Investigating grit and its relations with college students’
self-regulated learning and academic achievement
Christopher A. Wolters &Maryam Hussain
Received: 9 May 2014 / Accepted: 4 November 2014 / Published online: 18 November 2014
# Springer Science+Business Media New York 2014
Abstract We investigated grit and its relations with students ’self-regulated learning (SRL)
and academic achievement. An ethnically diverse sample of 213 college students completed an
online self-report survey that included the Grit Short scale (Duckworth and Quinn Journal of
Personality Assessment, 91(2), 166 –174, 2009), seven indicators of SRL and their past and
present academic achievement. Results indicated that one aspect of grit, perseverance of effort,
was a consistent and adaptive predictor for all indicators of SRL including value, self-efficacy,
cognitive, metacognitive, motivational, time and study environment management strategies,
and procrastination. A second aspect of grit, consistency of interest, was associated only with
the latter two facets of SRL. Perseverance of effort predicted achievement before, but not after,
accounting for SRL; hence, students ’engagement in SRL may serve as a mediating pathway
through which this aspect of grit is associated with improved academic outcomes. In contrast,
consistency of interest showed no relation to achievement. Implications of the findings for
additional research and instruction are discussed.
Self-regulated learning .
Over the past 25 years, self-regulated learning (SRL) has emerged as a major framework used
to understand, evaluate, and improve students ’functioning within academic contexts (Schunk
and Zimmerman 2008). Individual differences characterized by many as more stable or trait-
like, including personality, intelligence, and achievement motives, also have been investigated
repeatedly as an important influence on students ’academic functioning (Diseth, and
Kobbeltvedt 2010; Komarraju et al. 2009; Richardson et al. 2012). Studies integrating these
two perspectives indicate that accounting for both SRL and more stable individual differences
may provide for a better understanding of students ’academic performance than either one does
alone (Bidjerano and Dai 2007;DeFeyteretal. 2012; Eilam et al. 2009; Richardson and
Metacognition Learning (2015) 10:293 –311
C. A. Wolters ( *)
Department of Educational Studies, Dennis Learning Center, 250 Younkin, The Ohio State University,
Columbus, OH 43201-2333, USA
e-mail: [email protected]
Department of Educational Psychology, University of Houston, Houston, TX 77204, USA Abraham2009). In the present study, we advance this area of research by investigating the
relation of grit, SRL and academic achievement. Although grit is considered distinct from
other personal traits and has proven useful for explaining academic outcomes (Duckworth and
Quinn 2009; Maddi et al. 2012; Strayhorn 2013), the relation of grit and SRL has not yet been
investigated. The present study addresses this gap by exploring whether grit can be used to
predict seven indicators of college students ’engagement in SRL, and whether grit and SRL
together can be used to understand students ’academic achievement.
Grit is defined as a person ’s trait-level perseverance and passion for long-term goals
(Duckworth et al. 2007). As such, grit has been conceived as a stable characteristic or
disposition of the individual that, similar to traditional personality traits, influences his/her
attitudes and behavior across diverse contexts (Duckworth and Quinn 2009; Kleiman et al.
2013 ;Reedetal. 2013). Compared to their less gritty peers, individuals with higher levels of
grit are expected to exhibit greater persistence in the pursuit of their goals despite setbacks,
distractions, or other forms of interference. Within educational contexts, grit is portrayed as a
potentially important influence on outcomes such as students ’engagement, achievement level,
retention and probability of graduation (Duckworth and Quinn 2009; Maddi et al. 2012;
Although still quite limited, the research examining grit indicates that it can be measured
reliably and that it is empirically distinct from other trait-like individual differences. In
particular, Duckworth and colleagues (2007) developed an initial self-report measure of grit
and provided some evidence that it was different than traditional personality constructs such as
conscientiousness. Although analyses with adults suggested that it consisted of two related
dimensions, these researchers examined grit using a single 12-item scale. Based on samples
from several distinct populations, these researchers showed that this broad indicator of grit was
related positively to educational attainment, college grades, self-control, retention within a
rigorous military training program, and adolescents ’performance in a competitive national
spelling bee. Adding to this research, Duckworth and Quinn ( 2009) developed a shorter 8-item self-
report survey based on a subset of the original grit items. Re-evaluation of data from the
participants in Duckworth et al. ( 2007) with this smaller set of items as well as an additional
sample of adults indicated that grit was best modeled as two first-order latent factors that also
loaded on to one second-order latent factor. One of the first-order factors, titled consistency of
interest, reflected participants ’reported tendency to adhere to particular goals over longer
periods of time. The second first-order latent factor, termed perseverance of effort, represented
participants ’reported tendency to sustain the time and energy necessary for accomplishing
long term tasks even in the face of distractions. Despite the distinction between these factors,
Duckworth and Quinn utilized a single 8-item indicator of grit in several additional studies
with adolescents, military cadets and adults. Overall, Duckworth and Quinn replicated many of
the earlier findings from Duckworth et al. ( 2007) including the independence of grit from
aspects of personality, and its positive relation to academic success, retention within a rigorous
military training program, and ranking in a national spelling bee. They also found that the
single short measure of grit was relatively stable across the span of one year among
adolescents. Recent contributions from other researchers tend to confirm the view of grit as a potentially
important influence on aspects of individual ’s persistence and performance, including within
294 C.A. Wolters, M. Hussain academic contexts. Maddi et al. (2012), for instance, found that a singular indicator of grit was
a strong predictor of retention and performance in sample of military cadets. Higher levels of a
general measure of grit have also been linked to increased intensity of exercise (Reed et al.
2013 ) and reduced suicide ideation (Kleiman et al. 2013). In a sample of high school students,
MacCann and Roberts ( 2010) found that both dimensions of grit, but especially the persever-
ance of effort, were positively correlated with life satisfaction, multiple aspects of conscien-
tiousness and teacher ’s rating of social behavior, but not to grades or academic readiness.
Finally, Strayhorn ( 2013) found that an overall indicator of grit was a positive predictor of self-
reported grades among African-American males attending a university with a predominantly
White student population. In this study, grit was a stronger predictor of college grades than
high school GPA and other standardized college entrance exams. Summing across this work, grit appears to represent a compound personal characteristic
that is associated with students ’tendency to be successful within achievement contexts. The
evidence linking grit specifically to students ’academic achievement, however, is still very
limited and somewhat inconsistent. For instance, the two studies that examined the relation of
grit with students ’course grades produced conflicting results (MacCann and Roberts 2010;
Strayhorn 2013). Further, the pathways through which this impact may be realized have not
yet been examined. In particular, the possibility that grit is associated with increased engage-
ment in SRL has not been investigated.
SRL is understood generally to be the process through which students take an active,
purposeful role in managing motivational, cognitive and behavioral aspects of their own
learning (Pintrich 2004; Zimmerman 2000). This management is accomplished through
students ’engagement of various sub-processes that include goal setting, activation of relevant
prior knowledge, progress monitoring, engagement and regulation of learning strategies, and
reflection (Pintrich 2004; Winne and Hadwin 1998). Across these various sub-processes, two
consistently critical components of SRL reflect students ’motivation and their engagement and
use of different cognitive and regulatory strategies.
SRL is an effortful process that involves the activation and use of substantial cognitive and
metacognitive resources. Hence, motivation is viewed as an important facet of what it takes to
engage in SRL (Pintrich and Zusho 2002; Winne and Hadwin 2008; Zimmerman and Schunk
2008 ). The forms of motivation that have been incorporated into models of SRL are diverse
with no clear hegemony (Schunk and Zimmerman 2008). For instance, models of SRL have
highlighted the roles of goal setting, achievement goals, interest, value, perceived self-efficacy
and attributions (Pintrich and Zusho 2002;WinneandHadwin 2008). The focus in this study is
on two aspects of achievement motivation, perceived self-efficacy and value, which consis-
tently have been used within models of SRL (Pintrich and Zusho 2002;Zimmerman 2000).
Self-efficacy, defined as students ’beliefs about whether they are capable of successfully
reaching particular learning goals, has been intricately linked to their goal setting, choice
and effort within academic contexts (Linnenbrink and Pintrich 2003; Pajares1996;
Zimmerman 2000). As well, students who report higher levels of self-efficacy have been
found to exhibit increased use of the cognitive and metacognitive strategies central to SRL.
Even when examined outside the framework of SRL, self-efficacy has proven to be an
Grit and self-regulated learning 295 important predictor of success, retention, and performance within college student populations
(Richardson et al.2012; Robbins et al. 2004).
Students ’value for the material they are learning is also a key form of motivation within
many models of SRL (Pintrich and Zusho 2007; Wigfield and Cambria 2010). Value is a multi-
dimensional construct that can incorporate students ’personal interest, importance, usefulness,
and aspects of situational interest (Wigfield and Eccles 2000). In general, students who
perceive the materials or skills they are learning as useful, interesting, important or enjoyable
are more likely to engage the regulatory strategies necessary for SRL and evidence higher
academic achievement (Pintrich and Zusho 2007; Wigfield and Cambria 2010).
From its inception, the activation and effective use of strategies that promote learning,
understanding and achievement has been a hallmark of what it means to engage in SRL
(Pintrich 2004; Winne and Hadwin 1998;Zimmerman 2000). Although cognitive and
metacognitive learning strategies have been examined most commonly, researchers recognize
that other types of strategies are also critical to successful SRL (Pintrich 2004; Wolters 2003a).
Consistent with this view, we assess four distinct types of strategies that reflect adaptive
aspects of SRL including cognitive, metacognitive, motivational, and time and study environ-
ment management. Generally, cognitive strategies are understood to include different tactics that students use to
facilitate the encoding and storage of the material they are supposed to learn (Weinstein et al.
2011 ). For instance, cognitive strategies include efforts to rehearse, elaborate, or organize
material one is trying to learn. Closely linked to these, metacognitive strategies include
students ’efforts to plan, select, monitor, evaluate and, when necessary, modify or regulate
their use of learning strategies (Pintrich et al. 2000). Motivational regulation strategies
represent students ’efforts to control their own level of motivation or motivational processing
(Wolters 2003a). This type of strategy, including self-consequating, self-talk about goals, and
making learning tasks more enjoyable, are thought to be particularly important when students
are facing obstacles to their continued engagement and effort on academic tasks (Wolters 1998,
2003a ). Time management and control of the learning environment also represent important
self-regulatory skills (Pintrich 2004; Zimmerman et al. 1994). These more behavioral strate-
gies reflect students ’efforts to control when and where they study including the use of
planners, to-do lists, and well-organized study spaces. Overall, there is compelling evidence that the use of SRL strategies is associated positively
with students ’achievement within academic contexts. The evidence for this connection is
strongest for students ’use of cognitive and metacognitive strategies (Pintrich and Zusho
2007 ). Research also shows that college students have a repertoire of motivational regulation
strategies and that use of these strategies is associated with increased effort, persistence, and
academic performance (Schwinger et al. 2009; Wolters 1998). Although not always conducted
using a SRL framework, there is also ample research showing that students who report greater
time management tend to get better grades (Claessens et al. 2007; Kitsantas et al. 2008;Macan
et al. 1990).
We investigate procrastination as a reflection of students ’ability to effectively self-regulate
their learning. Procrastination can be defined as students ’delay of tasks or decisions that are
necessary and will eventually be completed (Steel 2007). Although studied through many
296 C.A. Wolters, M. Hussain theoretical perspectives, procrastination repeatedly has been portrayed as a failure of effective
self-regulation (Steel2007;Wolters 2003b). Consistent with this view, procrastination by
students within academic settings has been associated with lowered academic performance
(Schouwenburg et al. 2004). Procrastination also has been linked to maladaptive outcomes
such as reduced sense of well-being, depression, stress and fatigue (Schraw et al. 2007;Steel
2007 ). At the same time, studies have consistently found that procrastination is prevalent
among college student populations with some estimates indicating that over 75 % of students
reporting that they procrastinate regularly (Ferrari et al. 2007; Schraw, et al. 2007; Steel 2007).
Grit and self-regulated learning
According to Pintrich ( 2004), one assumption consistent within most models of SRL is that
SRL processes serve as a mediator between students ’personal and background characteristics
and their performance within particular contexts. Dispositions, personality traits, or other stable
individual differences such as grit are, therefore, commonly viewed as precursors or potential
influences on the attitudes, beliefs, cognitive processes, and behaviors that embody SRL
(Bidjerano and Dai 2007; Eilam et al. 2009; Komarraju et al. 2009). In line with this
assumption, one would expect that aspects of grit could be used to explain college students ’
motivation and use of strategies emblematic of SRL. Up to this point, however, no published
empirical research has investigated these theoretical links directly. Duckworth et al. ( 2010)did
find that grit was a positive predictor of the deliberate and more effortful forms of practice
reported by contestants preparing for a national spelling bee. This study, however, did not
assess any of the specific strategies used during the practice time and was focused on early
adolescents engaged in an extra-curricular activity. The overall goal of the present study was to
address this major gap in prior research by examining the relations between aspects of grit and
college students ’SRL as represented by measures of their value, self-efficacy, reported use of
four types of learning strategies, and procrastination. Despite the lack of studies examining grit and SRL directly, research investigating similar
trait-like individual differences supports the need to investigate these relations. Based on the
five-factor model of personality, for instance, greater conscientiousness among college stu-
dents tends to be associated with higher levels of academic motivation and especially self-
efficacy or perceived competence (De Feyter et al. 2012; Komarraju et al. 2009; Richardson
and Abraham 2009). As well, college students who are more conscientious also tend to report
increased use of some learning strategies typical of SRL (Bidjerano and Dai 2007). Given its
association with attributes such as diligence, dependability, organization, punctuality, careful-
ness and self-control, conscientiousness also has been linked to lower levels of procrastination
(vanEerde 2004). Although not found in every case (Trautwein et al. 2009), several studies
have concluded that the influence of conscientiousness and other personality traits on students ’
achievement is mediated by their motivation and use of regulatory strategies (Bidjerano and
Dai 2007 ;DeFeyteretal. 2012; Eilam et al. 2009; Richardson and Abraham 2009).
Achievement motives represent another type of trait or stable disposition that has been used
to understand students ’academic functioning (Bartels et al. 2010; Diseth and Martinsen 2003).
Motives linked to wanting to achieve success and to avoid failure both have been used to
explain more context specific aspects of students ’motivation, especially their achievement
goals (Bartels et al. 2010;Chenetal. 2009; Conroy and Elliot 2004;ElliotandChurch 1997;
Elliot and McGregor 2001; Elliot and Murayama 2008; Michou et al. 2013). Likewise,
approach motives have been associated positively with self-reported engagement and the use
deep, metacognitive or other adaptive types of strategies emblematic of SRL (Bartels and
Grit and self-regulated learning 297 Magun-Jackson2009; Bartels et al. 2010; Diseth and Kobbeltvedt 2010; Diseth and Martinsen
2003 ;Michouetal. 2013). In contrast, fear of failure or other avoidance motives have been
associated with performance-avoidance goals and test anxiety, along with decreased use of
adaptive strategies (Bartels et al. 2010), increased use of surface strategies (Diseth and
Kobbeltvedt 2010; Diseth and Martinsen 2003), and increased levels of self-handicapping,
procrastination, and other indicators of poor self-regulation (Chen et al. 2009). Finally, studies
also show that the relations of achievement motives to students ’academic performance may be
mediated by more situational forms of motivation and engagement (Diseth and Kobbeltvedt
2010 ; Diseth and Martinsen 2003; Elliot and Church 1997; Elliot and Murayama 2008).
In sum, individual differences similar to grit hav e been linked to several indicators of students’
SRL such as their motivation, use of learning strategies, and procrastination. Research investi-
gating these relations with regard to grit, however , is absent. We address this gap in prior work via
four related research questions. One , is grit related to college students’value and self-efficacy?
Given its basic conceptual definition as well as past research with conscientiousness and
achievement motives, we expected that grit would be associated positively with both of these
adaptive motivational beliefs and attitudes. Two, is grit related to more strategic aspects of
students ’SRL including their use of different regulatory strategies and level of academic
procrastination? We expected that grit would be a positive predictor of students ’use of regulatory
strategies and negatively associated with their self-reported procrastination. Three, is grit related
to college students’ academic achievement? Although th e evidence of this connection is not
entirely consistent, we anticipated that grittier students would tend to get better grades. Finally, do
the motivational and strategic aspects of SRL mediate the relation between grit and students ’
academic performance? Given findings with other types of individual differences, we expected
that aspects of SRL may also mediate any relation between grit and students ’grades.
The 213 participants for this study came from a large and diverse public university. The
students were primarily female ( n= 188, 88 %), and self-reported their race/ethnicity as
African-American ( n= 45, 21 %), Asian/Pacific Islander ( n= 53, 25 %), Hispanic ( n= 62,
29 %), White ( n= 43, 20 %), or Other ( n= 10, 5 %). The academic rank reported by the
participants included freshman ( n= 28, 13 %), sophomore ( n= 51, 24 %), junior ( n= 74, 35 %),
and seniors or post-baccalaureate ( n= 60, 28 %).
Participants were recruited through a subject pool associated with multiple undergraduate
psychology and educational psychology courses. Students in these courses had access to an
electronic list with short descriptions for the stud ies that could be used to satisfy requirements for
participation in research. Students who elected to volunteer for the present study clicked on a link
that took them first to a consent document and, if approved, to the actual survey. Surveys were
completed during the final two weeks of the au tumn semester before the final exam period.
298 C.A. Wolters, M. Hussain Measures
The primary instrument was an online self-report survey with a total of 136 items. Only data
from 78 items related to demographics, grit, achievement motivation, strategy use, procrasti-
nation and academic performance were used in the present study. Other than the demographics
and performance sections, all items used Likert-styled items with a response scale ranging
from 1 (not at all true of me )to5(very true of me ).
Grit Participants completed the eight-item Grit Short scale (Duckworth and Quinn 2009). In
line with Duckworth and Quinn, a confirmatory factor analysis with the current sample
indicated that a measurement model with two first-order latent factors fit the data well ( χ
(19,N= 213) =20.04, p= .392; RMSEA = .02 (90 % confidence interval [CI] = .00, .06),
CFI = .997). In contrast, alternative models that reflected all items in one general factor ( χ 2
(20,N= 213) =125.756, p< .001; RMSEA = .16 (90 % CI = .13, .19), CFI = .645) or that
included a second order general factor ( χ 2(36,N=213) =333.81, p< .001; RMSEA = .20
(90 % CI = .18, .22), CFI = .000) did not fit the data well. Given these findings and consistent
with prior work with this scale (Duckworth and Quinn 2009), we constructed separate scales
representing the two first order factors based on the mean of the relevant items. Perseverance
of effort reflected students ’self-reported tendency to sustain the time and energy necessary for
accomplishing long-term tasks. Consistency of interestrepresented students’self-reported
tendency to stick with particular goals over longer periods of time. Items for these scales
included “I finish whatever I begin .” (Perseverance of effort), and “I often set a goal but later
choose to pursue a different one ”(Consistency of effort, reverse coded). Coefficient alphas for
each of the scales, as well as all those described below are presented in Table 1.
Motivation Motivation was measured using 11 items modified to assess students ’beliefs and
attitudes regarding school or learning in general rather than a specific course (Pintrich et al.
1993 ; Wolters 2003b). Six items represented value, or the extent that students perceived
coursework as useful, interesting, and important. Five items reflected s elf-efficacy ,orhow
confident students felt about their ability to learn and successfully complete their coursework.
Strategy use and procrastination Using modified items from Pintrich et al. ( 1993) and Wolters
( 2003b ; Wolters and Benzon 2013) to refer to learning or coursework in general, four types of
self-regulatory strategies were assessed. Cognitive strategiesmeasured participants ’reported
use of rehearsal, elaboration and organization strategies to complete academic tasks (9 items).
Metacognitive strategies measured participants’reported use of techniques for planning,
monitoring, and managing their learning strategies (9 items). Time and study environment
management reflected the extent to which students believed they used effective strategies for
academic scheduling and regulating where they studied (8 items). Motivational strategies
measured participants ’reported use of strategies for managing their level of motivation and
their effort at particular academic tasks (14 items). Finally, procrastinationwas assessed by
adapting the 12 items from the Pure Procrastination Scale (Steel 2010) to refer to academic
contexts. These items measured students ’reported tendency to delay making decisions, begin
tasks, or miss deadlines for their academic work. As shown in Table 1, all of the motivation,
strategy use, and procrastination scales had moderate to high levels of internal consistency.
Academic performance Prior achievement was based on a single item on which students
reported their cumulative high school grade point average (HSGPA). Although not ideal, self-
reported grade point average is a widely used measure of academic performance and has
Grit and self-regulated learning 299 Ta b l e 1Means, standard deviations, and bivariate correlations for grit, self-regulated learning, and achievement variables
1. Consistency of interest –
2. Perseverance of effort .12 –
3. Value .03 .38 –
4. Self-efficacy .03 .41 .57 –
5. Cognitive strategies .07 .45 .44 .46 –
6. Metacognitive strategies .08 .51 .44 .44 .83 –
7. Motivational strategies .04 .51 .50 .42 .69 .71 –
8. Time & study environment management strategies .27 .55 .42 .32 .61 .65 .61 –
9. Procrastination −.36 −.49 −.28 −.23 −.35 −.41 −.39 −.67 –
10. Prior achievement −.12 .09 .11 .19 .11 .09 .09 .09 −.10 –
11. Current achievement .10 .29 .20 .26 .22 .24 .24 .41 −.41 .28 –
Alpha .18.104.22.168.22.214.171.124.92 ––
n 213 213 213 213 213 213 213 213 213 213 211
SD 0.57 0.72 0.78 0.75 0.68 0.72 0.75 0.71 0.90 2.15 0.48
Note :r≥ .20, p<.01; r≥ .24, p<.001
300 C.A. Wolters, M. Hussain shown a high correlation with actual grade point average (Caskie et al.2014;Kunceletal.
2005 ). Simple mean imputations were used to replace the missing observations for nine
students who did not respond or indicated that they did not know their HSGPA. The eight
response options for this item were based on 0.25 GPA increments from 8 (4.00 –3.76) to 1
(2.00 or below). Current achievement was based on three separate items on which students
reported the number of courses they were currently taking in which they expected to earn 1) an
A, 2) a B or C, and 3) a D or lower. Responses to these items were combined to create one five-
level variable with higher scores representing better expected grades in the courses they were
taking the semester the survey was completed. Students with the top score (i.e., a 5) reported
that they expected to receive an “A ”in all of their courses. Students with scores of 4 and lower
reported that they were expecting an increasingly larger number of B and C, or D and lower
Results are divided into two sections. First, we present descriptive information and bivariate
correlations among the major variables. Second, we discuss findings from three sets of
Descriptives and correlations
The means and standard deviations for the grit, SRL and achievement variables are presented
in Table 1. The mean for perseverance of effort appeared somewhat higher ( M=3.5) when
compared to the mean for consistency of interest ( M= 2.84). Consistent with previous studies
using similar scales with college populations (Wolters 2003b), all of the means for the SRL
variables fell near the middle of the response scale.
The bivariate correlations among the grit, SRL and achievement measures are also pre-
sented in Table 1. Most noteworthy among these results is the low correlation between the two
aspects of grit, as well as the distinctive pattern of relations each had with the SRL and
achievement measures. Perseverance of effort was positively correlated with both value and
self-efficacy, as well as with each of the four types of self-regulatory strategies. In contrast,
consistency of interest was correlated only to time and study environment management. Both
aspects of grit were correlated negatively with procrastination; only perseverance of effort was
correlated positively with current achievement (see Table 1).
Multiple regressions predicting SRL and achievement
Predicting students ’motivational beliefs We examined the relations of perseverance of effort
and consistency of interest to students ’value and self-efficacy in two separate multiple
regressions. In addition to the two aspects of grit, prior achievement, sex, and age were
included as control variables. Results from these analyses are presented in Table 2. Overall,
the model accounted for about 16 % of the variance in value, F(5, 205) =8.04, p< .001, and
about 19 % of the variance in self-efficacy, F(5, 205) =9.89, p< .001. Perseverance of effort
was a significant individual positive predictor for both value, β=.34, t(210) =5.20, p<.001,
95 % CI [.23, .51] and self-efficacy, β=.37, t(210) =5.78, p< .001, 95 % CI [.25, .52].
Together, these findings indicate that students who tended to report that they sustained their
engagement and did not give up on achieving long-term goals also reported, on average,
increased value for their schoolwork, and greater confidence that they could successfully learn
Grit and self-regulated learning 301 the material. In contrast, consistency of interest failed to emerge as significant individual
predictor for either aspect of motivation (see Table2).
Predicting students ’use of strategies and procrastination Next, we conducted a set of five
multiple regressions in which the two aspects of grit, the two indicators of motivation, and the
same control variables were used to predict the four strategy measures and procrastination.
These regressions were completed in two steps; with the grit and control variables entered
together in a first block, followed by the two motivational variables in a second block. As
presented in Table 3, the set of variables entered in the first block explained a significant
amount of variance in cognitive strategies, R
2=.21,F(5, 205) =10.71, p< .001, metacognitive
strategies, R 2=.26,F(5, 205) =14.21, p< .001, motivational strategies, R 2=.26,F(5, 205)
=14.61, p< .001, time and study environment management strategies, R2= .35,F(5, 205)
=21.93, p< .001, and procrastination, R 2=.36,F(5, 205) =22.57, p< .001. In this first block,
perseverance of effort was the only significant predictor of cognitive strategies, β=.43, t(210)
=6.77, p< .001, 95 % CI [.29, .53], metacognitive strategies, β=.50, t(210) =7.98, p<.001,
95 % CI [.37, .61], and motivational strategies, β=.51, t(210) =8.32, p<.001, 95 % CI [.41,
.67]. Both perseverance of effort and consistency of interest predicted time and study envi-
ronment management strategies, β= .52, t(210) =8.89, p< .001, 95 % CI [.40, .63], β=.22,
t (210) =3.75, p< .001, 95 % CI [.13, .42], and procrastination, β=−.42, t(210) = −7.38, p<.001,
95 % CI [ −.68, −.39], β=−.32, t(210) = −5.47, p<.001, 95 % CI [ −.69, −.32].
Adding the two motivational variables in the second step significantly increased the amount
of variance explained for cognitive strategies, ΔR
2=.11, F(7, 203) =13.73, p< .001,
metacognitive strategies, ΔR 2=.10, F(7, 203) =15.80, p< .001, motivational strategies,
Δ R 2=.12, F(7, 203) =18.29, p< .001, and time and study environment management strategies,
Δ R2=.05, F(7, 203) =19.51, p< .001. The amount of variance explained did not significantly
change for procrastination, ΔR 2=.01, F(7, 203) =16.58, p= .255, therefore, individual results
for the motivation predictors and this dependent variable are not presented or discussed further. In the second step, value was a positive predictor for cognitive strategies, β=.20, t(210)
=2.76, p< .01, 95 % CI [.05, .30], metacognitive strategies, β=.22, t(210) =3.18, p<.01, 95 %
CI [.08, .34], motivational strategies, β=.32, t(210) =4.68, p< .001, 95 % CI [.18, .44], and
time and study environment management strategies, β=.26, t(210) =3.79, p<.001, 95 % CI
[.11, .36]. Self-efficacy, in contrast, was a positive predictor only for cognitive strategies,
β =.24, t(210) =3.22, p< .01, 95 % CI [.08, .35] and metacognitive strategies, β=.17, t(210)
=2.41, p< .05, 95 % CI [.03, .30].
Ta b l e 2 Results of regression analyses predicting value and self-efficacy
Predictor Value Self-efficacy
Age .02 .01 .12 .02 .01 .15*
Female −.26 .16 −.11 −.14 .15 −.06
Prior achievement .03 .02 .09 .04 .02 .11
Perseverance of effort .37 .07 .34*** .39 .07 .37***
Consistency of interest −.01 .09 −.01 −.02 .09 −.02
2 .16*** .19***
Note :N =211, * p<.05, *** p< .001
302 C.A. Wolters, M. Hussain Even when accounting for the two motivational beliefs, the pattern of relations for the two
aspects of grit remained the same (see Table 3). Perseverance of effort was a positive
individual predictor for cognitive strategies, β=.28, t(210) =4.26, p<.001, 95 % CI [.14,
.39]; metacognitive strategies, β=.36, t(210) =5.56, p< .001, 95 % CI [.23, .48]; motivational
Ta b l e 3 Results of regression analyses predicting strategies for self-regulated learning and procrastination
Step 1 Step 2Step 1Step 2
Predictor BSEBβBSEB βBSEB βBSEB β
Cognitive strategies Metacognitive strategies
Age .00 .01 .01 −.01 .01 −.05 .00 .01 .01 −.01 .01 −.04
Female −.10 .14 −.05 −.02 .13 −.01 .10 .14 .04 .17 .13 .08
Prior achievement .02 .02 .07 .01 .02 .03 .02 .02 .05 .01 .02 .01
Perseverance of effort .41 .06 .43*** .26 .06 .28*** .49 .06 .50*** .35 .06 .36***
Consistency of interest .04 .08 .03 .04 .07 .04 .03 .08 .02 .03 .07 .03
Va l u e –.18 .06 .20** –.21 .07 .22**
Self-efficacy –.22 .07 .24** –.17 .07 .17*
2 .21*** .32***.26*** .35***
Δ R 2 .11 .09
Motivational strategies Time & study environment management
Age −.01 .01 −.07 −.02 .01 −.12* .00 .01 .00 .00 .01 −.03
Female −.09 .14 −.04 .01 .13 .00 .04 .13 .02 .10 .12 .05
Prior achievement .00 .02 .01 −.01 .02 −.03 .03 .02 .08 .02 .02 .05
Perseverance of effort .54 .07 .51*** .38 .07 .36*** .52 .06 .52*** .43 .06 .26***
Consistency of interest −
.01 .08 −.01 .00 .08 .00 .27 .07 .22*** .28 .08 .22***
Va l u e −.31 .07 .32*** −.24 .06 .26***
Self-efficacy –.12 .07 .11 –.00 .07 .00
2 .26*** .39***.35*** .40***
Δ R 2 – .13– .05
Age −.01 .01 −.10 −.01 .01 −.09
Female .17 .16 .06 .14 .16 .05
Prior achievement −.05 .03 −.12 −.05 .03 −.12
Perseverance of effort −
.54 .07 −.43*** −.51 .08 −.41***
Consistency of interest −
.54 .08 −.32*** −.51 .09 −.32***
Va l u e –−.14 .08 −.12
Self-efficacy –.06 .09 .05
2 .35*** .36***
Δ R 2 – .01
Note :N =211; * p<.05. ** p<.01. *** p< .001
Grit and self-regulated learning 303 strategies,β= .36, t(210) =5.84, p< .001, 95 % CI [.25, .51]; and time and study environment
management strategies, β=.43, t(210) =6.92, p< .001, 95 % CI [.31, .55]. Consistency of
interest was a significant individual predictor only for time and study environment manage-
ment strategies, β=.22, t(210) =3.94, p< .001, 95 % CI [.14, .42].
Predicting students ’current achievement In a final regression, we examined the control
variables, the two dimensions of grit, and the seven indicators of SRL together as potential
predictors of students ’current academic achievement. The control and grit variables were
entered in a first step, followed by the SRL variables in a second step. This design provides
insight into whether the two aspects of grit are able to account for variance in college students ’
academic performance both before and after accounting for their SRL. As a group, the variables in the first step explained a significant amount of variance in
current achievement, R
2=.15,F(5, 202) =7.24, p< .001 with both prior achievement, β=.27,
t (207) =3.92, p< .001, 95 % CI [.03, .09] and perseverance of effort, β=.23, t(207) =3.41,
p < .01, 95 % CI [.06, .24] emerging as positive individual predictors.
Adding the SRL variables increase the amount of variance explained in current achieve-
ment, F(12, 195) =6.14, p< .001 to about 27 %. As one might expect, prior achievement
remained a strong positive predictor of current, β= .21, t(207) =3.17, p< .01, 95 % CI [.02,
.08]. As well, time and study environment management strategies, β=.32, t(207) =2.99,
p < .01, 95 % CI [.07, .35], self-efficacy, β=.18, t(207) =2.25, p< .05, 95 % CI [.01, .21] and
procrastination, β=−.20, t(207) = −2.28, p<.05, 95 % CI [ −.20, −.01] emerged as individual
predictors of current achievement. Perhaps most noteworthy, after the addition of the SRL
variables, perseverance of effort was no longer a significant individual predictor of current
achievement, β=−.00, t(207) = −.02, p=.984, 95 % CI [ −.10, .10] (see Table 4).
Ta b l e 4 Results of regression analysis predicting current achievement
Step 1 Step 2
Predictor BSEBβBSEB β
Age .01 .01 .06 .00 .01 .02
Female −.08 .10 −.05 −.07 .10 −.05
Prior achievement .06 .02 .27*** .05 .01 .21**
Perseverance of effort .15 .04 .23** .00 .05 .00
Consistency of interest .09 .06 .11 −.02 .06 −.02
Va l u e −.02 .05 −.04
Self −efficacy .11 .05 .18*
Cognitive strategies −.06 .08 −.09
Metacognitive strategies −.02 .08 −.03
Motivational strategies −.03 .06 −.04
Time & study environment management strategies .21 .07 .32**
Procrastination −.11 .05 −.20*
2 .15*** .27***
Δ R 2 .12
Note :N =208; * p< .05. ** p< .01. ***, p<.001
304 C.A. Wolters, M. Hussain Discussion
Grit has been proposed as a stable trait-like characteristic(s) that can be used to explain
important academic outcomes among college students (Duckworth and Quinn2009;
Strayhorn 2013). Grittier students, it is argued, are more likely to persevere in the face of
adversity and maintain their pursuit of challenging long-term goals such as earning a college
degree. Our findings make an important contribution by providing initial evidence that
students ’engagement in SRL may serve as one key pathway through which grit leads to
academic success. In addition, findings provide new and noteworthy support for the need to
differentiate between two dimensions of grit. In the remainder of this section, we review the
evidence for these contributions, and identify implications for instruction, limitations and
directions for future research.
The relation of grit and college students ’SRL
Our findings address a notable gap in pri or research by providing preliminary
evidence that ties grit to core indicators of college students ’SRL. Perseverance of
effort was a strong positive predictor f or two aspects of motivation commonly
associated with SRL. Students who indicated that they were more diligent, worked
harder, and were less discouraged by setbacks also expressed greater interest, value,
and usefulness for their coursework and te nded to express increased confidence that
they could successfully complete academic ta sks. In contrast, consistency of interest,
representing students ’belief that they maintained pursuit of their goals across time,
hadnorelationtostudents ’value or self-efficacy. To our knowledge, this is the first
study to empirically link any indicator of grit to specific forms of achievement
motivation. As a further contribution, we also found that the two aspects of grit could be used to explain
four types of strategies emblematic of SRL among college students. Even when accounting for
their value and self-efficacy, students who perceived themselves as more diligent and effortful
workers also tended to report increased use of cognitive, metacognitive, motivational, and time
management strategies central to SRL. Students who reported greater consistency in pursuing
their established goals also tended to report increased use of time and study environment
management strategies. Although prior work has linked students ’use of self-regulation
strategies with traits that are similar to grit (Bidjerano, and Dai 2007; Eilam et al. 2009), the
present study is the first to do so specifically with a measure of grit. Our findings are also the first to show that grittier students may be less likely to procras-
tinate, one principal form of academic self-handicapping that is often portrayed as a failure of
SRL (Steel 2007;Wolters 2003b). Findings indicated that both facets of grit were associated
with reduced levels of self-reported delays in beginning and completing academic tasks. This
connection is consistent with the understanding that grit is a protective factor that may inhibit
behaviors that disrupt effective academic functioning (Duckworth et al. 2007; Strayhorn
2013 ). This finding is also in line with studies showing similar patterns between more adaptive
personality traits and procrastination (Steel 2007).
Our findings also contribute to a greater understanding of the influences on college
students ’SRL. Conceptual models stress that many of the beliefs, knowledge, and strategies
necessary for SRL are malleable or can be improved through instruction (Pintrich and Zusho
2007 ). Nevertheless, most models also assume that SRL is also a function of an individual ’s
stable or trait-like dispositions (Pintrich 2004). Prior support for this assumption has been
found with regard to personality traits and achievement motives (Bidjerano and Dai 2007;
Grit and self-regulated learning 305 Bartels et al.2010; De Feyter et al. 2012; Diseth and Kobbeltvedt 2010; Komarraju et al. 2009;
Michou et al. 2013; Richardson and Abraham 2009). Our findings support and extend this
perspective by showing that grit, another dispositional individual difference, also can be used
to explain the extent to which a diverse group of college students report several key aspects of
SRL as a mediator of grit
Our findings also provide insight into the relation of grit to academic performance, and the
possibility that engagement in SRL may mediate this relation. Three conditions are necessary
to draw the conclusion that SRL mediates the relation between grit and academic performance
(Baron and Kenny 1986). One, grit must be related directly to students ’academic perfor-
mance. Our findings met this condition when considering perseverance of effort but not for
consistency of interest. Even when accounting for their reported prior performance, students
who indicated that they were more diligent and hard-working expected, on average, to obtain
higher grades for the current semester than did those students who saw themselves as
providing effort less dependably. The lack of a direct relation between consistency of interest
and students ’self-reported grades precluded any mediating relation for this aspect of grit and
this pathway is not discussed further. A second condition necessary for mediation is that perseverance of effort must be related
directly to the potential mediators (Baron and Kenny 1986). As described in the previous
section, this condition was met for all seven indicators of SRL that we examined. The third and
final condition for mediation also was met for perseverance of effort. Specifically, the strength
of relation between perseverance of effort and academic achievement was reduced when
accounting for students ’motivational beliefs, use of regulatory strategies, and procrastination.
In fact, when accounting for these various indicators of SRL, the ability of perseverance of
effort to explain current academic achievement was reduced to the point that it was no longer
statistically different than zero. This finding suggests that, for students attending a large state
university with a diverse population and modest admission standards, the influence of
students ’grit on their academic achievement may be completely mediated by their engagement
in SRL. As well, findings point to self-efficacy, time and study environment management, and
procrastination as the most critical pathways for this mediation. Put differently, students with
increased perseverance of effort may perform better within postsecondary academic contexts
because they are more confident in their ability to succeed, effectively manage when and
where they study, and do not unnecessarily postpone completion of their academic work. This conclusion squarely supports the assumption that students ’SRL serves as a mediator
between more stable individual characteristics and their engagement, learning and achieve-
ment within academic contexts (Pintrich 2004). As well, our findings are in line with earlier
work showing that aspects of SRL may mediate the relation between other dispositions or
personality traits and students ’academic performance (Bidjerano and Dai 2007; De Feyter
et al. 2012; Eilam et al. 2009; Richardson and Abraham 2009). Our findings extend this work
by showing that perseverance of effort can also be included in models that connect disposi-
tional individual differences with students ’SRL and academic achievement.
We add to this research by including a broader spectrum of beliefs, strategies and behaviors
indicative of SRL. Another important contribution of the present work, therefore, is to identify
additional aspects of SRL that might mediate these relations within college student popula-
tions. In particular, our work suggests that college students ’tendency to self-regulate their time
and study environment, and to avoid unnecessary delays when completing academic tasks is a
critical pathway between personal dispositions or traits and students ’academic achievement.
306 C.A. Wolters, M. Hussain Dimensions of grit
Much of the existing research examining grit has examined how best to conceptualize,
measure, and differentiate it from other trait-like characteristics (Duckworth et al.2007;
Duckworth and Quinn 2009; Maddi et al. 2012). Although not a central goal of the study,
our findings do contribute to this line of research. Unlike many prior studies that utilized
younger or somewhat idiosyncratic samples (e.g., military cadets, contestants in a national
spelling bee), our participants were a diverse group of students at a large public university with
modest admission standards. Based on this population, we found that grit is best conceived as
having two distinct dimensions. The first, persistence of effort, reflects students ’perception of
themselves as diligent, hard-working, and continuing on important tasks even in the face of
setbacks. The second, consistency of interest, represents students ’perceived tendency to stick
with particular long-term goals even when faced with new alternatives. Although prior
research has provided evidence of a similar distinction (Duckworth and Quinn 2009), most
studies have relied on a single indicator for grit when examining its relation to potential
Three facets of our empirical findings support the need to differentiate among the two
dimensions of grit. One, the confirmatory factor analysis that investigated this differentiation
was a good fit to our data. Specifically, the two factor model differentiating perseverance of
effort from consistency of interest showed better fit than models with either a second-order
latent factor or a single eight-item factor representing overall grit. This distinction varies from
Duckworth et al. ( 2007) who found support for a distinction between these two dimensions as
first-order latent factors, but also a second-order factor representing grit as a whole. Two, the bivariate correlation between the two aspects of grit was positive but very modest.
Hence, students ’tendency to perceive themselves as diligent and hardworking did not
substantially overlap with their belief that they tended to stick to their long-term goals.
Three, the patterns of relations found for the two dimensions of grit in the regressions were
quite distinct. Perseverance of effort was a predictor of all seven indicators of SRL that we
investigated. In contrast, consistency of interest predicted only the two indicators of SRL that
most directly related to behavioral aspects of goal setting and planning (i.e., time and study
environment management and procrastination). Overall, our pattern of findings argues against
using a single global indicator to explore grit, at least among postsecondary students at a large
public university with an ethnically and academically diverse population. Rather, the concep-
tual distinction between the two aspects of grit first noted by Duckworth et al. appears
meaningful and should be continued in future studies.
Implications for practice
In light of the conceptual understanding of grit and SRL, our findings provide at least three
implications for educators interested in improving college students ’academic performance.
Although perseverance of effort was a strong positive predictor of students ’SRL and academic
performance, it may not be a reasonable target for instructional interventions. Conceptual
understandings portray grit more broadly, and perseverance of effort in particular, as having
stable trait-like qualities that reflect individuals ’genetic inheritance or develop over longer
periods of time (Duckworth et al. 2007). As is the case with other immutable dispositions,
therefore, it may be impractical for educators to focus on making students “grittier ”within a
particular course or even within their postsecondary educational experience more generally. In contrast to grit, the component processes of SRL are viewed as much more malleable and
responsive to intervention (Schunk and Zimmerman 1998). There is ample evidence that more
Grit and self-regulated learning 307 adaptive motivational beliefs, increased use of self-regulation strategies, and reduced procras-
tination can all be accomplished through well-designed interventions delivered within post-
secondary contexts (Richardson et al.2012; Robbins et al. 2004; Schouwenburg et al. 2004;
Schunk and Zimmerman 2008). Specific techniques to achieve these goals can include adjunct
coursework intended for students most deficit in SRL as well as instructional methods infused
more broadly into all courses (Hofer et al. 1998). In light of this existing work, efforts to
promote college students ’academic achievement may be more productive if they focus on the
motivational and strategic aspects of SRL rather than on increasing students ’grit directly. Of
course, research that explores and compares interventions targeting different aspects of grit and
SRL are needed to establish this conclusion more convincingly. Another implication suggested by the connection between perseverance of effort and
college students ’SRL and academic performance concerns the establishment of long-term
goals consistent with college success. Although grittier students may show greater persever-
ance and passion for their long-term goals (Duckworth et al. 2007), they may not necessary
have established high achievement, completion of a particular major, or graduation as high
priority personal goals. Students enter college for many different reasons, such as wanting to
avoid the labor market, achieve social goals, or because it was expected by their family. The
indirect connection between perseverance of effort and achievement in our study suggests that
it may be beneficial for postsecondary educators to ensure that all students view learning,
academic achievement, and graduation as personal accomplishments that are central to what
they want to achieve in life. Once established, these long term goals should then be pursued
more diligently, especially by those who are grittier and have the beliefs, attitudes and skills
necessary for SRL. Finally, even if one assumes that academic success and graduation is something all
university students want to achieve, assessing and promoting their level of grit may not be
an especially useful endeavor for postsecondary educators. College students are likely to have
a large and diverse set of long-term goals in addition to academic success. Conceptual models
of grit provide little insight into which of their long-term goals students will give priority or
strive to attain most whole-heartedly. It seems likely that even students with very high levels of
grit must, at times, give precedence to particular goals, or jettison certain long-term goals
completely. Indeed, career counseling rests on the assumption that individuals must refine their
interests and, in the process, abandon some of the professional and academic goals they might
once have strived to achieve (Platt and Drew 2013). Similarly, models of grit fail to explain
how general long-term goals (e.g., graduating from college) get transformed into specific
accomplishments (e.g., graduating from the University of Houston with a degree in chemistry).
The choice of which particular long-term goals assume priority and are most actively pursued
appears still to be a more direct function of motivational factors such as value, importance, and
perceived competence. Hence, interventions that target these more local influences on stu-
dents ’goals, choices and effort may be more effective for instructors and policymakers who
want to influence students ’academic outcomes.
Limitations and conclusions
One limitation to the present study is the self-report nature of all of the major variables
including students ’past and current academic achievement. The shared variance inherent in
this methodology suggests that the relations found here may be overstated and may be weaker
in studies relying on more diverse methods. Future research that assesses grit, SRL and
achievement using other valid methodologies should provide useful insights. The correlational
design of this study also is a limitation that might inflate the strength of the observed relations
308 C.A. Wolters, M. Hussain and precludes any causal conclusions about the relations that were found. It would be useful to
study these relations with a design that assesses students’trait-like dispositions earlier while
tapping into students ’engagement, SRL and achievement at later times. This type of design
would provide a more robust evaluation and greater insight into the potential causal relations
between students ’grit and their SRL and achievement. The use of expected, rather than actual,
grades is also a specific shortcoming of the present study. Conclusions would be strengthened
by research that tests these relations with indicators of academic performance that are more
direct (e.g., instructor assigned grades) and more diverse (e.g., specific exam grades, retention,
graduation). Finally, our sample was predominantly female; similar research with a more
gender-balanced sample would be worthwhile. Despite these limitations, the present study provides a substantial contribution as a prelim-
inary study that links grit, SRL and achievement within an academically and ethnically diverse
population of college students. Our findings demonstrate a number of important relations
between these two constructs, as well as their combined connection to students ’academic
achievement. Findings also highlight a number of potentially fruitful avenues for additional
empirical research and instructional applications.
Baron, R. M., & Kenny, D. A. (1986). The moderator –mediator variable distinction in social psychological
research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology,
51 (6), 1173– 1182. doi:10.1037/0022-35126.96.36.1993 .
Bartels, J. M., & Magun-Jackson, S. (2009). Approach –avoidance motivation and metacognitive self-
regulation: the role of need for achievement and fear of failure. Learning and Individual Differences,
19(4), 459– 463.
Bartels, J. M., Magun-Jackson, S., & Ryan, J. J. (2010). Dispositional approach-avoidance achievement
motivation and cognitive self-regulated learning: the mediation of achievement goals. Individual
Differences Research, 8 (2), 97–110.
Bidjerano, T., & Dai, D. Y. (2007). The relationship between the big-five model of personality and self-regulated
learning strategies. Learning and Individual Differences, 17 (1), 69–81.
Caskie, G. I. L., Sutton, M. C., & Eckhardt, A. G. (2014). Accuracy of self-reported college GPA: gender-
moderated differences by achievement level and academic self-efficacy. Journal of College Student
Development, 55 (4), 385–390. doi: 10.1353/csd.2014.0038 .
Chen, L. H., Wu, C. H., Kee, Y. H., Lin, M. S., & Shui, S. H. (2009). Fear of failure, 2× 2 achievement goal and self-handicapping: an examination of the hierarchical model of achievement motivation in physical educa-
tion. Contemporary Educational Psychology, 34 (4), 298–305.
Claessens, B. C., vanEerde, W., Rutte, C. G., & Roe, R. A. (2007). A review of the time management literature. Personnel Review, 36 (2), 255–276.
Conroy, D. E., & Elliot, A. J. (2004). Fear of failure and achievement goals in sport: addressing the issue of the chicken and the egg. Anxiety, Stress & Coping, 17 (3), 271–285.
De Feyter, T., Caers, R., Vigna, C., & Berings, D. (2012). Unraveling the impact of the big five personality traits on academic performance: the moderating and mediating effects of self-efficacy and academic motivation.
Learning and Individual Differences, 22 (4), 439–448.
Diseth, Å., & Kobbeltvedt, T. (2010). A mediation analysis of achievement motives, goals, learning strategies,
and academic achievement. British Journal of Educational Psychology, 80 (4), 671–687.
Diseth, Å., & Martinsen, Ø. (2003). Approaches to learning, cognitive style, and motives as predictors of
academic achievement. Educational Psychology, 23 (2), 195–207.
Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (GRIT –S). Journal
of Personality Assessment, 91 (2), 166–174.
Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92 (6), 1087–1101.
Duckworth, A. L., Tsukayama, E., & May, H. (2010). Establishing causality using longitudinal hierarchical linear modeling: an illustration predicting achievement from self-control. Social Psychological and Personality
Science, 1(4), 311 –317.
Grit and self-regulated learning 309 Eilam, B., Zeidner, M., & Aharon, I. (2009). Student conscientiousness, self‐regulated learning, and science
achievement: an explorative field study. Psychology in the Schools, 46(5), 420–432.
Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72 (1), 218–232.
Elliot, A. J., & McGregor, H. A. (2001). A 2× 2 achievement goal framework. Journal of Personality and Social
Psychology, 80 (3), 501–519.
Elliot, A. J., & Murayama, K. (2008). On the measurement of achievement goals: critique, illustration, and application. Journal of Educational Psychology, 100 (3), 613–628.
Ferrari, J. R., Driscoll, M., & Díaz-Morales, J. F. (2007). Examining the self of chronic procrastinators: actual, ought, and undesired attributes. Individual Differences Research, 5 (2), 115–123.
Hofer, B. K., Yu, S. L., & Pintrich, P. R. (1998). Teaching college students to be self-regulated learners. In D. Schunk and B. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice
(pp. 57 –85). New York: The Guilford Press.
Kitsantas, A., Winsler, A., & Huie, F. (2008). Self-regulation and ability predictors of academic success during college: a predictive validity study. Journal of Advanced Academics, 20 (1), 42–68.
Kleiman, E. M., Adams, L. M., Kashdan, T. B., & Riskind, J. H. (2013). Grateful individuals are not suicidal: buffering risks associated with hopelessness and depressive symptoms. Personality and Individual
Differences, 55 (5), 595–599.
Komarraju, M., Karau, S. J., & Schmeck, R. R. (2009). Role of the big five personality traits in predicting college
students ’academic motivation and achievement. Learning and Individual Differences, 19 (1), 47–52.
Kuncel, N. R., Crede, M., & Thomas, L. L. (2005). The validity of self-reported grade point averages, class rank, and test scores: a meta-analysis and review of the literature. Review of Educational Research, 75(1), 63–82.
doi: 10.3102/00346543075001063 .
Linnenbrink, E. A., & Pintrich, P. R. (2003). The role of self-efficacy beliefs in student engagement and learning
in the classroom. Reading &Writing Quarterly, 19 (2), 119–137.
Macan, T., Shahani, C., Dipboye, R. L., & Phillips, A. P. (1990). College students ’time management:
correlations with academic performance and stress. Journal of Educational Psychology, 82,760–768.
MacCann, C., & Roberts, R. (2010). Do time management, grit, and self-control relate to academic achievement
independently of conscientiousness? In R. Hicks (Ed.), Personality and individual differences: Current
directions (pp. 79–90). Bowen Hills, QLD, AUS: Australian Academic Press.
Maddi, S. R., Matthews, M. D., Kelly, D. R., Villarreal, B., & White, M. (2012). The role of hardiness and grit in predicting performance and retention of USMA cadets. Military Psychology, 24(1), 19–28.
Michou, A., Mouratidis, A., Lens, W., & Vansteenkiste, M. (2013). Personal and contextual antecedents of
achievement goals: their direct and indirect relations to students ’learning strategies. Learning and Individual
Differences, 23 ,187
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578.
Pintrich, P. (2004). A conceptual framework for assessing motivation and self –regulated learning in college
students. Educational Psychology Review, 16 ,385–407.
Pintrich, P., & Zusho, A. (2002). The development of academic self –regulation: The role of cognitive and
motivational factors. In A. Wigfield & J. Eccles (Eds.), Development of achievement motivation(pp. 249–
284). San Diego: Academic.
Pintrich, P. R., & Zusho, A. (2007). Student motivation and self-regulated learning in the college classroom. In R.
P. Perry & J. C. Smart (Eds.), The scholarship of teaching and learning in higher education: An evidence-
based perspective (pp. 731–810). The Netherlands: Springer.
Pintrich, P., Smith, D., Garcia, T., & McKeachie, W. (1993). Predictive validity and reliability of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53 ,801–813.
Pintrich, P., Wolters, C., & Baxter, G. (2000). Assessing metacognition and self –regulated learning. In G. Schraw
(Ed.), Metacognitive assessment (pp. 43–97). Lincoln: University of Nebraska Press.
Platt, A., & Drew, M. (2013). Career counseling. In D. Capuzzi & D. Gross (Eds.), Introduction to the counseling
thed., pp. 369 395) . New York: Routledge.
Reed, J., Pritschet, B. L., & Cutton, D. M. (2013). Grit, conscientiousness, and the transtheoretical model of change for exercise behavior. Journal of Health Psychology, 18 (5), 612–619.
Richardson, M., & Abraham, C. (2009). Conscientiousness and achievement motivation predict performance.
European Journal of Personality, 23 ,589–605.
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students ’academic
performance: a systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387.
Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study
skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130(2), 261–288.
310 C.A. Wolters, M. Hussain Schouwenburg, H. C., Lay, C. H., Pychyl, T. A., & Ferrari, J. R. (2004).Counseling the procrastinator in
academic settings[Electronic version]. Retrieved from http://www.elpub.bib.uniwuppertal.de/edocs/
Schraw, G., Wadkins, T., & Olafson, L. (2007). Doing the things we do: a grounded theory of academic
procrastination. Journal of Educational Psychology, 99 ,12–25.
Schunk, D., & Zimmerman, B. (Eds.). (1998). Self-regulated learning: From teaching to self-reflective practice .
New York: Guilford Press.
Schunk, D., & Zimmerman, B. (Eds.). (2008). Motivation and self-regulated learning: Theory, research, and
applications . Mahwah: Erlbaum Associates.
Schwinger, M., Steinmayr, R., & Spinath, B. (2009). How do motivational regulation strategies affect achieve-
ment: mediated by effort management and moderated by intelligence. Learning and Individual Differences,
19 ,621 –627.
Steel, P. (2010). Arousal, avoidant and decisional procrastinators: Do they exist? Personality and Individual
Differences, 48 ,926–934.
Steel, P. (2007). The nature of procrastination: a meta-analytic and theoretical review of quintessential self-
regulatory failure. Psychological Bulletin, 133 (1), 65–94.
Strayhorn, T. L. (2013). What role does grit play in the academic success of Black male collegians at
predominantly White institutions?. Journal of African American Studies ,1–10. doi: 10.1007/s12111-012-
Trautwein, U., Lüdtke, O., Roberts, B. W., Schnyder, I., & Niggli, A. (2009). Different forces, same conse- quence: conscientiousness and competence beliefs are independent predictors of academic effort and
achievement. Journal of Personality and Social Psychology, 97 (6), 1115–1128.
vanEerde, W. (2004). Procrastination in academic settings and the big five model of personality: A meta-analysis. In H. Schouwenburg, C. Lay, T. Pychyl, & J. Ferrari, (Eds.). Counseling the procrastinator in academic
settings (pp. 29–40). American Psychological Association.
Weinstein, C. E., Acee, T. W., & Jung, J. (2011). Self ‐regulation and learning strategies. New Directions for
Teaching and Learning, 2011 (126), 45–53.
Wigfield, A., & Cambria, J. (2010). Students ’achievement values, goal orientations, and interest: definitions,
development, and relations to achievement outcomes. Developmental Review, 30(1), 1–35.
Wigfield, A., & Eccles, J. S. (2000). Expectancy –value theory of achievement motivation. Contemporary
Educational Psychology, 25 (1), 68–81.
Winne, P., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 279–306). Hillsdale: Erlbaum.
Winne, P., & Hadwin, A. (2008). The weave of motivation and self-regulated learning. In D. Schunk & B. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297–
314). New York: Lawrence Erlbaum Associates.
Wolters, C. A. (1998). Self-regulated learning and college students ’regulation of motivation.
ucational Psychology, 90 (2), 224.
Wolters, C. A. (2003a). Regulation of motivation: evaluating an underemphasized aspect of self-regulated
learning. Educational Psychologist, 38 (4), 189–205.
Wolters, C. A. (2003b). Understanding procrastination from a self-regulated learning perspective. Journal of
Educational Psychology, 95 (1), 179–187.
Wolters, C., & Benzon, M. (2013). Assessing and predicting college students ’use of strategies for the self-
regulation of motivation. Journal of Experimental Education, 18 , 199–221.
Zimmerman, B. J. (2000). Self-efficacy: an essential motive to learn. Contemporary Educational Psychology,
25 (1), 82– 91.
Zimmerman, B., & Schunk, D. (2008). Motivation: An essential dimension of self-regulated learning. In D. Schunk & B. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications
(pp. 1 –30). Mahwah: Lawrence Erlbaum Associates Publishers.
Zimmerman, B., Greenberg, D., & Weinstein, C. (1994). Self-regulating academic study time: A strategy
approach. In D. Schunk & B. Zimmerman (Eds.), Self-regulation of learning and performance: Issues
and educational applications (pp. 181–199). Hillsdale: Lawrence Erlbaum Associates.
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