at the university level.
Study Overview
University entry is a time of great change for students and the extent to which
individuals are able to adjust to effectively navigate this change likely plays a role in their
academic outcomes. In the current study, we sought to examine adaptability among university
students to see whether it plays a positive role in predicting students’ engagement and
achievement. We also examined the extent to which significant indirect effects were evident
in linking adaptability with Semester 1 and 2 achievement via behavioral engagement, and in
linking behavioral engagement to Semester 2 achievement via Semester 1 achievement. Four
research questions guided the study:
Adaptability and University Students 14
1. To what extent does adaptability predict (positive and negative) behavioral
engagement among university students? (RQ1)
2. To what extent do adaptability and behavioral engagement predict achievement in
Semester 1 and 2? (RQ2)
3. To what extent does achievement in Semester 1 predict achievement in Semester
2? (RQ3)
4. To what extent is (a) adaptability significantly associated with Semester 1 and 2
achievement via behavioral engagement, and (b) behavioral engagement
associated with Semester 2 achievement via Semester 1 achievement? (RQ4)
Given that prior research has demonstrated that adaptability is associated with
behavioral engagement and achievement among secondary school students, we expected
similar associations in the current sample. Thus, we expected that adaptability would be
associated with greater behavioral engagement and lower negative behavioral engagement
(RQ1). We anticipated that adaptability and positive behavioral engagement would be
associated with greater Semester 1 and 2 achievement, whereas the opposite would be true
for negative behavioral engagement (RQ2). We assumed there would be a positive
association between Semester 1 and 2 achievement (RQ3). Finally, regarding indirect effects,
we assumed that behavioral engagement may play a significant linking role between
adaptability and achievement, given supported links between engagement and these
constructs in prior research (e.g., Author et al., 2016; Martin, Nejad et al., 2013; RQ4a). For
similar reasons, we expected that Semester 1 achievement may play a linking role between
behavioral engagement and Semester 2 achievement (RQ4b).
Participants and Procedure
Adaptability and University Students 15
All participating students in this study (N = 186) were recruited from a single higher
education institution (university) in the West Midlands, UK. Students were University
Freshmen – undergraduates in their first year of study – enrolled in either a single honors
psychology degree (BSc Psychology, n = 149) or a combined honors degree, which includes
psychology (BSc Psychology and Criminology, n = 26; BSc Sport Psychology, n = 11).
There were 185 full-time students and one part-time student. Three-quarters (75%) of the
sample were female (n = 139), students were aged between 18 and 35 years (M = 19.16, SD =
2.32), and six students (3%) disclosed some form of disability (Dyslexia [n = 3]; Dyspraxia;
long standing illness; other physical disability). The vast majority of participating students
were from the UK (76%; n = 142) with others coming from wider Europe (16%), Asia (5%),
and also Africa and the Americas (3%). The ethnic background of most students was ‘White
British’ (33%; n = 62), followed by ‘Black or Black BritishAfrican’ (15%; n = 27), ‘Other
White Background’ (11%; n = 20), ‘Asian or Asia British–India’ (9%; n = 17), ‘Asian or
Asia BritishPakistani(7%; n = 13), ‘Black or Black BritishCaribbean’ (7%; n = 12) or
other (18%). The selection criteria were not limited to any particular demographic or ability
group; all eligible students were invited to participate in this research. Participants completed
a paper questionnaire shortly after the program induction. At the end of Semester 1 and 2,
students’ academic achievement scores were extracted from the University Records System
along with background characteristics.
Students completed a questionnaire with items on adaptability and engagement.
Background characteristics and achievement data were obtained from students’ university
records. Descriptive and psychometric statistics for all core measures in this study are
presented in Table 1 and reflected upon in the Results section.
Adaptability and University Students 16
Adaptability. Students’ adaptability was measured using the Adaptability Scale
(Martin, Nejad et al., 2013). The scale comprises nine items assessing cognitive (e.g., “I am
able to think through a number of possible options to assist me in a new situation”),
behavioral (e.g., “I am able to seek out new information, helpful people, or useful resources
to effectively deal with new situations), and emotional (e.g., “I am able to reduce negative
emotions [e.g., fear] to help me deal with uncertain situations”) adaptability. Using a Likert
scale response format, respondents rated themselves on a scale of 1 (strongly disagree) to 7
(strongly agree) for each item. Previous measurement work (e.g., Martin, Nejad et al., 2012,
2013) has shown that the scale functions well when the three types of adaptability are
combined into an overarching adaptability factor given that they are highly interrelated. In
addition, prior research has provided evidence of validity for the one-factor scale via
confirmatory factor analysis, positive correlations with related constructs (e.g., self-
regulation), invariance in measurement properties across key participant subgroups (e.g.,
gender, age), and adequate levels of reliability (e.g., Author et al., 2016; Martin, Nejad et al.,
2012, 2013, 2015). In the current study, the Cronbach’s alpha was .89.
Behavioral Engagement. Students’ behavioral engagement was measured using the
Motivation and Engagement Scale – University/College (MES-UC) (Martin, 2007b). Five
factors assessed with 4 items each were used to assess engagement. Positive behavioral
engagement was assessed using three factors: persistence (e.g., “If I can’t understand my
university work at first, I keep going over it until I do”), planning (e.g., “Before I start an
assignment, I plan out how I am going to do it”), task management (e.g., “When I study, I
usually study in places where I can concentrate”). Negative behavioral engagement was
assessed with two factors (4 items each): self-handicapping (e.g., “I sometimes don’t study
very hard before exams so I have an excuse if I don’t do as well as I hoped”) and
disengagement (e.g., “I’ve pretty much given up being involved in things at university”).
Adaptability and University Students 17
Respondents rates themselves on a scale of 1 (strongly disagree) to 7 (strongly agree). Ample
prior work has provided support for combining these different factors into two overarching
positive and negative behavioral engagement constructs (e.g., Martin, 2007a; Yu & Martin,
2014). Moreover, the psychometric properties of this scale have been demonstrated among
secondary and university students showing evidence of sound factor structure through
confirmatory factor analysis, invariance across different participant subgroups, adequate
reliability, and appropriate correlations with other academic outcomes (e.g., positive
behavioral engagement associated with greater achievement; negative behavioral engagement
associated with lower academic valuing; e.g., Martin, 2007a, 2009).
Academic Achievement (Grade Point Average). Data on students’ academic
achievement (coursework, examinations, and overall module marks) at the end of semesters 1
and 2 were collected via a University Records System, which contains detailed records of
student profiles (personal and performance). All marks are carefully checked and verified;
thus, this was a reliable way to access students’ academic scores. At the participating
institution, University Freshman who enroll in September (i.e., those participating in this
study) complete three courses in Semester 1 (October – January) and three courses in
Semester 2 (February – May). Although participating students shared two courses in
Semester 1 and 2, a single course was ‘degree-specific’ and thus only taken by students in
that particular degree program. For each, an overall mark (/100) can be accessed from the
University Records System where a score of below 40 is considered a ‘fail’, 42-48 a ‘third
class’ or ‘D-grade’, 52-58 a ‘second class lower’ or ‘C-grade’, 62-68 a ‘second class higher’
or ‘B-grade’, and 72 or above a ‘first class’ or ‘A-grade’. Students’ academic achievement
was measured using their Grade Point Average – the most widely used method to assess
educational performance for courses taken in Semester 1 and Semester 2. These are referred
to as GPA Semester 1 and GPA Semester 2 respectively hereafter.
Adaptability and University Students 18
Demographics and Prior Achievement. The University Records System was also
consulted to gather demographic data (e.g., gender, age) and students’ academic achievement
prior to enrolment. To assess students’ previous academic scores (prior to enrolment), the
University Records System provides a Universities and Colleges Admissions Service
(UCAS) tariff point score for each student, which represents a ‘converted equivalent’ of their
A-Level, Business and Technician Educational Council (BTEC), or other qualification
scores. This score thus reflects students’ prior academic achievement and is used during the
admissions process to make acceptance decisions on prospective students. The measure was
chosen principally because it is reliable and provides an equivalent reflection of students’
academic achievement prior to enrolment.
Data Analysis
Means, standard deviations, and reliabilities were all calculated using SPSS Version
22. Confirmatory factor analysis (CFA) was used to assess the factor structure of
instrumentation and to ascertain correlations among the latent factors. Structural equation
modeling (SEM) was used to test the hypothesized process model. Both CFA and SEM
were performed with Mplus 7.30 (Muthén & Muthén, 2014) using Maximum likelihood
(ML) for estimation. Full information maximum likelihood was used to deal with missing
data (see Muthén & Muthén, 2014 for details). Missing values were 1.4% of the data. In
CFAs and SEMs, we posit an a priori structure and test the ability of a solution based on
this structure to fit the data by demonstrating that (a) the solution is well defined, (b)
parameter estimates are consistent with theory and a priori predictions, and (c) the
subjective indices of fit are reasonable (Marsh, Balla & McDonald, 1988; McDonald &
Marsh, 1990). Due to sample size, we aimed to reduce the number of parameters relative
to respondents while also harnessing the statistical advantages of modeling latent factors.
We therefore estimated latent engagement factors via the scale scores of planning, task
Adaptability and University Students 19
management, and persistence for positive engagement, and via the scale scores of self-
handicapping and disengagement for negative engagement. This use of overarching
positive and negative engagement factors also avoided multicollinearity, which was
identified as an issue with the separate engagement factors in preliminary analyses. We
also parceled the nine adaptability items into three scale scores as indicators for the latent
adaptability factor (Little, Cunningham, Shahar, & Widaman, 2002). In the hypothesized
process model, we explored a ‘fully forward’ process model in which adaptability predicts
engagement, adaptability and engagement predict GPA Semester 1, and engagement,
adaptability, and GPA Semester 1 predict GPA Semester 2. The covariates of age, gender,
and prior achievement were predictors of all substantive factors in the model; hence
substantive parameters are purged of any shared variance with covariates. Figure 1 shows
the hypothesized model.
When assessing model fit, several fit indexes were consulted, namely, the root mean
square error of approximation (RMSEA), the comparative fit index (CFI), the Gamma Hat
index, and the standardized root mean square residual (SRMR). Models were considered to
adequately fit the data at values of ≤.08 for the RMSEA, values of ≥.90 for the CFI and
Gamma Hat index, and values of ≤.08 for the SRMR. The chi-square test and normed chi-
square test are also reported. The CFI contains no penalty for a lack of parsimony so that
improved fit due to the introduction of additional parameters may reflect capitalization on
chance, whereas the NNFI and RMSEA contain penalties for a lack of parsimony.
Whereas tests of statistical significance and indices of fit aid in the evaluation of the fit,
there is ultimately a degree of subjectivity and judgment in the selection of a ‘best’ model.
A parametric bootstrapping approach (with 1000 draws) was used to test for indirect
effects (Shrout & Bolger, 2002). Significant indirect effects would support the
Adaptability and University Students 20
hypothesized process of adaptability to GPA Semesters 1 and 2 via engagement and the
process of engagement to GPA Semester 2 via GPA Semester 1.
Descriptive Statistics, Reliability, Factor Structure, and Correlations
Table 1 presents scale means, standard deviations, reliabilities, and mean factor
loadings from the CFA. Internal reliabilities for all substantive factors were acceptable, as
follows: α = .89 for adaptability, α = .92 for positive engagement, and α = .85 for negative
engagement. Mean factor loadings were also acceptable (ranging from .73 to .75). The fit for
the CFA was good, χ2 = 69.03, df = 42, p < .01, χ2/df = 1.64, RMSEA = .059 CFI = .96,
Gamma Hat = .98, and SRMR = .045.
Correlations between all latent factors taken from the CFA are presented in Table 2. It
can be seen that adaptability is significantly correlated with positive engagement (r = .77, p <
.001) and negative engagement (r = -.62, p < .001). It can also be seen that negative
engagement but not positive engagement is significantly correlated with GPA Semester 1 (r =
-.30, p = .001) and GPA Semester 2 (r = -.18, p = .05). Significant correlations are also found
between all measures of achievement: prior achievement with GPA Semester 1 (r = .22, p <
.05) and GPA Semester 2 (r = .20, p < .05); and, GPA Semester 1 with GPA Semester 2 (r =
.57, p < .001).
Exploring the Process Model
Given the sound measurement support, structural equation modeling was then used to
explore the hypothesized model (Figure 1). This model provided a good fit to the data (χ2 =
76.03, df = 43, p < .01, RMSEA = .064, CFI = .95, Gamma Hat = .97, and SRMR = .049).
Results are presented in Table 3 and Figure 2. They reveal that adaptability is a predictor of
Adaptability and University Students 21
both positive engagement (β = .79, p < .001) and negative engagement (β = -.66, p < .001). In
turn, negative engagement predicts GPA Semester 1 (β = -.43, p < .001), which subsequently
predicts GPA Semester 2 (β = .53, p < .001). Direct effects between adaptability and GPA
Semesters 1 and 2 are not significant, nor is the direct effect of positive or negative
engagement to GPA Semester 2.
Indirect Effects
We next tested the significance of indirect effects using bootstrapped standard errors
(with 1000 draws) (Shrout & Bolger, 2002). Findings are presented in Table 4. Three
significant indirect effects emerged: adaptability to GPA Semester 1 via negative engagement
(adaptability → negative engagement → GPA Semester 1; β = .28, p < .05), adaptability to
GPA Semester 2 via negative engagement and GPA Semester 1 (adaptability → negative
engagement → GPA Semester 1 → GPA Semester 2; β = .15, p < .05), and negative
engagement to GPA Semester 2 via GPA Semester 1 (negative engagement → GPA
Semester 1 → GPA Semester 2; β = -.23, p < .01). Together, these effects provide evidence
of the significant indirect influence of adaptability and negative engagement on later
This study examined the relations between adaptability, behavioral engagement, and
academic achievement in undergraduate students. Findings revealed that adaptability was
associated with greater positive behavioral engagement and lower negative behavioral
engagement. In addition, negative behavioral engagement predicted lower GPA Semester 1.
As expected, GPA Semester 1 positively predicted GPA Semester 2. Also important were the
Adaptability and University Students 22
indirect findings showing that adaptability was indirectly associated with GPA in Semester 1
and 2, and that negative behavioral engagement was indirectly associated with GPA Semester
2. Key findings are discussed below.
Role of Adaptability
Students’ adaptability was significantly associated with greater positive and lower
negative behavioral engagement. This supports the conceptual positioning of adaptability as a
strategy that can help individuals to better negotiate (changing, new, or uncertain) demands in
their environment (Martin, Nejad et al., 2013). As noted above, this finding likely occurred
because adaptable students are better able to engage in sustained efforts to solve new
challenges (persistence), actively plan and keep track of schoolwork (planning and
monitoring), and adjust their responses in order to manage learning tasks (task management).
In addition, adaptable students are less likely to react to new circumstances defensively (self-
handicapping) or with disaffection (disengagement) given that they are able to adjust as
needed to effectively manage new or changing demands (Martin, Nejad et al., 2013). This
finding is significant because it extends studies involving secondary school students (e.g.,
Martin, Nejad et al., 2012, 2013, 2015) and suggests that a similar link between adaptability
and behavioral engagement occurs among university students. Thus, it is relevant for
developing conceptual knowledge of the adaptability construct.
In a finding that was contrary to our expectations, adaptability did not directly predict
GPA (however, it was indirectly associated; discussed below). This finding contrasts
emerging research on the link between adaptability and achievement among secondary school
students (e.g., Author et al., 2016; Martin, Nejad et al., 2012) and suggests that, for university
students, adaptability may influence achievement via other processes. Indeed, adaptability
was indirectly associated with greater GPA via negative behavioral engagementmore
adaptable students tended to report lower negative behavioral engagement and, in turn, lower
Adaptability and University Students 23
negative engagement was associated with greater GPA. Together, these findings provide
preliminary information about the role of adaptability in predicting academic achievement
among university students and suggest differences in how it functions compared with
secondary school students.
Behavioral Engagement and GPA
As expected, negative behavioral engagement was found to predict lower GPA
Semester 1 (e.g., Appleton et al., 2008; Schwinger et al., 2014). In a somewhat surprising
finding, however, positive behavioral engagement did not predict greater GPA Semester 1.
This finding is inconsistent with other evidence in this area (e.g., Chow & Yong, 2013), but
may have occurred because prior research does not often involve both positive and negative
behavioral engagement in the one model. In the current study, we accounted for shared
variance between the engagement factors in the SEM. When this control was in place,
findings indicated that the remaining (unique) variance was attributable only to negative
behavioral engagement.
With respect to why this occurred, perhaps for university students, effective processes
such as persistence, planning (and monitoring), and task management are more the norm—
thereby truncating possible variance among university students. Indeed, these processes are
arguably essential for successfully completing the final year of secondary school and gaining
entry to university. Thus, at the university-level, perhaps it is in terms of the negative
behavioral engagement processes (self-handicapping and disengagement) that more
differentiation occurs. Another possible reason for why this finding occurred concerns the
difference in instructional approaches and expectations that occur between secondary school
and university such as the increase in autonomy for students and the reduction in instructor or
parental support for completing schoolwork. These changes might exacerbate the impact of
negative behavioral engagement if students feel they are lacking support. Taken together, this
Adaptability and University Students 24
finding is significant given that it highlights the unique role played by self-handicapping and
disengagement in university students’ GPA and highlights the importance of efforts to
address these negative processes. Indeed, this finding closely aligns with other research into
maladaptive engagement patterns among university students. For example, Martin, Marsh
and Debus (2003) found university students higher in maladaptive strategies such as self-
handicapping (e.g., not studying for tests, procrastination, reduction of effort) received
significantly lower grades.
Links Across Achievement Outcomes
As expected, academic achievement in Semester 1 positively predicted academic
achievement in Semester 2. This suggests that early achievement seems to lay the foundations
for later achievement (see Martin, Wilson, Liem, & Ginns, 2013) and is consistent with prior
theoretical perspectives on university course progression. Academic momentum, for example,
is one such perspective that seeks to explain academic outcomes later in one’s university
course (e.g., degree completion; Attewell, Heil, & Reisel, 2012; Martin, Wilson et al., 2013).
The theory emphasizes that initial academic course progress strongly influences subsequent
progress, that early momentum effects occur beyond effects of socio-demographic
background and secondary school attainment, and that there are some value-adding activities
or orientations that enhance momentum through one’s university course. The present findings
suggest that value-adding activities or orientations such as adaptability and engagement may
play a part in academic momentum. At the same time, it is important to recognize that
although prior achievement was positively associated with both Semester 1 and 2
achievement in the bivariate correlations, it was only related to Semester 1 achievement in
the SEM. Thus, after controlling for shared variance, background characteristics, and the
inclusion of the adaptability and behavioral engagement factors, prior achievement did not
explain unique variance in Semester 2 achievement. This provides further support for the
Adaptability and University Students 25
value-adding nature of adaptability and engagement for academic momentum, in line with the
theory (Attewall et al., 2012).
Implications for Practice
Taken together, these findings have educational applications. There is scope for
higher education institutions to consider students’ negative behavioral engagement patterns
and to intervene (where necessary) in order to reduce the likelihood of maladaptive processes
which, in turn, might enhance students’ academic achievement. Researchers (e.g., Schwinger
et al., 2014) have argued that educational interventions should focus on preventing negative
behavioral engagement as a way to enhance students’ academic achievement. Educators
might consider creating learning environments (and associated activities) that demonstrate the
important association between effort and achievement. Activities that promote students’
understanding of the relation between their actions and outcomes are useful to illustrate how
consequences are contingent upon what students do (see Martin, 2013). This will promote
control and therefore ought to reduce levels of self-handicapping and disengagement (Martin,
2007a). Moreover, if educators can emphasize that mistakes are a useful way to identify areas
of improvement and further development, this may reduce the threat to self-worth that
mistakes can bring, students may become more ‘success-oriented’ (Covington, 1992), and
they may have a more positive outlook on mistakes/failures as ‘challenges’ (Martin, 2010).
Thus, if fear can be reduced, then this will likely reduce self-handicapping and
disengagement. Indeed, intervention research supports the feasibility of efforts aimed to
reduce maladaptive engagement. For example, in studies where students participated in a
self-complete workbook program (Martin, 2008) and a group workshop program (Martin,
2005) focusing on motivation and engagement, reductions in self-handicapping were found.
Moreover, small learning communities where teachers show an active interest in students and
Adaptability and University Students 26
provide extra help to close learning gaps have been shown to lead to reductions in
disengagement (Balfanz, Herzog, & Mac Iver, 2007).
Given that adaptability was associated in beneficial ways with behavioral engagement
and that it indirectly predicted greater GPA, there may be merit in promoting this among
students. An adaptability intervention might take a similar form to those designed to address
resiliencea cognate construct that is more concerned with adjustment to ‘setbacks’ rather
than novelty and uncertainty (Martin, Nejad et al., 2013). Martin, Nejad, Colmar, Liem, and
Collie (2015), drawing upon Morales’ (2000) cycles of resilience, put forward an adaptability
intervention that involves showing students how to: 1) recognize uncertainty and novelty that
might demand an appropriate regulatory response; 2) appropriately adjust their cognition,
behavior, and affect (emotion)—the most critical part of the process (Martin & Burns, 2014);
and 3) recognize the importance of these regulatory responses and to improve/adjust them if
necessary. This adjustment might enable the student to respond more constructively to
circumstances and conditions of uncertainty and novelty leading to a heightened sense of
control and a reduction of failure dynamics (e.g., disengagement and self-handicapping)
which, in turn, might enhance their academic achievement. Indeed, this process was
supported in Martin, Nejad et al.’s (2015) research.
Limitations and Future Directions
In the present study, there are several limitations that require discussion. First, the
sample size was relatively modest. Although this did not prevent significant effects from
being revealed, examinations with larger sample sizes and from multiple universities is
important. Second, the sample was relatively homogeneous in terms of gender. Although not
surprising given the typical make-up of psychology courses, this does limit the
generalizability of our findings given that males and females can exhibit different
engagement profiles (e.g., Yu & Martin, 2014). Third, we examined domain-general
Adaptability and University Students 27
engagement. Future research examining how the current study’s findings play out in different
domains of study is important given research highlighting that engagement can vary across
different subjects (e.g., Green, Martin, & Marsh, 2007). Fourth, the current study did not
examine the role of motivation alongside engagement. This is important in future research
given that motivation is considered to underpin engagement and will enable alignment with
well-established motivational frameworks (e.g., self-regulatory theories, Zimmerman, 2002;
need achievement and self-worth theories, Covington, 1992). Fifth, although our data were
collected at multiple time points and this added strength to the study’s findings, we cannot
make casual inferences. In the present study, ordering decisions were based on the research
literature and our conceptual model. The results supported this ordering; however, we cannot
rule out the potential of reciprocal effects. Intervention research, which is also needed in this
area, would better inform us about the pattern of cause-effect relations between adaptability,
behavioral engagement, and academic achievement.
The aim of the current study was to examine the extent to which adaptability and
behavioral engagement are associated with academic achievement among university students.
Findings supported prior research among secondary school students by showing strong links
between adaptability and behavioral engagement. Several findings differed from the
secondary school research. Adaptability and positive behavioral engagement were not
directly associated with GPA, whereas negative behavioral engagement was. Of note,
however, adaptability was indirectly associated with greater GPA (via lower negative
behavioral engagement). Together, these findings provide important preliminary
understanding about the role of adaptability among university students and extend knowledge
of behavioral engagement in several ways. Thus, they have relevance to efforts endeavoring
Adaptability and University Students 28
to understand how students can successfully navigate the often tumultuous first year of
university and the promotion of effective adjustment at this educational stage.
Adaptability and University Students 29
Author et al. (2016) [details removed for peer review]
Appleton, J.J., Christenson, S.L. & Furlong, M.J. (2008) Student engagement with school:
Critical conceptual and methodological issues of the construct. Psychology in the
Schools, 45, 369-386.
Attewell, P., Heil, S., & Reisel, L. (2012). What is academic momentum? And does it matter?
Educational Evaluation and Policy Analysis, 34, 27–44.
Balfanz, R., Herzog, L., & Mac Iver, D.J. (2007). Preventing student disengagement and
keeping students on the graduation path in urban middle-grades schools: Early
identification and effective interventions. Educational Psychologist, 42, 223-235.
Buss, D.M., & Cantor, N. (Eds.). (1989). Introduction. Personality psychology: Recent trends
and emerging directions.
Caprara, G.V., Fida, R., Vecchione, M., Del Bove, G., Vecchio, G.M., Barbaranelli, C., &
Bandura, A. (2008). Longitudinal analysis of the role of perceived self-efficacy for
self-regulated learning in academic continuance and achievement. Journal of
Educational Psychology, 100, 525-534.
Chow, S.J., & Yong, B.C.S. (2013) Secondary school students’ motivation and
achievement in combined science. US-China Education Review B, 3. 213-228.
Collie, R.J., Martin, A.J., & Curwood, J.S. (2016). Multidimensional motivation and
engagement for writing: Construct validation with a sample of boys. Educational
Psychology, 36, 771-791.
Adaptability and University Students 30
Collie, R.J., Martin, A.J., Papworth, B., & Ginns, P. (2016). Students' interpersonal
relationships, personal best (PB) goals, and academic engagement. Learning and
Individual Differences, 45, 65-76.
Covington, M.V. (1992). Making the grade: A self-worth perspective on motivation and
school reform. Cambridge, YK: Cambridge University Press.
Credé, M., & Kuncel, N.R. (2008). Study habits, skills, and attitudes: The third pillar
supporting collegiate academic performance. Perspectives on Psychological Science,
3, 425-453.
Fredricks, J.A., Blumenfeld, P.C., & Paris, A.H. (2004). School engagement: Potential of the
concept, state of the evidence. Review of Educational Research, 74, 59–109.
Gall, T.L., Evans, D.R., & Bellerose, S. (2000). Transition to first-year university: Patterns of
change in adjustment across life domains and time. Journal of Social and Clinical
Psychology, 19, 544-567.
Grebennikov, L., & Shah, M. (2012). Investigating attrition trends in order to improve
student retention. Quality Assurance in Education, 20(3), 223-236.
Green, J., Martin, A.J., & Marsh, H.W. (2007). Motivation and engagement in English,
mathematics and science high school subjects: Towards an understanding of
multidimensional domain specificity. Learning and Individual Differences, 17, 269-
Heckhausen, J., & Schulz, R. (1995). A life-span theory of control. Psychological
Review, 102, 284-304.
Adaptability and University Students 31
Heckhausen, J., Wrosch, C., & Schulz, R. (2010). A motivational theory of life-span
development. Psychological Review, 117, 32-60.
Howard, S., & Johnson, B. (2000). Resilient and non-resilient behavior in adolescents. In A.
Graycar (Ed.), Trends and issues in crime and criminal justice series (pp. 1–6).
Canberra, Australian Capital Territory, Australia: Australian Institute of Criminology.
Jang, H., Reeve, J., & Deci, E.L. (2010). Engaging students in learning activities: It is not
autonomy support or structure but autonomy support and structure. Journal of
Educational Psychology, 102, 588-600.
Johnson, M.L., Taasoobshirazi, G., Kestler, J.L., & Cordova, J.R. (2015). Models and
messengers of resilience: A theoretical model of college students’ resilience,
regulatory strategy use, and academic achievement. Educational Psychology, 35, 869-
Lazarus, R.S., & Folkman, S. (1984). Stress, appraisal, and coping. New York, NY:
Liem, G.A.D., & Martin, A.J. (2012). The motivation and engagement scale: Theoretical
framework, psychometric properties, and applied yields. Australian Psychologist, 47,
Little, T.D., Cunningham, W.A., Shahar, G., & Widaman, K.F. (2002). To parcel or not to
parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9,
Marsh, H.W., Balla, J.R., & McDonald, R.P. (1988). Goodness-of-fit indices in confirmatory factor
analysis: The effect of sample size. Psychological Bulletin, 102, 391-410.
Martin, A.J. (2005). Exploring the effects of a youth enrichment program on academic
motivation and engagement. Social Psychology of Education, 8, 179-206.
Adaptability and University Students 32
Martin, A.J. (2007a) Examining a multidimensional model of student motivation and
engagement using a construct validation approach. British Journal of
Educational Psychology, 77, 413-440.
Martin, A.J. (2007b) The Motivation and Engagement Scale. Sydney, Australia: Lifelong
Achievement Group (
Martin, A.J. (2008). Enhancing student motivation and engagement: The effects of a
multidimensional intervention. Contemporary Educational Psychology, 33, 239-269.
Martin, A.J. (2009) Motivation and engagement across the academic life span: A
developmental construct validity study of elementary school, high school, and
university/college students. Educational and Psychological Measurement, 69, 794-
Martin, A.J. (2010) Should students have a gap year? Motivation and performancefactors
relevant to time out after completing school. Journal of Educational
Psychology, 102, 561-576.
Martin, A.J. (2013) Motivation to learn. In A.J. Holliman (ed.) The Routledge international
companion to educational psychology. Abingdon, Oxon: Routledge.
Martin, A.J., & Burns, E.C. (2014). Academic buoyancy, resilience, and adaptability in
students with ADHD. The ADHD Report, 22, 1-9.
Martin, A.J., & Marsh, H.W. (2009). Academic resilience and academic buoyancy:
Multidimensional and hierarchical conceptual framing of causes, correlates and
cognate constructs. Oxford Review of Education, 35, 353-370.
Adaptability and University Students 33
Martin, A.J., Marsh, H.W., & Debus, R.L. (2003). Self-handicapping and defensive
pessimism: A model of self-protection from a longitudinal perspective. Contemporary
Educational Psychology, 28, 1-36.
Martin, A.J., Nejad, H., Colmar, S. & Liem, G.A.D. (2012) Adaptability: Conceptual and
empirical perspectives on responses to change, novelty and uncertainty. Australian
Journal of Guidance and Counselling, 22, 58-81.
Martin, A.J., Nejad, H.G., Colmar, S. & Liem, G.A.D. (2013) Adaptability: How students’
responses to uncertainty and novelty predict their academic and non-academic
outcomes. Journal of Educational Psychology, 105, 728-746.
Martin, A.J., Nejad, H.G., Colmar, S., Liem, G.A.D. & Collie, R.J. (2015). The role of
adaptability in promoting control and reducing failure dynamics: A mediation model.
Learning and Individual Differences, 38, 36-43.
Martin, A.J., Wilson, R., Liem, G.A. & Ginns, P. (2013) Academic momentum at
university/college: Exploring the roles of prior learning, life experience, and
ongoing performance in academic achievement across time. Journal of Higher
Education, 84, 640-674.
McDonald, R.P., & Marsh, H.W. (1990). Choosing a multivariate model: Noncentrality and
goodness-of-fit. Psychological Bulletin, 107, 247-255.
Miller, R.B., Greene, B.A., Montalvo, G.P., Ravindran, B., & Nichols, J.D. (1996).
Engagement in academic work: The role of learning goals, future consequences,
pleasing others, and perceived ability. Contemporary Educational Psychology, 21,
Adaptability and University Students 34
Morales, E.E. (2000). A contextual understanding of the process of educational resilience:
High achieving Dominican American students and the "resilience cycle”. Innovative
Higher Education, 25, 7-22.
Muthén, L.K., & Muthén, B.O. (2014). Mplus user's guide. Los Angeles: Muthén & Muthén.
Pittman, L.D., & Richmond, A. (2008). University belonging, friendship quality, and
psychological adjustment during the transition to college. The Journal of
Experimental Education, 76, 343-362.
Putwain, D.W., Connors, L., Symes, W., & Douglas-Osborn, E. (2012). Is academic
buoyancy anything more than adaptive coping? Anxiety, Stress, & Coping, 25, 349-
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university
students’ academic performance: A systematic review and meta-analysis.
Psychological Bulletin, 138, 353–387.
Rothman, S., & McMillan, J. (2003). Influences on achievement in literacy and numeracy.
LSAY Research Reports, 36.
Schwinger, M., Wirthwein, L., Lemmer, G. & Steinmayr, R. (2014) Academic self-
handicapping and achievement: A meta-analysis. Journal of Educational Psychology,
106, 744-761.
Shrout, P.E., & Bolger, N. (2002). Mediation in experimental and non-experimental
studies: New procedures and recommendations. Psychological Methods, 7, 422-445.
Skinner, E.A., Kindermann, T.A., & Furrer, C.J. (2009). A motivational perspective on
engagement and disaffection: Conceptualization and assessment of children's
behavioral and emotional participation in academic activities in the
Adaptability and University Students 35
classroom. Educational and Psychological Measurement, 69, 493-525.
Stoeber, J., Childs, J.H., Hayward, J.A., & Feast, A.R. (2011). Passion and motivation for
studying: Predicting academic engagement and burnout in university students.
Educational Psychology, 31, 513-528.
Tomasik, M., Silbereisen, R., & Heckhausen, J. (2010). Is it adaptive to disengage from
demands of social change? adjustment to developmental barriers in opportunity-
deprived regions. Motivation and Emotion, 34, 384-398.
Virtanen, T.E., Lerkkanen, M., Poikkeus, A., & Kuorelahti, M. (2015). The relationship
between classroom quality and students’ engagement in secondary school.
Educational Psychology, 35, 963-983.
Wang, M., & Eccles, J.S. (2012). Social support matters: Longitudinal effects of social
support on three dimensions of school engagement from middle to high school. Child
Development, 83, 877-895.
Winne, P.H., & Hadwin, A.F. (2008). The weave of motivation and self-regulated learning.
In D.H. Schunk & B.J. Zimmerman (Eds.), Motivation and self-regulated learning:
Theory, research, and application (pp. 297–314). New York, NY: Routledge.
Yu, K., & Martin, A.J. (2014). Personal best (PB) and ‘classic’ achievement goals in the
chinese context: Their role in predicting academic motivation, engagement and
buoyancy. Educational Psychology, 34, 635-658.
Adaptability and University Students 36
Zimmerman, B.J. (2002). Achieving self-regulation: The trial and triumph of adolescence. In
F. Pajares & T. Urdan (Eds.), Academic motivation of adolescents (pp. 1–27).
Greenwich, CT: Information Age Publishing.
Adaptability and University Students 37
Table 1
Summary Statistics for Modeled Variables
Std. Dev.
Mean CFA loading
Gender (M)
Prior Achievement
Positive Engagement
Negative Engagement
GPA Semester 1
GPA Semester 2
Note: Age was measured in years; adaptability, positive engagement and negative engagement were scored from
1-7 with higher scores corresponding to higher levels of each construct; GPA Semesters 1 and 2 were scored
from 0-100 with higher scores corresponding to higher levels of academic achievement. Covariates (age, gender,
prior achievement) and GPAs are single-item indicators and so reliability is not computed and the factor
loadings are set at 1.00.
Adaptability and University Students 38
Table 2
Bivariate Latent Correlations Between Modeled Variables
1. Age
2. Gender (M)
3. Prior Achievement
4. Adaptability
5. Positive Engagement
6. Negative Engagement
7. GPA Semester 1
8. GPA Semester 2
*p < .05; **p < .01; ***p < .001
Adaptability and University Students 39
Table 3
Standardized Beta Coefficients in SEM
Endogenous Variables
Semester 1
Semester 2
Gender (M)
Prior Achievement
Positive Engagement
Negative Engagement
GPA Semester 1
*p < .05; **p < .01; ***p < .001
Adaptability and University Students 40
Table 4
Indirect Effects from Bootstrapping in SEM
Indirect Pathway
Adaptability → Pos. Engagement → GPA Sem. 1
Adaptability → Neg. Engagement → GPA Sem. 1
Adaptability → Pos. Engagement → GPA Sem. 1 → GPA Sem. 2
Adaptability → Neg. Engagement → GPA Sem. 1 → GPA Sem. 2
Pos. Engagement → GPA Sem. 1 → GPA Sem. 2
Neg. Engagement → GPA Sem. 1 → GPA Sem. 2
*p < .05; **p < .01; ***p < .001
Adaptability and University Students 41
GPA Sem. 1
GPA Sem. 2
- Age
- Gender
- Prior Achievement
Figure 1. Hypothesized model. Solid paths represent hypothesized links and dashed paths represent additional links tested for completeness. Sem.
= Semester.
Adaptability and University Students 42
GPA Sem. 2
GPA Sem. 1
Figure 2. Significant substantive parameters from SEM. Results involving covariates and remaining non-significant effects are shown in Table 3.
All indirect effects are shown in Table 4. Sem. = Semester.
***p < .001.
... Educational research has indicated that engagement is a strong predictor of academic achievement (Fredricks et al., 2004). For instance, Collie et al. (2017) employed a structural equation modeling (SEM) based on a sample of 186 undergraduate students, and found that negative behavioral engagement was negatively correlated with general academic achievement. Wang and Holcombe (2010) collected data from 1,046 urban students and conducted a SEM, finding that behavioral engagement positively influence general academic achievement. ...
... According to the broaden-and-build theory and previous empirical studies (e.g., Fredrickson, 1998Fredrickson, , 2013Dewaele and MacIntyre, 2014;Dewaele and Li, 2021), positive emotions can broaden people's mind scope and build cognitive as well as social resources, which in turn boost engagement. At the same time, many educational studies have found the positive influence of engagement on learning achievement (Park, 2005;Ponitz et al., 2009;Wang and Holcombe, 2010;Collie et al., 2017). The prerequisites for mediation are that an independent variable predicts a mediator variable and that the mediator variable simultaneously predicts a dependent variable (Pekrun et al., 2009). ...
... SRA was found to be positively correlated with FLE and behavioral engagement, and negatively with FLCA. The findings are in accordance with previous studies (e.g., Wang and Holcombe, 2010;Reschly and Christenson, 2012;Dewaele and MacIntyre, 2014;Collie et al., 2017;Li et al., 2019;Wang et al., 2019). ...
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Since the coming of Positive Psychology in the field of second language acquisition (SLA), the significance of emotion, especially positive emotion, has been well recognized by researchers. Educational research has indicated that both emotion and engagement play fundamental roles in learning process and psychological wellbeing, but research on their relationship is scant in SLA. The present study contributed to the development of Positive Psychology in SLA by investigating the relationships between achievement emotions, behavioral engagement, and self-reported English achievements. 633 students from four senior high schools in China participated in the study. Data collected through questionnaires revealed: (1) Participants reported a medium to high level of foreign language enjoyment (FLE), foreign language classroom anxiety (FLCA), and behavioral engagement; females had a higher level of FLE and behavioral engagement than males; the participants reported more anxiety and less enjoyment than western samples; (2) significant correlations were found between FLE, FLCA, behavioral engagement, and self-reported achievement; (3) significant mediating effects of behavioral engagement were found between both achievement emotions and self-reported achievement, and the mediating effect of engagement was stronger between FLCA and self-reported achievement. The findings extended the nomological network of achievement emotions, developed our insights into the complex relationships between emotions, learner engagement and language achievement, and revealed the mediating effects of behavioral engagement. Finally, directions for future study and implications for foreign language educators were provided.
... Within the educational psychology literature, similar behavioral indicators could be used to assess observable behavioral changes. One example proposed by Collie and her colleagues [63] would be to use class attendance/participation to assess behavioral changes in class engagement within the school context. ...
... Research across various psychological disciplines all suggested that emotional coping is an important adaptability resource that helps individuals to cope with psychological setbacks, distress, and uncertainty when they are in transition [63]. Martin and his colleagues [6] showed that within the educational context, adaptability is linked negatively to distress, poor mental health outcomes, and fear of uncertain situations. ...
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The concept of career adaptability has been widely applied across cultural and educational settings in the hope of finding working solutions to facilitate school-to-work transition. In Hong Kong, China, there are signs showing that career adaptability scores are increasingly being used as the dominant benchmark to measure the effectiveness of career interventions designed for student populations. However, this concept is developed primarily based on western values. For it to fulfill its theoretical promises, the concept needs to be reinterpreted in the local context. This means that issues concerning the measurement and interpretation of career adaptability need to be clarified due to cultural differences. This paper attempts to address this issue by reviewing published studies on adaptability across other disciplines of psychology. Results showed that most published studies in Hong Kong on career adaptability did not use data and methodological triangulation research methodologies. Overall, this review shows that a reductionist approach has been applied to the study of career adaptability in Hong Kong. The current understanding of career adaptability remains largely at the psychological level only. Cognitive and behavioral changes are seldom studied or reported despite their importance. Implications on how future research could be enhanced are discussed.
... It is related to both initial success in the job search (Sulastri et al., 2015) and later success, such as higher salary (Roth and Clarke, 1998) and better work performance (Dye and Reck, 1989), and is utilized as a selection criterion for evaluating a graduate's initial job suitability (Akkermans et al., 2018). Students who invest time and effort in their academic tasks and show higher study engagement also express stronger career competencies (Avram et al., 2019;Collie et al., 2017), and those with a clearer vocational identity also perform better because they are more committed to their selected studies (Aleni Sestito et al., 2015;Wong and Kaur, 2018). Thus, we hypothesized that: ...
Purpose Using latent profile analysis, the authors explored the career profiles of young adult tertiary students ( N = 468, 73.9% women; mean age 20 years) to determine the relative importance of traditional career orientation (TCO) and protean career orientation (PCO) beliefs for them. Design/methodology/approach Young adults studying at university can aspire to traditional career experiences as they believe organizations will support their professional and career development. However, since the development of newer career models, the TCO model has received little research attention compared to the PCO. Findings The authors found that the dominant profile exhibited average levels of TCO, PCO and career competencies, and that this mixed profile was associated with more mature career identity development and greater organizational commitment. A second profile, with low TCO, average PCO and career competencies, showed a similar level of career maturity to the mixed profile, but exhibited less organizational commitment. A third profile, with average TCO, low PCO and career competencies, especially vocational identity awareness, was related to less career development and organizational commitment. Originality/value The findings suggest that a mixed traditional-protean orientation is common in young adult tertiary students and that the development of a vocational identity is important for positive career outcomes, regardless of orientation.
... A number of scholars have investigated the roles of learner engagement toward someone's learning (Collie et al., 2017;Fuller et al., 2011;Öz & Boyacı, 2021). Although many researchers have explored learner engagement in educational contexts, it remains lacking results in language learning (Heng, 2014;Korobova & Starobin, 2015). ...
... Keywords: adaptability, uncertainty, pandemic, remote education, health professions education INTRODUCTION Adaptability refers to the cognitive, behavioral and emotional adjustments that individuals make to manage new, changing and uncertain circumstances, conditions and situations (1,2). As Martin et al. (2) describes, cognitive adjustment refers to amending individual's thoughts, behavioral adjustment refers to amending actions, and emotional adjustment refers to amending affective responses, all to manage changing, new or uncertain events. ...
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During the COVID-19 pandemic, universities across the world transitioned rapidly to remote education. Engaging with a curriculum that has been transitioned from in-person to remote education mode is likely to impact how students and educators adapt to the changes and uncertainties caused by the pandemic. There is limited knowledge about individual differences in students' and educators' adaptability to remote education in response to the pandemic. This paper explored healthcare students' and educators' adaptability experiences to remote education. Drawing on pragmatism, a convergent mixed-methods design was adopted. Data were collected between May and August in 2020 using an online survey, followed by interviews with students and educators of five large health courses at an Australian research-intensive University. Data included 476 surveys and seven focus group interviews with 26 students, and 95 surveys and 17 individual interviews with educators. Results were interpreted through an integration of quantitative and qualitative elements from student and educator experiences. Findings indicated that students were less adaptable than educators. Whilst remote learning was less appealing than in-person learning, some students adapted well to the new learning environment. Limited social learning, transmissive pedagogy, and lack of technical and non-technical skills were identified as factors that impacted upon the experience of students and educators. Navigating the challenges associated with remote education provided students and educators with a unique opportunity to improve adaptability—an attribute critical for future uncertainties in healthcare practice.
The purpose of this survey study was to explore the relationships between well-being and adaptability among music teacher educators in the United States during the 2020–2021 academic year. We used an online questionnaire to collect data related to demographics, well-being, adaptability, and academic year events. The 85 participants reported relatively high levels of psychological well-being and adaptability. Specific adaptability attributes (e.g., ability to draw on positive feelings and emotions when confronted with new situations in the classroom) were found to be significant predictors of overall well-being.
The success of blended learning depends on many factors. Among these, adaptability, though acknowledged as an important issue in blended learning, warrants further study, together with its influencing factors. This study aimed to explore non-English majored learners’ adaptability to blended learning in College English from the perspective of English teachers. Semi-structured interviews with 16 English instructors from 5 universities were conducted, and the interview transcripts were qualitatively analyzed. The results showed that the non-English majored learners were not perceived to have fully adapted themselves to the new mode of College English and their adaptability was highly affected by agentive factors and contextual factors. The findings emphasize the key role of learners’ adaptability in blended learning environments. Additionally, this study further provides a vital insight for administrators and instructors to reconsider the role of learners’ responses to the blended learning mode and apply this understanding to improve achievement in English courses.
This study explored the relationships among learning adaptation, peer attachment, and learning conformity behavior among college students in Hengyang, Hunan Province, China. A total of 704 questionnaires were collected from three universities in Hengyang through purposive sampling, and 650 valid questionnaires were obtained. Structural equation models were used to test the direct and moderating effects among the variables. The research indicated that good learning adaptation in Hengyang college students had a positive impact on learning conformity behavior and that college students’ peer attachments had a positive effect on their learning conformity behavior. Compared with students with low peer attachment, students with high peer attachment were better at learning and adapting; they engaged in learning conformity behavior more frequently. College students’ peer attachment positively moderated the relationship between learning adaptation and learning conformity behavior.
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The article presents the results of an empirical study of academism in the assessments of professors of Russian classical universities. In total, 50 experts from various universities of Russia took part in the online survey. The aim of the study was to assess the role of academism in the development of university, maintaining the quality of education and maintaining the authority of professors at the university. The article gives a definition of the concept of academism, and reveals its structure. Basically, academism represents academic freedoms and the intellectual potential of the individual. The main results of the study include the following provisions: 1) academism is an integral part of a modern university and influences the formation of relevant values and traditions of university life; 2) academism influences the quality of students’ education, the preservation of high requirements in the educational process; 3) academism determines the authority of professors and teachers, promotes their active involvement in the social and cultural life of the university.
Evidence suggests that short‐term study abroad experiences can help advance various dimensions of second language (L2) learners’ linguistic competence. However, the extent to which gains following a brief sojourn are durable and maintained after immersion remains an open question. The present study examined both the immediate and sustained learning outcomes following a short‐term study abroad program by considering L2 Spanish learners’ (a) linguistic gains in global proficiency, oral production, and L2 knowledge, and (b) nonlinguistic gains in perceived psychobehavioral flexibility, a trait that remains underresearched despite its broad relevance to study abroad contexts. Data were collected at the onset and end of a summer study abroad program in Spain, as well as 5 weeks post‐sojourn, after students’ reimmersion in the first language environment. Mixed‐effects models revealed that gains in global proficiency and some aspects of oral production and L2 knowledge, as well as increases in perceived psychobehavioral flexibility, were noticeable at the end of the program and were maintained for several weeks post‐sojourn in the absence of further L2 instruction. Findings provide a first look into the potential of short‐term study abroad for promoting durable developmental changes across linguistic and nonlinguistic domains.
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Student engagement has been identified as an influential mediator between classroom interactional quality and adolescent learning outcomes. This study examined the relationship between classroom quality and student behavioural engagement in secondary school classrooms. Three dimensions of classroom quality (emotional, organisational and instructional support) and the dimension of student engagement were observed in nine classrooms using the Classroom Assessment Scoring System. Self-ratings of behavioural engagement were provided by 181 Finnish secondary school students along with their teachers’ ratings of classroom-level student engagement. The results showed, first, that there was variation in both classroom quality and student behavioural engagement between the classrooms. Second, classroom organisational support was associated with observed and with teacher- and student-rated engagement, and instructional support was associated with student-rated and observed engagement. Third, emotional support did not have a direct effect on student engagement but contributed to student engagement indirectly via organisational and instructional support. There were no gender differences with respect to self-reported engagement. Class size had a positive effect on teacher-rated engagement. The results demonstrated specific associations between the domains of classroom quality and student behavioural engagement in secondary school classrooms.
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Informed by self-determination theory, the aims of this study were to examine the extent to which students' interpersonal relationships (a basic psychological need) with teachers, parents, and peers are associated with personal best (PB) goals and academic engagement, as well as the extent to which PB goals are associated with academic engagement beyond the effects of interpersonal relationships. With a sample of 3232 students (aged 11-19) from independent schools catering to day and boarding students in major cities in the US, Canada, and the UK, we used structural equation modeling to examine the associations among these constructs. Students' perceptions of teacher, parent, and peer relationships were associated with PB goals and the three types of academic engagement: cognitive, behavioral, and emotional engagement. In addition, there were significant indirect effects via PB goals from teacher and peer relationships to cognitive and behavioral engagement. These results provide new understanding of the relative salience of associations among interpersonal relationships, PB goals, and academic engagement. They also suggest that PB goals are one mechanism by which interpersonal relationships may connect with academic engagement. Implications are discussed.
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Given recent concerns around boys’ literacy, this study examined multidimensional writing motivation and engagement among boys. We explored internal and external validity of 11 adaptive (e.g. self-efficacy for writing) and maladaptive (e.g. disengagement from writing) factors of writing motivation and engagement. The sample comprised 781 male Australian high school students (aged 11–18 years). We used confirmatory factor analysis and structural equation modelling to conduct our analyses. Results confirmed the internal structure of the factors and revealed that parent education, and to a lesser degree language background, predicted several motivation and engagement factors. In addition, the adaptive motivation and engagement factors were associated positively with several writing (e.g. enjoyment of writing) and literacy outcomes (e.g. literacy achievement), whereas the maladaptive factors tended to be negatively associated. Invariance was found in the associations among the motivation and engagement factors and the outcomes across several participant subgroups (e.g. by gender, language background). There was also measurement invariance between the study’s sample and five randomly drawn samples of students from archived data on domain-general academic motivation and engagement. Combined, our analyses shed light on a domain-specific area of motivation and engagement – writing – that could benefit from an integrative multidimensional examination.
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This study seeks to examine a multidimensional model of student motivation and engagement using within- and between-network construct validation approaches. The study tests the first- and higher-order factor structure of the motivation and engagement wheel and its corresponding measurement tool, the Motivation and Engagement Scale - High School (MES-HS; formerly the Student Motivation and Engagement Scale). The study draws upon data from 12,237 high school students from 38 Australian high schools. The hypothesized 11-factor first-order structure and the four-factor higher-order structure, their relationship with a set of between-network measures (class participation, enjoyment of school, educational aspirations), factor invariance across gender and year-level, and the effects of age and gender are examined using confirmatory factor analysis and structural equation modelling. In terms of within-network validity, (1) the data confirm that the 11-factor and higher-order factor models of motivation and engagement are good fitting and (2) multigroup tests showed invariance across gender and year levels. In terms of between-network validity, (3) correlations with enjoyment of school, class participation and educational aspirations are in the hypothesized directions, and (4) girls reflect a more adaptive pattern of motivation and engagement, and year-level findings broadly confirm hypotheses that middle high school students seem to reflect a less adaptive pattern of motivation and engagement. The first- and higher-order structures hold direct implications for educational practice and directions for future motivation and engagement research.
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Prior research has shown personal best (PB) goals to be significantly related to students’ motivation, engagement and achievement. However, research thus far has investigated PB goals only among Western samples and it is unclear to what extent PB goals hold academic merit in the Asian context. It is also unclear whether PB goals explain variance in motivation and engagement beyond that explained by ‘classic’ performance, mastery and avoidance goals. With a sample of 3753 middle school students in China, the present study showed that mastery and PB goals explained the bulk of variance in motivation, engagement and academic buoyancy outcomes. It therefore appears that the effects of PB and ‘classic’ goals derived in Western contexts generalise to the Chinese context. Further, although correlated, mastery and PB goals explain unique variance in distinct academic outcomes such that mastery goals appear more salient in mapping onto motivation factors while PB goals appear more salient in mapping onto engagement and buoyancy factors.
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The present study sought to investigate the effects of a multidimensional educational intervention on high school students’ motivation and engagement. The intervention incorporated: (a) multidimensional targets of motivation and engagement, (b) empirically derived intervention methodology, (c) research-based risk and protective factors, (d) established practices that nurture optimal youth development, (e) use of interpersonally skilled staff, and (f) evidence-based programming. Using a pre-/post-treatment/control group design, it was found that the self-complete intervention brought about significant shifts in motivation and engagement. Specifically, findings showed that the treatment group made positive motivation shifts on key dimensions including task management, persistence, anxiety, failure avoidance, and uncertain control. Moreover, against a large weighted external comparison group, the treatment group made positive shifts on valuing, mastery orientation, planning, task management, persistence, failure avoidance, uncertain control, and self-handicapping. Taken together, these findings attest to the potential for multidimensional educational interventions for enhancing students’ motivation and engagement.
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The purpose of this prospective study was to (1) monitor the change in adjustment across time and life domains and (2) identify the resources predictive of short-and long-term adjustment to the university transition. A sample of 68 first-year students reported on events, resources, and well-being at four times across the year. Coping and appraisal were evaluated specifically in relation to three life domains (e.g., academic). Results showed that this transition represents a relatively acute stressor, evidencing the greatest strain for students and the largest impact on their well-being on entry. As time passed, students experienced steady improvement in most aspects of adjustment. Results also suggested that women, despite having more resources, may have greater vulnerability to this transition. Finally, the importance of a specific resource to the prediction of well-being depended on the measure of well-being assessed, the specific life domain addressed, the time of assessment, and whether or not concomitant change between predictor and criterion variables was taken into account.