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Clarifying Achievement Goals and Their Impact


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The study of achievement goals has illuminated basic motivational processes, though controversy surrounds their nature and impact. In 5 studies, including a longitudinal study in a difficult premed course, the authors show that the impact of learning and performance goals depends on how they are operationalized. Active learning goals predicted active coping, sustained motivation, and higher achievement in the face of challenge. Among performance goals, ability-linked goals predicted withdrawal and poorer performance in the face of challenge (but provided a "boost" to performance when students met with success); normative goals did not predict decrements in motivation or performance; and outcome goals (wanting a good grade) were in fact equally related to learning goals and ability goals. Ways in which the findings address discrepancies in the literature are discussed.
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Clarifying Achievement Goals and Their Impact
Heidi Grant and Carol S. Dweck
Columbia University
The study of achievement goals has illuminated basic motivational processes, though controversy
surrounds their nature and impact. In 5 studies, including a longitudinal study in a difficult premed
course, the authors show that the impact of learning and performance goals depends on how they are
operationalized. Active learning goals predicted active coping, sustained motivation, and higher achieve-
ment in the face of challenge. Among performance goals, ability-linked goals predicted withdrawal and
poorer performance in the face of challenge (but provided a “boost” to performance when students met
with success); normative goals did not predict decrements in motivation or performance; and outcome
goals (wanting a good grade) were in fact equally related to learning goals and ability goals. Ways in
which the findings address discrepancies in the literature are discussed.
Considerable evidence suggests that much of achievement mo-
tivation (e.g., intrinsic interest, strategy use, and persistence) can
be understood in terms of the different goals individuals bring to
the achievement context (see Ames, 1992; Ames & Archer, 1988;
Butler, 1987, 1993; Dweck & Elliott, 1983; Dweck & Leggett,
1988; Elliott & Dweck, 1988; Harackiewicz, Barron, Carter,
Lehto, & Elliot, 1997; Kaplan & Maehr, 1999; Middleton &
Midgely, 1997; Nicholls, 1984; Pintrich, 2000a; Rawsthorne &
Elliot, 1999; Utman, 1997). However, there are some disagree-
ments and some conflicting findings on the nature of these rela-
tions. Specifically, researchers disagree on how to best define and
operationalize the major classes of goals, and on the precise impact
of these goals on motivation and achievement.
In the original goal models, two classes of goals were identi-
fied—performance goals, where the purpose is to validate one’s
ability or avoid demonstrating a lack of ability, and learning goals,
where the aim is to acquire new knowledge or skills (i.e., to
increase one’s ability; see Dweck & Elliott, 1983). Different
researchers have used different labels for these two classes of
goals—performance goals have also been called ego-involved
goals (e.g., Nicholls, 1984) or ability goals (e.g., Ames, 1992), and
learning goals have also been called mastery goals (e.g., Ames,
1992; Butler, 1993; Elliot & Harackiewicz, 1996; Meece & Holt,
1993) or task goals (e.g., Middleton & Midgely, 1997; Nicholls,
These two classes of goals were then linked to motivation and
performance in achievement situations. Performance goals, with
their emphasis on outcomes as measures of ability, were shown to
produce a vulnerability to helplessness and debilitation after a
setback or negative feedback, particularly in cases where current
perceptions of ability were low (Ames & Archer, 1988; Butler,
1993; Elliott & Dweck, 1988; Jagacinski & Nicholls, 1987; Meece,
Blumenfeld, & Hoyle, 1988). That is, when the goal is to validate
ability and individuals do not believe they can accomplish this,
motivation and performance tend to suffer. Learning goals, with
their emphasis on understanding and growth, were shown to fa-
cilitate persistence and mastery-oriented behaviors in the face of
obstacles, even when perceptions of current ability might be low
(Ames & Archer, 1988; Butler, 1993, Elliott & Dweck, 1988;
Jagacinski & Nicholls, 1987; Utman, 1997).
Performance and learning goals have also been shown to predict
real-world performance, including exam grades, course grades,
and achievement test scores, controlling for past performance
(Dweck & Sorich, 1999; Greene & Miller, 1996; Kaplan & Maehr,
1999; Meece & Holt, 1993; Midgely & Urdan, 1995; Roeser,
Midgely, & Urdan, 1996). In addition, goal effects obtain both
when the goals have been experimentally manipulated (Butler,
1987; Elliott & Dweck, 1988; Graham & Golen, 1991), and when
students’ naturally existing goals have been assessed (Ames &
Archer, 1988; Bouffard, Boisvert, Verzeau, & Larouche, 1995;
Midgely, Anderman, & Hicks, 1995; Miller, Behrens, Greene, &
Newman, 1993; Pintrich & DeGroot, 1990; Pintrich & Garcia,
1991). The fact that induced goals have been found to have strong
impact is important for two reasons. First, it means that goals can
have a causal role in producing achievement patterns. Second, it
means that learning environments can be constructed in ways that
enhance achievement (Ames, 1992; Maehr & Midgley, 1991;
Roeser et al., 1996).
Despite early agreement regarding the effects of performance
and learning goals on motivation and performance, recent research
has revealed a more complicated picture. Some researchers have
questioned whether learning goals affect performance at all, sug-
Heidi Grant and Carol S. Dweck, Department of Psychology, Columbia
This article is based on a doctoral dissertation submitted to Columbia
University by Heidi Grant under the supervision of Carol S. Dweck. It
was supported by National Institute of Mental Health Grant F31-
MH12706-01 to Heidi Grant. We thank Dean Kathleen McDermott and
Professor Leonard Fine and the Department of Chemistry at Columbia
University for their support and assistance with this project. We are also
grateful for the comments and suggestions given by the dissertation com-
mittee: Geraldine Downey, E. Tory Higgins, Harvey Hornstein, and Gab-
riele Oettingen. Finally, we thank Andrew Eliot, Judith Harackiewicz, and
Corwin Senko for their insightful comments on an earlier version of this
Correspondence concerning this article should be addressed to Heidi
Grant, who is now at the Department of Psychology, New York University,
6 Washington Place, 7th Floor, New York, New York 10003. E-mail:
Journal of Personality and Social Psychology Copyright 2003 by the American Psychological Association, Inc.
2003, Vol. 85, No. 3, 541–553 0022-3514/03/$12.00 DOI: 10.1037/0022-3514.85.3.541
gesting that they chiefly influence intrinsic motivation (e.g., Bar-
ron & Harackiewicz, 2001; Elliot & Church, 1997; Harackiewicz
et al., 1997; Harackiewicz, Barron, Tauer, Carter, & Elliot, 2000).
Some have argued that performance goals predict higher, not
lower, grades, and do not affect intrinsic motivation (e.g., Barron
& Harackiewicz, 2001; Elliot & Church, 1997; cf. Rawsthorne &
Elliot, 1999).
We propose that looking at the ways in which performance and
learning goals have been defined or operationalized can help
account for the discrepant findings that have been obtained by
different researchers. To test this proposal, items were created to
measure the different forms of goals that have been prominently
represented in existing research. Five studies explore the relation-
ships among these goals, their ability to predict intrinsic motiva-
tion and performance under highly challenging or difficult circum-
stances, and the mechanisms through which they may bring about
those effects. We begin by describing the important dimensions
along which the operationalizations of performance and learning
goals vary in current achievement goal research, and describing
how each of these dimensions is represented in the following
What Is a Performance Goal and What Is Its Effect?
Achievement goal researchers have already made one important
distinction among performance goalsnamely, the distinction be-
tween performance approach goals (where the focus is on attain-
ing success) and performance avoidance goals (where the focus is
on the avoidance of failure; Elliot, 1999; Elliot & Church, 1997;
Elliot & Harackiewicz, 1996; Middleton & Midgely, 1997; Pin-
trich, 2000a). In general, this program of research has suggested
that it is the avoidance form of performance goals that predict
lower intrinsic motivation and performance, with approach goals
often relating positively to performance.
However, as discussed below, the positive and negative effects
of performance approach goals have typically been found when
performance goals are operationalized in particular ways, and the
positive and negative effects of different types of performance
approach goals have not been systematically explored. Thus our
purpose in this article is to distinguish among approach forms of
performance goals, and we propose that they take at least three
distinct forms: (a) goals that are linked to validating an aspect of
self (e.g., ones ability), (b) goals that are explicitly normative in
nature, and (c) goals that are simply focused on obtaining positive
outcomes (i.e., doing well). It is the first form that was linked to
impairment in the earlier models, but it has tended to be the second
two forms that have been linked to more positive outcomes in
recent work. Let us take a closer look at these different forms of
approach goals.
For some researchers, the essence of a performance goal is
seeking to validate ones ability (operationalized either by sug-
gesting to participants that their performance on a task measures
the extent to which they possess a valued ability, or by assessing
the extent to which they generally strive to validate their ability).
Debilitation occurs when outcomes indicate a lack of ability, but
performance maintenance or enhancement can occur when success
is expected (Ames, 1992; Elliott & Dweck, 1988; see Dweck &
Leggett, 1988). It should be noted that debilitation here requires
the presence of challenges, setbacks, or failurean easy task or
course is not expected to produce debilitation, even in the presence
of strong performance goals. To represent this view, we developed
ability goal items (e.g., It is important to me to validate that I am
For others, the essence of a performance goal is a normative
comparison (i.e., wanting to perform better than others), and a goal
that is nonnormative (e.g., using an absolute standard such as a
perfect score, or tying absolute performance to self-worth) is not
considered to be a performance goal (Elliot, 1999; Elliot &
Church, 1997; Elliot & Harackiewicz, 1996; Maehr & Midgely,
1991; Pintrich, 2000b). Here, performance goals are often opera-
tionalized by informing participants that their performance on a
task will be evaluated normatively, or by measuring their agree-
ment with statements such as It is important to me to do well
compared to others in this class (Elliot & Church, 1997).
The issue of whether normative performance goals are empiri-
cally distinct from performance goals that do not contain a nor-
mative standard has not been systematically addressed in the
achievement goal literature. Yet it is an important question, be-
cause to some theorists, as noted, the presence of normative
comparison is the essence of a performance goal (Elliot & Harac-
kiewicz, 1996; see Rawsthorne & Elliot, 1999), and to others, a
potentially interesting but nonessential aspect of a performance
goal (Elliott & Dweck, 1988). It would be interesting to find that
normative and nonnormative performance goals do indeed differ,
particularly if these differences could illuminate discrepancies in
the reported effects of performance goals on motivation and per-
formance. The following studies contain both normative and non-
normative versions of performance goals. An example of an ex-
plicitly normative goal would be the following: One of my major
goals in school is to feel that I am more intelligent than other
students. In contrast, the goal item, It is important to me to
validate that I am intelligent, is not explicitly normative.
Sometimes goal items used to measure performance-goal orien-
tation simply ask the participant about wanting to do well on a
task, such as wanting to earn a high grade in a course. For people
who are focused on doing well, negative outcomes do not neces-
sarily indicate a lack of ability (i.e., holding this type of goal does
imply a particular causal attribution for success or failure). We
refer to the goal of wanting to do well on a particular task as an
outcome goal, and it, too, is represented in our studies (e.g., It is
important to me to get good grades in my classes.). A closely
related construct is competence valuation, or the degree to which
a task is perceived to be important (Elliot & McGregor, 2001),
which has been found to relate positively to intrinsic motivation
and performance (Barron & Harackiewicz, 2001). We find this
type of goal particularly interesting, because wanting to do well
can also be an important part of a learning goal framework. In
other words, a person with a learning goal may care very much
about doing well on a task, but perhaps for different reasons (i.e.,
in order to maximize learning, as an indicator of successful learn-
ing, or for instrumental reasons). Later, we address the question of
whether outcome goals are best understood as performance goals.
What Is a Learning Goal? When Is It Helpful?
There is generally less controversy and more agreement with
respect to the nature of learning goals. As noted learning goals,
task goals, and mastery goals have often been regarded as concep-
tually equivalent (Ames, 1992; Linnenbrink & Pintrich, 2000).
Yet, potentially important differences among operationalizations
do exist. For some (Ames, 1992; Elliot & Church, 1997; Elliott &
Dweck, 1988; Harackiewicz et al., 1997; Middleton & Midgley,
1997), a learning goal is an active striving toward development and
growth of competence, and is operationalized by emphasizing the
importance and benefits of learning some new knowledge or skill
to the participant, or by asking participants to indicate the extent to
which learning and developing new skills are major academic
goals. However, the terms task goalsand mastery goalsdo not
put an explicit emphasis on learning; thus, we thought it important
to test the extent to which the desire to learn may be similar or
different from the desire to master challenges. As a result, we
included items measuring two forms of learning goals. An example
of a learning goal without an explicit challenge-mastery compo-
nent is I strive to constantly learn and improve in my courses. An
example of an explicit challenge-mastery item is It is very im-
portant to me to feel that my coursework offers me real
It should be reiterated that, despite the substantial agreement
among researchers with respect to the concept of a learning goal,
the data with respect to the influence of learning goals on moti-
vation and performance are not without inconsistencies. Typically,
those who adopt learning goals are found to engage in deeper,
more self-regulated learning strategies, have higher intrinsic mo-
tivation, and perform better, particularly in the face of challenge or
setbacks (Ames, 1992; Dweck & Leggett, 1988; Kaplan & Midg-
ley, 1997; Pintrich, 2000a; Pintrich & Garcia, 1991; Utman, 1997;
see also Barron & Harackiewicz, 2000). However, recently, sev-
eral studies have failed to find enhanced performance outcomes
resulting from learning goals (although enhanced intrinsic moti-
vation was found; Elliot & Church, 1997; Elliot, McGregor, &
Gable, 1999).
Conditions Under Which Goal Effects Are Tested
The effects of learning and performance goals on motivation
and achievement have been tested under a wide variety of circum-
stanceswith students working on interesting NINA puzzles
(Elliot & Harackiewicz, 1996), performing a concept-formation
task (Elliott & Dweck, 1988), solving math problems (designed to
be highly challenging in one condition; Barron & Harackiewicz,
2001; cf. Middleton & Midgely, 1997), or taking an intermediate-
level psychology course (Elliot & Church, 1997). Importantly,
these tasks may have varied with respect to the degree of difficulty
or challenge encountered by the participant, and the degree to
which performance on the task had importance or meaning to the
participant. We feel that conditions where the degree of difficulty
is substantial for a large number of participants and the outcome is
highly important are more likely to reveal goal effects on motiva-
tion, coping, and achievement, and have tried to use such condi-
tions in the studies reported here.
In summary, there have been major differences in the ways
goals have been operationalized, and it is not surprising that the
data are inconsistent with respect to how and when performance
and learning goals affect motivation and achievement. In the
following studies, we attempted to illuminate these issues. In three
studies, we developed and tested a set of items to tap different
forms of learning and performance goals. In the fourth study, to
gain an initial sense of the patterns associated with each goal type,
we presented students with scenarios depicting important aca-
demic setbacks and examined how the different goals predicted
intrinsic motivation and coping. In the fifth study, the different
goals were used to predict intrinsic motivation, study strategies,
and performance in an important and challenging course.
Study 1
Given the number of goals we hoped to measure and compare
(e.g., ability goals, outcome goals, normative outcome goals, nor-
mative ability goals, learning goals, and challenge-mastery goals),
we wanted to use the fewest possible items to measure each type
of goal while still maintaining high reliability. It was felt that using
relatively few items would minimize the frustration and confusion
participants might experience when required to answer many sim-
ilarly worded items. Thus, 10 items for each of type of goal were
created and carefully tested with 560 participants, and the most
reliable three items were selected for each goal. In three prelimi-
nary studies (Studies 13) reported below, the items used to
measure each type of goal achieved good reliability and validity, as
demonstrated by the relatively high alphas for each group of items,
the high correlation of each group of items with scales assessing
conceptually similar variables, and the high testretest correlations
(the Appendix contains the complete list of items). For each goal
item, participants were asked to rate their agreement on a 7-point
Likert-type scale ranging from 1 (strongly disagree)to7(strongly
A total of 451 participants (218 men and 233 women) recruited from the
Columbia University student population were paid $5 for their participa-
tion. Fifty-seven percent of participants were Caucasian, 9% African
American, 19% Asian, 8% Latino, and 7% were other or unidentified.
Participants were asked to complete a goal inventory containing the
three items for each of the types of goals along with several unrelated
measures. The goal items were presented in random order. Participants in
this initial study (and all subsequently reported studies) read and signed
consent forms that informed them about the procedure, the information that
would be asked of them, and their rights as research participants. They
were reminded that they were free to leave the study at any time without
Exploratory Factor Analysis
Principal-components analysis, using varimax rotation and eig-
envalues greater than 1, yielded four factors (accounting for 24%,
20%, 16%, and 12% of the total variance, respectively). Factor 1
contains all normative items (both normative ability and normative
outcome). Factor 2 contains both the learning and challenge-
mastery items. Factor 3 contains all of the nonnormative outcome
goal items. Factor 4 contains all nonnormative ability goal items.
This analysis was repeated using oblimin rotation, which yielded
nearly identical results (see Table 1).
These analyses revealed that, consistent with our expectations,
learning goals (Factor 2), outcome goals (Factor 3), and ability
goals (Factor 4) are distinct constructs. As we will see, learning
and ability goals are the major classes of predictive goals in
subsequent studies. In addition, participants clearly distinguished
between normative and nonnormative forms of performance goals,
suggesting that this distinction is a meaningful and potentially
important one. Normative goals loaded together (Factor 1), sug-
gesting that participants who endorsed them did not distinguish
strongly between normative outcome and normative ability goals.
Similarly, challenge-mastery goals did not distinguish themselves
reliably from learning goals. Thus, four groups or classes of goals
emerged in this study, and we focus on these groups throughout the
remainder of the article: learning (comprised of both learning and
challenge-mastery items), outcome, ability, and normative (com-
prised of both normative outcome and normative ability items).
Internal Consistency
Although Cronbachs alpha is dependent on the length of the
measure (i.e., number of items in a scale), our goal measures
nonetheless achieve substantial alphas. The alphas for each of the
four subsets of the goal items (ranging from .81 to .92) had an
average of .86, consistent with unidimensionality for each set of
Confirmatory Factor Analysis (CFA)
CFA was conducted on the achievement goal items using
EQS 5.7 (Bentler & Wu, 1995). Solutions were generated on the
basis of maximum-likelihood estimation. Seven models were
tested. For each model, we calculated multiple indices of fit:
chi-square, comparative fit index (CFI), normed fit index (NFI),
nonnormed fit index (NNFI), root-mean-square error of approxi-
mation (RMSEA), and Akaike Information Criterion (AIC), a
comparison statistic for nonhierarchical models. The results from
these analyses indicated that two models provided a good fit to the
data: Model A,
(123, N 451) 490.23, CFI .93, NFI .91,
NNFI .92, RMSEA .08, AIC 244; Model B,
(120, N
451) 395.76, CFI .95, NFI .93; NNFI .94, RMSEA
.07, AIC 155). Although Model B (a six-factor model) does
provide a slightly better fit, Model Aa hierarchical model with
four primary factors (i.e., an ability goal factor, an outcome goal
factor, a normative factor comprised of normative ability and
normative outcome factors, and a learning factor comprised of
learning and challenge-mastery factors)is consistent with the
results from the two principal-components analyses, as well as
with the pattern of item intercorrelations and scale alphas. Thus, a
four-factor goal model, consisting of learning goals, outcome
goals, ability performance goals, and normative performance
goals, received the most consistent support and provided the best
overall fit to the data.
Correlations Among Classes of Goals
All four goal indices were positively correlated. Outcome goals
(wanting to do well) appear to accompany the valuing of any
achievement goals, whether those goals pertain to learning (r
.37, p .001), to validating ones ability (r .53, p .001), or
to outperforming others (r .34, p .001). Learning goals were
positively related to outcome goals, as noted, as well as ability
goals (r .41, p .001) and normative goals (r .17, p .001).
Finally, ability goals and normative goals were strongly correlated
(r .52, p .001). Although it appears that individuals who value
achievement may value many aspects of it, we will see that clearly
distinct and unique patterns are associated with each type of goal.
Table 1
Principal-Component Factor Analysis With Item Loadings
Goal item type Item no.
Varimax rotation
12 3 4
Learning 1 .72 (.74)
2 .70 (.68)
3 .66 (.62) .51 (.44)
Challenge-mastery 1 .84 (.87)
2 .80 (.82)
3 .74 (.75)
Outcome 1 .81 (.79)
2 .74 (.71)
3 .87 (.86)
Ability 1 .50 (.46)
2 .79 (.80)
3 .81 (.83)
Normative outcome 1 .81 (.85)
2 .81 (.86)
3 .83 (.86)
Normative ability 1 .82 (.78)
2 .81 (.77)
3 .85 (.82)
Note. All loadings above .40 are shown. Oblimin rotation values are shown in parentheses.
Analysis by Gender
Tests for mean difference in goal ratings by gender revealed
several significant differences, though the pattern of differences
varied across studies. Because these differences did not replicate
across studies, there is little reason to believe that any were
representative of the general population. It is important to note that
there were no interactive effects of goal and gender in any of the
studies. In other words, performance and learning goals exerted the
same effects on both men and women in each study. Therefore, in
the interest of brevity, gender differences will not be discussed for
each study.
Study 2
A total of 54 participants (23 men, 31 women) recruited from the
Columbia University student population were paid $10 for their
Participants completed the goal items as part of a battery of measures,
and then completed the items again in another battery of measures ex-
actly 2 weeks later.
Correlations between Time 1 and Time 2 ratings were calculated
for each goal. The correlations ranged from .69 to .88, and the
average testretest correlation was .79. Thus, participants scores
were substantially consistent over time.
Study 3
Study 3 was designed to obtain construct validity for the goal
items by relating them to other goal measures. Two commonly
used measures of achievement goal orientation were chosen (i.e.,
Button, Mathieu, & Zajac, 1996, and Elliot & Church, 1997). In
particular, it was important to show that (a) our measure of
learning goals mapped onto other operationalizations of learning
goals, (b) that our measure of normative performance goals was an
accurate representation of how these goals have been measured in
the literature, and (c) that our outcome goals were equally related
to learning and performance goals in other measures, as they had
been in ours. Neither measure taps ability goals as we have defined
Learning and Performance Orientation Scales (Button et al., 1996).
Button et al.s (1996) inventory is composed of two scales (Learning and
Performance), each containing eight items. In general, Button et al.s
learning goal items capture the conceptualization proposed by Dweck and
Elliott (1983)an emphasis on challenge-seeking, use of effort and strat-
egies, and desire to develop and grow. Button et al.s performance items
emphasize wanting to do well and not make mistakes, though there are two
items that involve social comparison and the opinions of others.
Elliot and Church’s (1997) Achievement Goal Scale. The goal orien-
tation scale used by Elliot and his colleagues in their classroom studies
(Elliot & Church, 1997; Elliot et al., 1999) consists of three subscales with
six items each, of which we focused on two: Mastery and Performance
Approach (the third subscale is Performance Avoidance). The Mastery
items emphasize wanting to learn as much as possible and thoroughly
master new material. Performance Approach items emphasize wanting to
do better than others (i.e., normative items).
A total of 87 participants (37 men, 50 women) were recruited from the
Columbia University student community and paid $5 for their participa-
tion. Sixty percent of participants were Caucasian, 22% African American,
13% Asian, and 5% were other or unidentified.
Participants completed our goal items, along with the Learning and
Performance Orientation Scales (Button et al., 1996), and the Achievement
Goals Scale used by Elliot and his colleagues (Elliot & Church, 1997). The
three measures were presented in three different orders across participants.
There were no discernable effects of order.
As expected, the learning goal items were highly positively
correlated with Button et al.s (1996) Learning scale (r .72, p
.001) and Elliot and Churchs (1997) Mastery scale (r .76, p
.001). This suggests that the items are valid indices of a learning
In Study 1, outcome goal items, with their focus on the value of
doing well, were compatible with learning goals, ability goals, and
normative goals. They were also positively correlated with both
Button et al.s (1996) Learning (r .37, p .01) and Performance
(r .45, p .001) Scales, as well as with Elliot and Churchs
(1997) Mastery (r .41, p .001) and Performance Approach
(r .30, p .01) scales. This is further evidence of the hybrid
nature of outcome goals.
Ability goal items were positively correlated with Button et al.s
(1996) Performance Scale (r .45, p .001) and Elliot and
Churchs (1997) Performance Approach scale (r .46, p .001),
but only moderately, because neither of those scales focus on
ability validation.
As predicted, normative goal items were highly correlated with
Elliot and Churchs (1997; normative) Performance Approach
scale (r .83). Ability and outcome goal items were significantly
less correlated with this scale (outcome r .30 vs. normative r
.83, t[85] 4.01, p .001; Ability r .46 vs. Normative r .83,
t[85] 1.98, p .05).
In summary, a comparison of these three measures yielded
evidence for the construct validity of our goal items. High corre-
lations with conceptually similar subscales in the Button et al.
(1996) and Elliot and Church (1997) measures can be taken as
evidence that the items are tapping into the right goal constructs.
Study 4
We believe that it is important to look at goal effects when
individuals experience major setbacks or failure on highly valued
tasks, because it is under these conditions that we would expect
goal effects on motivation, coping, and achievement to be maxi-
mal. Studies 4 and 5 were designed to look at how each of the
different goals we identified predicts indices of intrinsic motiva-
tion, mastery-oriented coping, and performance, after a significant
or sustained difficulty or setback, by means of hypothetical failure
scenarios (Study 4), reports of habitual coping style (Study 4), and
a very challenging premed college course (Study 5).
We also included measurements of some of the affective and
cognitive variables that comprise the psychological processes that
accompany goal pursuit. Much recent achievement goal work pays
little attention to the psychological concomitants of goals: attribu-
tions, beliefs, and contingency of self-worth (Molden & Dweck,
2000). By including these measures, we hoped to capture a richer
motivational picture of performance and learning goal processes.
In Study 4, two scenarios were generated in which the par-
ticipant encounters failure in an important achievement setting
(adapted from Zhao & Dweck, 1997). The use of hypothetical
scenarios was used here as a first step in relating the different goals
to the variety of cognitive, affective, and behavioral variables
involved in coping with difficulty in achievement situations.
Participants in Study 4 also completed a measure of chronic
coping style (COPE; Carver, Scheier, & Weintraub, 1989), so that
we might look at the relationship between goal orientation and
participants own personal history of coping with setbacks. Thus,
the first part of Study 4 asks participants to indicate how they
would respond to a situation if it occurred, and the second part of
Study 4 asks them to reflect on past situations that have actually
A total of 92 participants (40 men, 52 women) were recruited for pay
from the Columbia University community. Sixty-one percent of partici-
pants were Caucasian, 21% African American, 12% Asian, and 6% were
other or unidentified. They received $5 for their participation.
Participants completed the goal items, and then, after a 5-min word-
completion filler task, they received one of two randomly assigned sce-
narios, shown in previous work to elicit motivational differences (see Zhao
& Dweck, 1997). The scenario asked them to read about a failure experi-
ence in a college classroom (either getting a bad grade on an important
essay in a key course or doing poorly on the Graduate Record Examination
when they strongly wished to go to graduate school), and to imagine it
happening to them. These two scenarios were vividly written and were
selected to represent situations that they could easily personally relate to
(i.e., doing poorly on an essay in a course in your major, and doing poorly
on a test in science class). Here is an example:
Imagine that during your second semester at Columbia, you take an
important course in your major, in which students are required to read
their essays out loud to the entire class. This happens several times
throughout the semester. The time comes for the first readings. By the
time its your turn, most of the students have already presented their
essays. All of them did pretty well, and you know that their essays got
good grades from the professor. But when you read your essay to the
class, the professor and the other students dont seem to like your
presentation very much, and later you find out that the grade he gave
you was a C.
Participants were then asked to indicate what they would think, how they
would feel, and how they would behave after the failure by rating their
degree of agreement with a series of statements. These statements include
items assessing loss of intrinsic motivation (e.g., Id probably feel less
interested in the subject), help-seeking (e.g., I would seek help from my
professor or my classmates), planning (e.g., Id start planning how to do
better on the next presentation), and time and energy withdrawal (e.g., I
would devote less time and energy to the class), as well as attributions for
the failure (e.g., I would feel like I wasnt smart enough), loss of
self-worth (e.g., I would feel like a loser), and rumination (e.g., I would
dwell on my failure). Responses were made by circling a number on a
7-point scale ranging from 1 (not at all true of me) to7(very true of me).
After a second 5-min word-completion filler task, participants were
asked to complete the Ways of Coping Scale (COPE; Carver et al., 1989).
This scale measures the ways in which individuals have coped with
difficulties when they have arisen. Subscales include Active Cop-
ing, Planning, Positive Reinterpretation, Denial, and Behavioral
For each of the analyses conducted, scenario version (1 or 2)
was entered as a predictor, and no effect for scenario version was
found. Therefore, all analyses reported were conducted collapsing
across scenario version. Each of the four goal types (learning,
outcome, ability, and normative) was entered as a predictor in a
series of simultaneous regressions that included all two-way inter-
actions. There were no significant two-way interactions, so these
terms were dropped in subsequent analyses. Thus, the effects of
each goal on the variables of interest control for any effects of the
other three classes of goals. In this way, we could determine what,
if any, were the unique effects of each class of goal on our
achievement variables.
Intrinsic Motivation
Table 2 depicts the unique relationship between each type of
goal and an index of loss of intrinsic motivation, created by adding
together responses from the following three items (
.89): Id
probably feel less interested in the subject,”“I probably wouldnt
enjoy the course as much as before, and I wouldnt really be
excited about the course anymore.
As can be seen, learning goals were negatively related to de-
creases in intrinsic motivation, whereas outcome and ability goals
were significantly correlated with decreases in intrinsic motiva-
tion. Of interest, normative goals did not predict loss of intrinsic
motivation. This finding is worth noting, in that the program of
research that has most consistently found that approach forms of
performance goals do not negatively influence intrinsic motivation
has used a normative definition of performance goal (e.g., Elliot &
Church, 1997). Also, Epstein and Harackiewicz (1992) have found
that students high in achievement motivation who were assigned
competitive goals (which are inherently normative) experienced
increased interest in a task when they were given a failure expec-
tancy. This finding suggests that competitive strivings may buffer
individuals when they experience difficulty or failure, in ways that
ability-focused strivings do not.
Behavioral Coping
Endorsement of several possible behavioral responses by goal
type is also displayed in Table 2. Consistent with the maintenance
of intrinsic motivation, learning goals predicted planning (one
item: Id start planning how to do better on the next presenta-
tion), and negatively predicted withdrawal of time and energy
(one item: I would devote less time and energy to the class).
Ability goals, in contrast, positively predicted withdrawal of time
and energy.
Outcome goals were the only goals that were positively related
to help-seeking (one item: I would seek help from my professor
or my classmates). Help-seeking may be perceived as a good way
to obtain the good grades that those who endorse outcome goals
clearly value.
Turning to the psychological processes that accompany goal
pursuit, learning goals (
.56, p .001) were predictive of
effort-based attributions for failure (one item: I think that if I
work harder, I can do better), whereas ability goals (
.22, p
.05) and outcome goals (
.36, p .01), in contrast, were
predictive of ability-based attributions (one item: I feel like Im
just not good at this subject). Learning goals were negatively
related to making ability attributions for poor performance (
.37, p .01).
These results are consistent with prior research, which found
attributions to low ability to be associated with drops in intrinsic
motivation and helplessness, whereas attributions to effort were
associated with intrinsic motivation maintenance and mastery-
oriented coping (e.g., Mueller & Dweck, 1998).
Again, normative goals were not reliable predictors of negative
ability attributions. Taken together with the finding that these goals
do not reliably predict loss of intrinsic motivation, the data begin
to suggest that normative performance goals may be a hardier form
of performance goal (i.e., one that does not tend to lead to help-
less forms of coping and behavior). This is again consistent with
Elliot and colleagues findings (see Elliot & Church, 1997; Elliot
et al., 1999) that (normative) performance approach goals do not
lead to lower motivation and performance.
Loss of Self-Worth
Loss of self-worth is akin to a negative ability attribution, but it
is more global. It, too, can often accompany helpless motivational
and behavioral responses to a setback (e.g., Covington, 1992;
Crocker & Wolfe, 2001). A composite index of self-worth loss was
created by adding together responses from the following three
items: I would feel like a loser,”“I would feel like a failure, and
Id think less of myself as a person (
.84). Consistent with
results thus far, ability (
.56, p .001) goals were positively
correlated with loss of self-worth.
The tendency to ruminate on ones setbacks has been associated
with helplessness. A composite index of ruminating and dwelling
on the failure was created by adding together responses from the
following two items: I would dwell on how poorly I did and I
would replay it all over and over again in my mind (
Rumination was fairly strongly related to ability goals (
p .001). Thus, those goals that tend to lead to ability attributions
and negative self-evaluation also predict dwelling on the negative
outcome and its meaning.
The results from the hypothetical failure scenarios revealed a
consistent pattern among the motivational and coping variables.
Learning goals predicted active, engaged responding, whereas
ability goals predicted self-denigration and withdrawal. Outcome
goals were associated with a hybrid response pattern (i.e., low
ability attributions and decreased intrinsic motivation as well as
help-seeking). Finally, normative goals were not reliable predic-
tors of mastery-oriented or helpless responding.
Chronic Coping Style
We now turn to the question of whether different goals predict
different reported histories of coping with setbacks in past achieve-
ment situations. Different styles of chronic coping were measured
by the Ways of Coping Scale (Carver et al., 1989), which asks
participants to indicate the extent to which they have typically
engaged in various coping strategies.
Consistent with the responses to the failure scenarios, learning
goals predicted active coping (
.38, p .01) and planning
.33, p .01). They were also predictive of positive reinter-
pretation of a setback (
.30, p .05) and negatively related to
denial (
⫽⫺.36, p .01), behavioral disengagement (
p .01), and mental disengagement (
⫽⫺.28, p .05).
Ability goals negatively predicted positive reinterpretation of a
setback (
⫽⫺.30, p .05). Of interest, normative goals were
significant predictors of denial after a setback (
.25, p .05)
and behavioral disengagement (
.28, p .01). The finding for
denial perhaps suggests that competitive striving might keep indi-
Table 2
Goals and Responses to Failure
Loss of intrinsic
Withdrawal of time
and effort Help-seeking Planning
Learning .39*** .40*** .17 .57***
Outcome .29** .00 .36** .03
Ability .40*** .32** .02 .02
Normative .11 .02 .16 .16
Note. Values are standardized regression coefficients.
** p .01. *** p .001.
viduals from recognizing a poor performance when they produce
one. This may provide some explanation for the consistent finding
that normative goals did not predict negative cognitive, affective,
and behavioral responding to a hypothetical setback (e.g., loss of
intrinsic motivation, low ability attributions, loss of self-worth,
rumination) as strongly or consistently as nonnormative ability
In summary, learning goals were associated with active coping,
and a wide range of positive, mastery-oriented indicators. Learning
goals appear to be a powerful predictor of behaviors that will
preserve intrinsic motivation and performance in the face of dif-
ficulty. In contrast, ability goals were associated with a loss of
motivation and common indices of helplessness. Outcome goals
(which are related to both learning goals and ability goals) also
predicted a loss of motivation and low ability attributions for
failure, but predicted proactive behaviors as well (e.g., help-
seeking). Taken together, these results suggest that valuing doing
well is not in itself a good predictor of responses to failurerather,
the goals that accompany valuing doing well (learning or validat-
ing ability) seem responsible for much of the action. Normative
goals were not among the performance goals that related strongly
or consistently to the variables measured, suggesting that under
some circumstances, competitive performance goal items may not
predict maladaptive cognitions, affect, or coping when other types
of performance goals (i.e., ability goals) do.
Study 5
Study 5 differed from Study 4 in several ways. First, the goal
items were used to predict motivation and performance in a real-
world setting, specifically for freshman and sophomore under-
graduates taking an important and often career-defining course.
Study 5 also differed from many past course-taking studies in the
level of sustained challenge or difficulty encountered by partici-
pants (and, as explained below, in our special attention to students
who encountered successive setbacks over the course of the se-
mester). For this reason, we would expect to see more facilitative
effects of learning goals on motivation and performance, as well as
the debilitating effects of performance goals.
Aside from being a real-world study, Study 5 differed from
Study 4 in another important way. Study 4 presented students with
a fait accomplia defined failureand therefore, perhaps did not
allow us to see the potentially beneficial effects of performance
goals for people experiencing challenge but not failure. Study 5
allowed us to monitor students throughout the semester, by looking
in on students as they began this new, important, and challenging
course. Here we might find that for students who are doing well,
ability goals will provide a boost over time, whereas for students
who are encountering difficulty, ability goals will predict further
impairment. In other words, Study 5 allowed us to see goal effects
as they played out over timeboth their facilitative effects and
their detrimental effects.
Most potential premed, engineering, and science majors at Co-
lumbia University enroll in General Chemistry in the Fall of their
freshman year. The permission and support of the Columbia Uni-
versity Provost, Deans of the College of Arts and Sciences, and
General Chemistry instructors were granted to conduct an inten-
sive study of these students throughout the semester. Surveys
tracked students intrinsic motivation and performance at several
points throughout the semester, and grades were obtained from the
Chemistry Department with permission of the students.
Participants were 85% freshmen, 50% female and 50% male. The
number of participants in each wave of the study varied between 78 and
128, depending on class/recitation attendance. In the largest sample, par-
ticipants were 59% Caucasian, 7% African American, 26% Asian, and 8%
Latino. The average grade on any exam in this course was a C, suggest-
ing that this was a course in which many participants experienced difficulty
or setbacks. For the smaller samples, we tested to ensure that the partici-
pants were entirely representative of the larger sample and that no system-
atic attrition had occurred. Thus, although attendance (and hence partici-
pation in the study) varied over the waves of the study, no significant
differences among samples at the different time points were found in terms
of gender, ethnicity, goal endorsement, or grades.
General Chemistry is a lecture course that is structured around three
midterms and a final exam. Data were collected from participants at four
points during the semester: twice 23 weeks before the first midterm, once
immediately after the first midterm, and again 2 weeks before the final
exam. Data were collected in the last 1520 min of class or recitation. The
measures were presented (along with other measures in a packet of ques-
tionnaires) in the following sequence:
Session 1 (23 weeks before first midterm): goal items, demographic
Session 2 (1 week after Session 1): intrinsic motivation, perception of
chemistry ability
Session 3 ( after first midterm): general study strategies (from Elliot
et al., 1999)
Session 4 (before final exam): intrinsic motivation
Consistent with the results of Study 4, we predicted that learning goals
would be positively related to intrinsic motivation and grades (despite the
lack of the influence of learning goals on performance found in previous
studies in what may have been less academically strenuous or personally
relevant contexts). We expected ability goals to be associated with lower
performance after multiple setbacks, as suggested by Dweck and Leggett
(1988), but not necessarily with lower performance overall. In fact, we
expected that students who were doing well in the course might experience
a boost from holding strong ability goals.
Perceived Ability
If different types of goals are systematically related to different
levels of perceived ability, then it is possible that the effects of
goals obtained in this study are simply due to this confounding
factor. To rule out this explanation, perceived level of ability in
chemistry was measured at the beginning of the course (one item:
Compared to other students in this course, please rate your ability
in chemistry on a 10-point scale ranging from top 10% to lower
10%). Perception of ability in chemistry was related to overall
course grade (r .27, p .01). It was also related to intrinsic
motivation at the beginning (r .26, p .01) and at the end (r
.31, p .05) of the course.
Correlations between perceived ability in chemistry and goal
type revealed that normative goals were significantly positively
related to perceived ability (r .38, p .001). In other words,
people with normative goals tended to believe that their ability was
high relative to others. This could help account for the resilience
(or, better put, lack of negative consequences) associated with
normative goals in Study 4. Outcome goals were also positively
related to perceived ability (r .21, p .05), whereas learning
and ability goals were unrelated to perceptions of chemistry
Intrinsic Motivation
In a set of linear regressions, we looked at the relationship
between goal type and intrinsic motivation, measured by enjoy-
ment of and interest in the course (two items,
.88). These data
were collected at the beginning of the course and again before the
final exam. The regressions controlled for perceived ability
(though an essentially identical pattern emerged when perceived
ability was not included in the analysis) and for the effects of each
of the other three goal indices. We also included gender in our
initial analyses, but gender did not predict intrinsic motivation and
was dropped from subsequent analyses predicting intrinsic
In this highly difficult course, learning goals predicted higher
intrinsic motivation at the beginning (
.23), t(128) 2.34, p
.05, and at the end (
.22), t(78) 2.02, p .05, of the course.
This is consistent with the findings of Elliot, Harackiewicz, and
their colleagues (Elliot & Church, 1997; Elliot & McGregor, 1998;
Harackiewicz & Elliot, 1998) that learning goals positively predict
intrinsic motivation. There were no other significant predictors of
intrinsic motivation.
We looked at the relationship between each goal type and
students total grades, controlling for Scholastic Aptitude Test
(SAT) score, perceived ability in chemistry, number of prior
courses in chemistry, and gender, as well as the effects of other
goal indices. Gender predicted total grade (
⫽⫺.19), t(126)
3.03, p .01, such that men tended to have higher grades than
women. In addition, we looked at the extent to which each goal
type predicted improvement from Exam 1 to the final exam,
controlling for performance on Exam 1 (the interaction of each
goal with performance on Exam 1 was also included as a
Total Course Grade
Consistent with the pattern of effort attribution and mastery-
oriented coping associated with learning goals in Study 4, learning
goals positively predicted course grade (
.20), t(120) 2.42,
p .05. No other goals were significant predictors of course
grade. The fact that learning goals emerged as a significant pre-
dictor of performance supplements the findings of Elliot, Harac-
kiewicz, and their colleagues (e.g., Elliot & Church, 1997;
Harackiewicz et al., 1997), who have suggested that performance
goals, and not learning goals, predict course performance. This
result could imply that when a course involves sustained challenge,
learning goals do positively affect course performance.
Improvement in Grade From Exam 1 to Final Exam
Learning goals also significantly predicted grade improvement
.25), t(122) 2.94, p .01, and were the only goals to do
Final Exam Grade
Earlier, we had predicted that ability goals would have a nega-
tive effect on performance for those students who had experienced
prolonged setbacks. To address this question, we looked at how
goals predicted performance on the final exam for those students
who had performed poorly throughout the semester. We simulta-
neously regressed each goal type, the average of students Exam 1,
2, and 3 grades (our index of past performance), and the interaction
of goal type with average exam grades, onto final exam grades. We
predicted a significant interaction for ability goals, such that stu-
dents who had done poorly throughout the semester (i.e., those
with low average exam grades) would suffer for holding strong
ability goals, whereas those who had done well throughout the
semester might receive a boost on the final.
As predicted, there was a significant interaction between ability
goals and average grade on Exams 1, 2, and 3 (
t(71) 2.23, p .05. Figure 1 illustrates this effect. We have
plotted data for participants who were either one standard devia-
tion above or below the mean endorsement of ability goals (see
Jaccard, Turrisi, & Wan, 1990). Participants were further separated
into high- and low-course performance groups (based on a median
split of performance on exams prior to final). As shown, partici-
pants with low prefinal grades score lower on the final exam if
they are high rather than low in ability goals. In contrast, partici-
pants with higher prefinal grades earn better scores on the final
Figure 1. Final exam grade predicted by past performance and ability
exam if they are high rather than low in ability goals. This finding
suggests that when setbacks are repeated, ability goals predict poor
performance, but may indeed provide a boost when an individual
is doing well (see Elliott & Dweck, 1988).
Study Strategies
To further understand the differences we found in performance,
we looked at three study strategies (deep processing, surface
processing, and disorganized processing) that were adapted from a
scale used by Elliot et al. (1999), and were assessed immediately
after students took the first exam. The tendency to engage in deep
processing was significantly correlated with grade in the course
(r .29, p .01). Disorganized processing was negatively related
to course grade (r ⫽⫺.36, p .001). Surface processing was
unrelated to course grade (r .08, ns).
Outcome goals predicted surface processing of course material
(r .29, p .01), and learning goals predicted deeper processing
of course material (r .31, p .01). In contrast, normative goals
were negatively related to deep processing (r ⫽⫺.21, p .05),
suggesting that one drawback associated with a competitive goal
might be the absence of deep analysis of issues or material.
Mediational Analyses for Learning Goal Effects on
Course Grade
The significant correlation between learning goals and deep
processing (r .31), as well as the correlation between deep
processing and course grade (r .29), suggested processing style
as a possible mediator of the effect of learning goals on course
grade. Consistent with this hypothesis, the relationship between
learning goals and course grade (controlling as we had earlier for
SAT score, perceived ability in chemistry, past chemistry course
experience, and gender), when controlling for extent of deep
processing, is not significant (
⫽⫺.06, ns), whereas deep pro-
cessing remains a significant predictor of course grade (
p .05; see Table 3 and Figure 2).
General Discussion
Items measuring different types of performance and learning
goals were created and used in five studies to help to shed light on
several important, unresolved issues in current achievement goal
research. Studies 13 yielded evidence for four types of goals:
learning goals, outcome goals (wanting to do well), ability-linked
performance goals, and normative performance goals. Individuals
responses in these three preliminary studies and two more com-
prehensive studies suggested answers to a number of the funda-
mental questions posed in the literature.
First, are there different types of learning goals? What is the
relationship of learning goals to intrinsic motivation and perfor-
mance? We looked at two types of learning goals: striving to learn
and develop versus seeking to master challenges. These two goals
were highly correlated and loaded together in two principal-
components analyses, so the items were combined into a single
learning goal measure. Although we did not find evidence in our
studies for separating these two types of learning goals, they may
still differ importantly from the task goals found in past research
that are often operationalized in ways that contain neither striving
to learn nor challenge-seeking.
Studies 4 and 5 provided evidence for the positive effects of
learning goals on both intrinsic motivation and performance, con-
sistent with the early research on achievement goals (see, e.g.,
Ames, 1992; Ames & Archer, 1988; Butler, 1987; Dweck & Leg-
gett, 1988; Elliott & Dweck, 1988; Meece et al., 1988; Nicholls,
1984). Individuals who endorse learning goals should be more
likely to see negative outcomes as information about ways to
improve the learning process, rather than as indicators of stable
low ability. As expected, in response to a major hypothetical
failure (Study 4), learning goals predicted a wide range of positive,
mastery-oriented indicatorsincluding sustained intrinsic motiva-
tion, planning, and persistence. Participants with strong learning
goals also reported a history of having used more mastery-oriented
coping methods (e.g., active coping, planning) in response to past
In Study 5, in an important and difficult college course, learning
goals predicted better processing of course material, higher intrin-
sic motivation, higher grades, and greater improvement over time.
Further analysis suggested that the relationship between learning
goals and course grades was mediated by the tendency to engage
in deeper processing of course material. The impact of learning
goals on performance may be seen chiefly when a high degree of
challenge is present, when a task is personally important, or when
the processing of complex, difficult material is necessary. A po-
tentially important topic for future research is the role that these
factors play in the presence or absence of learning-goal effects on
Turning to other questions posed earlier, is wanting to do well
different from wanting to prove your ability? When might perfor-
Table 3
Summary of Learning and Ability Goal Effects From Studies 4 and 5
Goal Study 4 Study 5
Learning No decrease in intrinsic motivation
Less time and effort withdrawal
Effort attributions
Seeking positive reinterpretation and growth
Higher intrinsic motivation at beginning and
end of course
Higher grades
Greater improvement over time
Deeper processing
Ability Lower intrinsic motivation Lower grades after repeated poor performance
Loss of self-worth Higher grades after repeated good performance
Low ability attributions
Time and effort withdrawal
mance goals predict vulnerability, and when might they prove
beneficial to intrinsic motivation and/or performance? Individuals
who endorse ability goals (i.e., seek to validate their ability) should
be more likely to see negative outcomes as indicative of a lack of
ability. Consistent with this prediction, ability goals were associ-
ated with common indices of helplessness after a significant hy-
pothetical failure in Study 4. These goals predicted attributions to
low ability, loss of self-worth, rumination about the setback, and
loss of intrinsic motivation. In Study 5, consistent with the results
of Study 4, after multiple setbacks, ability goals predicted lower
grades. Thus, ability goals tend to predict a pattern of negative
affect and cognition, as well as poorer subsequent performance,
after a significant setback or a series of setbacks. These findings
are also consistent with the early work on achievement goals
(Ames & Archer, 1988; Butler, 1993; Elliott & Dweck, 1988;
Jagacinski & Nicholls, 1987; Meece et al., 1988; see also Midgley,
Kaplan, & Middleton, 2001). However, ability goals do not appear
to have negative effects on performance when one is still in the
running(i.e., when success is still possible), or when one is doing
well, and may in these cases sometimes even boost performance
because so much is on the line.
Why do the negative effects of ability goals occur? Dweck and
Leggett (1988) suggested several potential cognitive and affective
mechanisms of debilitation for individuals who hold ability goals
in the face of difficulty. These include the loss of belief in the
efficacy of effort (i.e., My ability is so low, no amount of effort
could help me), defensive withdrawal of effort (either as a form
of self-handicapping or as a response to the belief that the need to
put in effort confirms that one has low ability), and interference of
negative affect with concentration or test performance. Another
possibility is that students with ability goals may withdraw effort
strategically when they are doing poorly to redirect the resources
to courses where they have a better chance at getting a good grade.
Although these data do not test specifically for this possibility, the
pattern of negative attributions, rumination, and loss of self-worth
associated with ability goals suggest that withdrawal is not a solely
cool-headed strategic process.
Outcome goals had surprisingly few effects. Although corre-
lated with many key outcomes, these effects were almost always
due to the association of outcome goals with either learning goals
(e.g., for active coping and effort attributions) or ability goals (e.g.,
for loss of self worth and rumination). These effects did not
survive simultaneous regression analyses that controlled for the
influence of learning, ability, and normative goals. Taken together,
these results suggest that those researchers interested in studying
the unique effects of performance goals would do better not to
operationalize them this way, as outcome goals (wanting to do
well) can clearly be as much a part of a learning framework as a
performance framework. In fact, doing well can be a means of
assessing the acquisition and mastery of new skills and knowledge
or of demonstrating ability.
Finally, do normative and nonnormative performance goals
produce different effects? Unlike (nonnormative) ability goals,
normative performance goals did not predict any of the affective,
cognitive, or behavioral variables measured in Study 4, with the
exception of the tendency on the COPE scale (Carver et al., 1989)
to report engaging in denial and behavioral disengagement after
experiencing an academic setback. In other words, wanting to
outperform others might lead you to be reluctant to perceive your
performance as a failure. In Study 5, normative goals, unlike
ability goals, did not predict vulnerable performance, and in fact,
were associated with higher levels of perceived ability. As men-
tioned earlier, the absence of a relationship between competitive
goals and helplessness is worth noting, in that those researchers
who have most consistently found that performance goals do not
negatively influence intrinsic motivation and performance have
used a normative definition of performance goal (e.g., Elliot &
Church, 1997). Further research is warranted to explore the roles
that perceived ability and denial may play in this protective func-
tion. Moreover, it is striking that although deep processing medi-
ates the beneficial effects of learning goals on grades, the negative
relationship between normative goals and deep processing did not
seem to predict poorer grades. If the lower level of deep processing
was not a hindrance in this setting, it is very likely that competitive
zeal could have positive effects in the many settings in which deep
processing is not required (Kanfer & Ackerman, 2000).
Because ability performance goals and normative performance
goals appear to behave so differently, it would seem important for
researchers to include both types of performance goals in future
studies. In this way, we could continue to gain knowledge about
when, why, and for whom each has costs and benefits.
It should be noted that there are several differences between
these studies and many past studies of goal effects. First, the
present studies used a measure of general goal orientation (i.e., the
extent to which students typically felt oriented toward particular
goals in their courses), whereas many past studies have used goal
inventories that were specific to the task at hand, or to the course
the student was currently taking. Although it is not certain how this
difference might have affected our results and their interpretation,
the field of achievement motivation might benefit from research
that addressed differences in general versus specific goal measure-
ment. Next, our participants, attending a highly selective univer-
sity, may have differed somewhat from the typical student in ways
that could increase or decrease the impact of particular goals. Also,
somewhat larger sample sizes in some other studies may have
yielded significant effects for certain performance goals that were
not significant in our studies. Finally, as noted above, the course
that our participants were enrolled in (Study 5) appeared to require
deep processing in order to do well, and it is possible that this
factor heightened the impact of learning goals. Nonetheless, our
findings make sense both in view of much previous research and
in view of the different meanings that various goals have for the
Indeed, in this article we have taken great care to consider the
meaning that particular goals may have for the individual and to
Figure 2. Processing style mediates the effects of learning goals on
course grade. Values are standardized regression coefficients.
ized regression coefficient controlling for Scholastic Aptitude Test, per-
ceived chemistry ability, past chemistry course experience, and gender.
*p .05. **p .01.
consider goal effects in that light. For example, in addressing the
effects of ability-linked goals on behavior or performance, we
pointed to the inferences that students with ability-linked goals
draw from setbacks. In addressing the effects of outcome goals, we
noted that wanting to do well, far from being a pure performance
goal, may be equally linked to learning and performance goals. In
thinking about learning goals, we stressed the element of active
striving rather than a simple focus on the task or the absence of
performance goal concerns. Thus, for each goal type, we tried to
spell out the impact it might have in the face of achievement
outcomes and why. We hope that our findings have shown the
importance of conceptualizing the psychological processes that
accompany different types of goals, and of matching operational-
izations to these conceptions. When thought of in this way, it
becomes clearer when and why different goalseven ones that
have typically been classified under the same namewill have
different effects.
In conclusion, we have found evidence to suggest that a careful
examination of different types of performance and learning goals
can indeed begin to clarify current controversies in the field. These
studies have shown that learning goals do exert a positive influ-
ence on both intrinsic motivation and performance when individ-
uals encounter prolonged challenge or setbacks. In addition, al-
though performance goals that are focused on validating ability
can have beneficial effects on performance when individuals are
meeting with success, these same goals can predict impaired
motivation and performance after setbacks.
Ames, C. (1992). Classrooms: Goals, structures, and student motivation.
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Achievement Goal Inventory Items
Outcome Goal Items (
It is very important to me to do well in my courses.
I really want to get good grades in my classes.
A major goal I have in my courses is to perform really well.
Ability Goal Items (
It is important to me to confirm my intelligence through my schoolwork.
In school I am focused on demonstrating my intellectual ability.
One of my important goals is to validate my intelligence through my
Normative Goal Items (
Normative Outcome
It is very important to me to do well in my courses compared to others.
I try to do better in my classes than other students.
A major goal I have in my courses is to get higher grades than the other
Normative Ability
It is very important to me to confirm that I am more intelligent than other
When I take a course in school, it is very important for me to validate
that I am smarter than other students.
In school I am focused on demonstrating that I am smarter than other
Learning Goal Items (
I strive to constantly learn and improve in my courses.
In school I am always seeking opportunities to develop new skills and
acquire new knowledge.
In my classes I focus on developing my abilities and acquiring new ones.
I seek out courses that I will find challenging.
I really enjoy facing challenges, and I seek out opportunities to do so in
my courses.
It is very important to me to feel that my coursework offers me real
Received February 6, 2002
Revision received January 16, 2003
Accepted January 21, 2003
... Feedback utilization could also be influenced by a student's achievement motivation. Achievement goals emerge as learning and performance orientation towards a task and are strong predictors of academic performance (Grant and Dweck, 2003;Huang, 2012). Performance orientation is identified by outcome goals (wanting to perform really well), ability goals (demonstrate high abilities), normative ability goals (confirm superiority) and normative outcome goals (outperform others; Grant and Dweck, 2003). ...
... Achievement goals emerge as learning and performance orientation towards a task and are strong predictors of academic performance (Grant and Dweck, 2003;Huang, 2012). Performance orientation is identified by outcome goals (wanting to perform really well), ability goals (demonstrate high abilities), normative ability goals (confirm superiority) and normative outcome goals (outperform others; Grant and Dweck, 2003). In contrast, learning orientation is identified by learning goals (acquiring new skills) and challenge-mastery goals (seeking challenges; Grant and Dweck, 2003). ...
... Performance orientation is identified by outcome goals (wanting to perform really well), ability goals (demonstrate high abilities), normative ability goals (confirm superiority) and normative outcome goals (outperform others; Grant and Dweck, 2003). In contrast, learning orientation is identified by learning goals (acquiring new skills) and challenge-mastery goals (seeking challenges; Grant and Dweck, 2003). Learning goals are associated with higher intrinsic motivation and greater academic improvement over time (Rawsthorne and Elliot, 1999;Utman, 1997). ...
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Initiating effective feedback processes is a major goal in university teaching. However, systematic investigations of structural feedback elements making instructor feedback economic, concise, motivating and beneficial for learning are still scarce. In our study, we compare two feedback modes with respect to learning gains and changes in self-efficacy in a quasi-experimental pre-post design. Participants ( N = 75 first-year students) received either scoresheet or textual instructor feedback on four individual assignments during a seminar. Outcome variables were knowledge gain, change in self-efficacy and changes in metacognitive monitoring. After the semester, we observed substantial knowledge gains for both feedback groups with only small advantages for scoresheet feedback. In contrast, self-efficacy was relatively stable across the semester and was not influenced by feedback mode. Achievement motivation measures normative ability and challenge-mastery goal orientation did not moderate the observed relationships but influenced knowledge gain and change in self-efficacy directly. Changes in metacognitive monitoring did not depend on feedback mode. Taken together, our data suggest that scoresheet and textual feedback conveying identical feedback content have comparable effects on achievement and self-evaluation measures. For university settings, scoresheets can be recommended as parsimonious feedback tools.
... Fundamentally distinguished are mastery-based goals (focused on doing tasks correctly and on the development of competence) and performance-based goals (focused on one's performance relative to others and as perceived by others). Moreover, most Butler, 2012;Elliot, 2005;Elliot et al., 2011;Grant & Dweck, 2003;Hulleman et al., 2010; see and the temporal associations with burnout/engagement as well as teaching and research performance. researchers agree that goals can be characterized by an approach or an avoidance goal valence (i.e., whether desired end-states are sought to be approached, or undesired end-states are sought to be avoided; Murayama et al., 2011). ...
... researchers agree that goals can be characterized by an approach or an avoidance goal valence (i.e., whether desired end-states are sought to be approached, or undesired end-states are sought to be avoided; Murayama et al., 2011). Furthermore, researchers have suggested that a finer differentiation and a disentanglement, based on the content of mastery-and performance-based goals, is necessary (Brophy, 2005;Elliot, 2005;Grant & Dweck, 2003;Hulleman et al., 2010). Specifically, performance goals are distinguished (see Elliot, 1999Elliot, , 2005Hulleman et al., 2010;Lee & Bong, 2016;Senko & Dawson, 2017;Urdan & Mestas, 2006) based on an appearance component (striving to be perceived as competent or not to be perceived as incompetent, irrespective of personal performance) and a normative component (striving to be more competent than others or not worse than others regarding actual performance). ...
... In such a context, primarily hard "facts" often matter (such as third-party funding or the amount of publications). This extends prior research on appearance and normative aspects representing different aspects of performance goals that can entail different effects (Brophy, 2005;Elliot, 2005;Grant & Dweck, 2003;Hulleman et al., 2010;see Daumiller et al., 2019). Our findings imply that, depending on the surrounding contexts and the affordances of performance, these two orientations may be differently relevant for performance through the distinct motivational systems that they span up. ...
Motivation plays a central role in faculty members’ professional lives—with achievement goals having been found to have important links with their burnout/engagement and performance. However, the few studies investigating these links were cross-sectional and considered only one of the two equally important work domains of faculty members. In the present research, we analyze the temporal relationships between achievement goals and burnout/engagement as well as performance and investigate domain specificity of goal pursuit by considering goals for teaching and for research. We conducted a longitudinal study (4 measurement points across two years) including 681 German faculty members. Multivariate Latent Change Score modeling attested that in both domains, mastery-approach goals were positively related to subsequent development of performance, while performance was also positively related to subsequent development of mastery goals, creating a double positive loop. Performance goals and work-avoidance goals were differentially associated with performance in both domains, indicating that the effects of goals can be bound to different contextual features. For overall burnout/engagement, our results implied that primarily research goals mattered for its development (with performance-avoidance and work-avoidance goals being risk factors), while high burnout levels were associated with subsequent reduction of adaptive mastery-approach goals in both domains. This highlights the relevance of achievement goals for burnout/engagement and performance of faculty and illuminates their complex temporal dynamics that can also meaningfully inform achievement goal research in other contexts.
... Further, the feedback and framing of learning activities children receive from teachers determines whether they will continue to pursue those challenges, especially when they become difficult (Kamins & Dweck, 1999). Therefore, teachers should praise the process rather than the outcome (Dweck, 2003). Additionally, teachers should offer incentives when it is necessary or delay the feedback to allow the child an opportunity to continue trying (Waldman-Levi & Erez, 2014). ...
... Additionally, teachers should offer incentives when it is necessary or delay the feedback to allow the child an opportunity to continue trying (Waldman-Levi & Erez, 2014). Instead, harness learners' natural curiosity and interest to achieve a particular realistic learning goal rather than a reward (Grant & Dweck, 2003). Children who have experienced demographic risks also benefit from a warm and quality teacher-child relationship (e.g., Hamre & Pianta, 2005;Meehan et al., 2003). ...
The overall goal of this study is to enhance school readiness assessment in Kenya by developing an easy-to-use tablet-based android app that can support teachers and learners during the assessment of Pre-academic skills, Mastery Motivation (MM) and Executive Functions (EF) in the Kenyan context. We operationalised MM and EF as components of Approaches to Learning (ATL): one of the poorly assessed domains of school readiness. This research was based on the theory of ATL and followed a non-experimental longitudinal research design. One study was a Scoping Review that identified the gap in the literature in the assessment of School Readiness domains using game-like apps. This study formed the basis for developing Finding Out Children's Unique Strengths (FOCUS) app for Kenya following Education Design Research Approach. Two studies tested and evaluated the psychometric properties of the FOCUS app in the Kenyan context. Another two empirical studies focused on adapting the Preschool Dimension of Mastery Questionnaire 18 (DMQ 18) and the Childhood Executive Functioning (CHEXI) to complement the assessment of MM and EF, respectively. In addition, one study addressed the role played by MM and EF on school academic performance. A total of 40 teachers, 497 preschool and 535 grade 1 children were involved in this study. Both parametric and non-parametric statistical analyses were used to analyse the generated data. The FOCUS app, CHEXI and DMQ 18 fit well with the data and exhibited strong psychometric properties, thus being suitable for the Kenyan context. Furthermore, both MM and EF were directly and indirectly, involved in grade one children's academic performance. FOCUS app tasks, pre-academic skills, and number and letter search tasks at preprimary II strongly predicted preschool and grade one academic performance. MM assessed using the FOCUS app as a better predictor of academic performance than the DMQ 18. Interventions to improve MM and EF promise to enhance School Readiness in the Kenyan context. The FOCUS app can greatly complement Kenya School Readiness Test to give teachers and parents a broader spectrum to make correct decisions concerning the child.
... From this perspective, students' activity is intrinsically motivated. Students consider learning as their goal to be willing to challenge themselves and persist in the face of difficulty (Dweck and Leggett 1988;Grant and Dweck 2003). Students who have SN Soc Sci (2022) 2:201 201 Page 18 of 23 the propensity to engage in activities due to genuine interest (an autonomous personality style) should be more likely to develop a HP . ...
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Personality variables contribute to the development of passion for studies leading undergraduates to different affective experiences. Academic hardiness, an affective personality trait, may have effect on undergraduates’ passion for studies. The purpose of the study (which uses a quantitative methodological approach) was twofold: (a) to examine the psychometric properties of Passion scale in Greek undergraduates and (b) to investigate the role of Academic Hardiness 3Cs in the Harmonious (HP) and Obsessive (OP) passion. A convenience sample of 293 undergraduates completed the following scales: (a) Passion scale, (b) Oxford Happiness Questionnaire, (c) The Positive and Negative Affect schedule, and (d) The revised Academic Hardiness scale. Initially, results from exploratory and confirmatory factor analyses provided support for the two-factor passion scale. Moreover, results from SEM analyses revealed that control and challenge were positively related to HP, whereas commitment was positively related to both HP and OP. A negative relationship was found between control and OP. OP was positively related with negative affect, which, in turn, was negatively related with undergraduates’ happiness. On the contrary, HP was positively related with positive affect, which, in turn, was positively related with happiness. Findings of the study are discussed, focusing on the adaptive nature of academic hardiness and harmonious passion in academic settings.
... The link between the growth mindset and motivation for academic success has been well reported in the literature. [5,6] Research has shown that those who demonstrate a fixed mindset are often more concerned with looking intelligent that they often avoid difficult learning experiences for the fear of looking foolish. [7] In congruence, students who presented with fixed mindsets were more focused on unhealthy competition with peers, proving their competence, and were noted to avoid making mistakes. ...
... These views encompass beliefs that some people are "STEM people" and some are not, and that these abilities are conferred at birth (Williams & King, 1980). Holding this "fixed view" of intelligence has been shown to lower motivation, undermine performance, and disrupt learning, especially when learners are faced with challenging content (Dweck & Leggett, 1988;Grant & Dweck, 2003;Mangles et al., 2006;Yeager & Dweck, 2012). ...
We investigated how participating in a STEM teacher recruitment program impacted undergraduate students’ decisions to pursue teaching and their self-reported preparation for teaching. We collected and analyzed survey and interview data from current and former participants of a University of California system-wide STEM teacher recruitment program called CalTeach. We found a significant relationship between undergraduates’ decision to pursue a career in teaching and the number of undergraduate education courses they completed. We also found that undergraduates who decided to pursue a career in teaching reported various ways that CalTeach influenced their decision. Undergraduates reported that participating in CalTeach reinforced or strengthened their decision to pursue teaching and that the classroom-based field experiences were especially helpful in shaping their decision. Indeed, the field experience component of CalTeach provided participants with opportunities to gain experience working with students in a variety of grade levels and classroom contexts, gain a teacher’s perspective of classrooms, and gain opportunities to practice teaching or to apply theory and methods. Further, we found that undergraduates who decided not to pursue a teaching career also reported ways that CalTeach influenced their decision. For many in this latter group, CalTeach helped them realize that a career in teaching was not aligned with their strengths or interests. Finally, we found that CalTeach participants reported gaining more knowledge of current science and mathematics standards and a greater appreciation of teachers. However, fewer participants reported gaining an understanding of teaching multilingual learners. Our findings strengthen the argument for the implementation of STEM teacher recruitment programs and suggest ways to improve these programs. Recruitment programs should attend to the types of field experiences offered and how field experiences and coursework can deepen prospective teachers’ understanding of reform-based instruction for linguistically diverse students.
Scheduling is essential for the pursuit of everyday goals. Individual differences in scheduling are known as scheduling styles. Two subtypes of scheduling styles, clock-time style (based on time) and event-time style (based on progress), have recently been focused on as being related to individual differences in self-control. This study developed the Japanese version of the Task Scheduling Questionnaire (TSQ) for assessing people’s trait-like scheduling styles. We conducted three studies to examine the relationship between scheduling styles and university students’ pursuit of academic goals. Studies 1 and 2 surveyed university and high school students. The results indicated that the Japanese version of the TSQ had good reliability and validity. Study 3, using the Japanese version of the TSQ, revealed that the event-time style was positively associated with academic engagement, learning behavior, and university students’ academic performance. In contrast, the clock-time style was not associated with academic indicators. We have discussed the limitations of this study and the relationship between clock-time style and everyday goal pursuit.
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Intergroup contact theory provides a useful framework for effective interventions to improve intergroup relationship; however, disharmony between various social groups perseveres. These contact processes, their successes and failures, remain relatively unexplored from the perspective of human motivation to engage in intergroup contact. To address this, we integrate self-determination theory as a well-established theory of human motivation with existing evidence of intergroup contact research. Further, we explore the role of individual well-being in intergroup contact, which, though a prominent outcome of self-determined behavior according to motivational theories, is rarely addressed in the contact literature. Finally, this review discusses how the theoretical integration can serve to categorize and interpret findings from contact research. We deduce implications for future intergroup contact research which may reveal further mechanisms of contact and guide conceptualizing more effective intergroup contact interventions.
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Why do some students excel in their college classes and develop interest in an academic discipline? The authors examined both the short-term and long-term consequences of students' achievement goals in an introductory psychology course. Mastery goals positively predicted subsequent interest in the course, but not course grades. Performance goals positively predicted grades, but not interest. Three semesters later, the authors obtained measures of continued interest in the discipline and long-term performance. Mastery goals predicted subsequent enrollment in psychology courses, whereas performance goals predicted long-term academic performance. These positive and complementary effects of mastery and performance goals on different measures of academic success are consistent with a multiple-goals perspective in which both goals can have beneficial consequences in college education.
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The authors investigated personality predictors of achievement goals in an introductory psychology class, as well as the consequences of these goals for the motivation and performance of 311 undergraduates. Two dimensions of achievement motivation (workmastery and competitive orientations; J. T. Spence & R. L. Helmreich, 1983) predicted the goals endorsed. Individuals high in workmastery were more likely to adopt mastery goals and less likely to adopt work avoidance goals, whereas competitive individuals were more likely to endorse performance and work avoidance goals. Students adopting mastery goals were more interested in the class, but students adopting performance goals achieved higher levels of performance. These results suggest that both mastery and performance goals can lead to important positive outcomes in college classes.
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Currently, there is a debate about which types of achievement goals promote optimal motivation. A number of theorists argue for a mastery goal perspective focusing on the adaptive consequences of mastery goals and the maladaptive consequences of performance goals. Others endorse a multiple goal perspective in which both mastery and performance goals can be beneficial. The purpose of the present investigation was to review why this debate has emerged and to offer a comprehensive test of the mastery vs. multiple goal perspectives. In Study 1, a correlational approach was employed to identify the optimal goals for college participants to adopt for a learning activity. In Study 2, an experimental approach was employed to identify the optimal goals to assign for the same activity. Each study revealed benefits of both mastery and performance goals, providing support for the multiple goal perspective.
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This chapter addresses the question of optimal motivation using an achievement goal approach. In general, goals can be defined as cognitive representations of what is hoped to be accomplished, and they give direction and energy to behavior. They also can vary in their level of specificity, ranging from concrete and task-specific goals to more general and broad goals. One particular class of goals, achievement goals, has emerged as the dominant framework for studying achievement motivation. Achievement goals reflect the purpose or reason for an individual's achievement pursuits in a particular situation, and theorists have converged on two general types of achievement goals. The chapter also reviews experimental work that reveals some of the conditions under which performance goals promote intrinsic motivation, as well as correlational work that reveals a positive association between performance goals and academic performance. Achievement goal theorists have reexamined the performance goal construct and argued that it confounds theoretically distinct components. The chapter describes a theoretical model that has guided experimental work on this topic and then presents experimental results that support the model. Following this, it presents some of the recent correlational work in classroom settings. The chapter then demonstrates how the findings, whether experimental or correlational, support a multiple goal perspective rather than the mastery goal perspective. Finally, it describes a set of studies designed to bridge existing experimental and correlational work and outlines four ways in which mastery and performance goals can work in concert to promote optimal motivation.
This research was designed to incorporate the test anxiety (TA) construct into the hierarchical model of approach and avoidance achievement motivation. Hypotheses regarding state and trait TA were tested in 2 studies, and the results provided strong support for the predictions. State TA (specifically, worry) was documented as a mediator of the negative relationship between performance-avoidance goals and exam performance. The positive relationship between performance-approach goals and exam performance was shown to be independent of TA processes. A series of analyses documented the conceptual and functional convergence of trait TA and fear of failure (FOF), and further validation of the proposed integration was obtained by testing trait TA/FOF and state TA together in the same model. Mastery goals were positively and performance-avoidance goals negatively related to long-term retention.
We used structural equation analysis to test the validity of a goal mediational model for conceptualizing the influence of individual and situational variables on students' cognitive engagement in science activities. Fifth- and sixth-grade students (N = 275) from 10 classrooms completed a set of questionnaires designed to assess their goal orientations and their use of high-level or effort-minimizing learning strategies while completing six different science activities. Results indicate that students who placed greater emphasis on task-mastery goals reported more active cognitive engagement. In contrast, students oriented toward gaining social recognition, pleasing the teacher, or avoiding work reported a lower level of cognitive engagement. The relative strength of these goals was related to differences in students' intrinsic motivation and attitudes toward science. Our analyses also suggested that these variables exerted a greater influence in small-group than in whole-class activities.
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed.