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The Journal of Experimental Education, 2011, 79, 429–451 Copyright C© Taylor & Francis Group, LLC ISSN: 0022-0973 print /1940-0683 online DOI: 10.1080/00220973.2010.539634

MOTIVATION AND SOCIAL PROCESSES

Achievement Goals and Persistence Across Tasks: The Roles of Failure

and Success

Georgios D. Sideridis University of Crete, Greece

Avi Kaplan Temple University

The focus of this study is on the role of achievement goals in students’ persistence. The authors administered 5 puzzles to 96 college students: 4 unsolvable and 1 rela- tively easy (acting as a hope probe). They examined whether and how persistence may deteriorate as a function of failing the puzzles, as well as whether and how persistence may rebound after an event of success. Time spent engaging in the task comprised the dependent variable persistence (representing a behavioral aspect of engagement). Results suggested that mastery-oriented students persisted significantly longer com- pared with performance approach–oriented, performance avoidance–oriented, and amotivated students across failure trials. However, performance approach–oriented students were more likely to rebound after experiencing success. Qualitative data provided insights into the affective processes that accompanied engagement with the task.

Keywords: achievement goal theory, failure, goal orientation, motivation, perfor- mance goals, success

Address correspondence to Georgios D. Sideridis, Department of Psychology, University of Crete, Rethimnon, 74100, Crete, Greece. E-mail: [email protected]

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IN THE PAST FEW YEARS, achievement goal theory became one of the most highly used frameworks to conceptualize motivation in achievement settings, par- ticularly schools (Elliot, 1999; Pintrich, 2000). In comparison with many other motivational frameworks, which mostly focus on people’s level of motivation such as behavioral choice and persistence, research in achievement goal theory involves a primary focus on the quality of motivation (Atkinson, 1964, 1974). In the past 2 decades, research findings established achievement goal theory as a powerful framework for conceptualizing differences in the quality of students’ engagement, primarily their employment of cognitive and metacognitive strate- gies (Pintrich, 2000). Beyond some early work (e.g., Dweck & Leggett, 1988), relatively less attention has been paid to the association of different achievement goals with indicators of level of motivation such as the initiation and maintenance of engagement. Whereas the importance of quality of engagement is indisputable, quality of engagement requires level of engagement. Arguably, these two dimen- sions of motivation should not be considered separately. Therefore, the relative lack of research on the relations between achievement goals and patterns of level of engagement can be considered a weakness in the current achievement goal literature. This weakness is extended more to the quality of that engagement; thus, engagement should not only be evaluated using quantitative means (e.g., time en- gaged or how that engagement was related to achievement), but also using quality experience indicators (i.e., affective outcomes). So, whether the emotional expe- rience from being engaged in a task by various goal orientations is a byproduct of that experience or a means to an end, is an important question to answer (E. Anderman & Wolters, 2006; Tyson, Linnenbrink-Garcia, & Hill, 2009), especially given the fact that goal failure has been linked to poor physical health outcomes (i.e., enhanced cortisol secretion; see Wrosch, Miller, Scheier, & Brun de Pontet, 2007). In the present study, we investigated the relations between participants’ goal orientations and the patterns of their persistence across several tasks and across experiences of failure and success as expressed emotionally.

Achievement Goal Theory

Achievement goal theory emerged from several lines of research (see Brophy, 2005; Elliot, 1999; Kaplan, Middleton, Urdan, & Midgley, 2002; McClelland, 1951) that were concerned with the meaning that people construe for action and how these affected engagement (e.g., Ames, 1992; Ames & Archer, 1988; Nicholls, 1984). The focus of theorists was on “why and how are students engaging” rather than on “are students engaging?” (Ainley, 1993). Researchers in achievement goal theory grouped meanings of action into two broad classes: (a) mastery goals, in which the purpose is to develop competence; and (b) performance goals, in which the purpose is to demonstrate competence, particularly in comparison with others (Ames, 1992; Dweck, 1986). Mastery and performance goals involve “different waysof approaching, engaging in, and responding to achievement-type activities”

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(Ames, 1992, p. 261), and thus have different implications for cognition, emotion, and behavior (Church, Elliot, & Gable, 2001).

Early on in the development of the theory, several theorists attended to the rela- tions between mastery and performance goals and indicators of level of motivation (Dweck, 1986; Maehr & Anderman, 1993; Meece, 1991; Meece, Blumenfeld, & Hoyle, 1988; Nicholls, 1984). Nicholls (1984) and Skaalvik (1997), for exam- ple, suggested that performance-oriented (“ego-involved” [p. 329] in Nicholls’s terms) people with very high perceived ability would choose to engage in tasks that are deemed to be of moderate difficulty on a normative standard (Nicholls, 1992; Nicholls & Miller, 1984, 1985; Nicholls, Patashnick, & Mettetal, 1986; Nicholls, Patashnick, & Nolen, 1985). Performance-oriented people with lower perceived ability would choose either normatively very easy or very difficult tasks, thus averting the risk of demonstrating low ability. In contrast, Nicholls (1984, 1989) suggested that mastery goals (task involvement) would be associated with choice of tasks that are deemed moderately difficult on a personal standard, re- gardless of perceived ability (see also Dweck, 1986). Similarly, Dweck (1986; Dweck & Leggett, 1988) found that performance-oriented participants with high confidence in their ability demonstrate a mastery behavioral pattern that includes seeking challenges, high persistence, and positive affect in the face of difficulty, whereas performance-oriented participants with low confidence in their ability demonstrate a helpless behavioral pattern that involves avoidance of challenge, low persistence, and negative affect when facing difficulty (Diener & Dweck, 1978). Mastery- (learning-) oriented participants demonstrate the mastery behav- ioral pattern regardless of their level of confidence in their ability.

However, most researchers in achievement goal theory have contended that “researchers and educators should focus on quality of involvement and a contin- uing commitment to learning as consequences of different motivation patterns” (Ames, 1992, p. 262, emphasis added; see Ames & Archer, 1988; Jagacinski & Nicholls, 1987; Pintrich, Conley, & Kempler, 2003; Pintrich & DeGroot, 1990; Ryan, Kiefer, & Hopkins, 2004). Perhaps because most other motivational theories at the time (e.g., self-efficacy, Expectancy × Value, attribution theory, with the exception of self-determination theory) focused on level of motivation, research in achievement goal theory in the past 2 decades has mostly concentrated on the quality of motivation once students are already engaged in the task. Such research has shown that mastery goals are almost always associated with adaptive cognitive, affective, and behavioral patterns. These include the employment of deep, task- relevant, cognitive and metacognitive strategies, positive affect and well-being, optimism, beliefs about the links between effort and success, and also, at times, high performance. In contrast, performance goals were often found to be related to the use of surface cognitive strategies, negative affect and lowered well-being, and with disruptive behavior and cheating in school (see Ames, 1992; Dweck & Leggett, 1988; Nicholls, 1984; Rawsthorne & Elliot, 1999; Urdan, 1997). How- ever, in some studies, performance goals were found to have no relations with

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negative outcomes and processes, and sometimes even positive associations with efficacy and grades (Elliot, 1999; Urdan, 1997).

More recently, achievement goal theorists have made a distinction between two types of performance goals that may help resolve some of the inconsistent findings associated with early studies of this goal orientation: performance approach and performance avoidance (Elliot & Harackiewicz, 1996; Elliot, McGregor, & Gable, 1999). Whereas both of these goals indicate concern with demonstration of com- petence, performance-approach oriented people focus on the possibility of suc- cess and attempt to demonstrate high ability. In contrast, performance-avoidance oriented people focus on the possibility of failure and attempt to avoid demon- strating low ability (Elliot, 1997). Research that investigated the characteristics of engagement associated with these two types of performance goals suggests that performance-avoidance goals are associated with low quality of engagement that involves negative affect, anxiety, self-handicapping strategies, low efficacy, and low performance. In comparison, performance-approach goals are associated with a host of positive characteristics of engagement such as high efficacy, self- regulated learning, high grades, and enjoyment of the task (Elliot, 1999).1 The dichotomization of goals has moved further with inclusion of the mastery avoid- ance construct (Elliot & McGregor, 2001; Elliot & Reis, 2003) or performance goals with different foci (e.g., on outcomes, norms, or ability; Grant & Dweck, 2003). The present study did not include such dichotomizations that are currently at the validation stage.

The research that investigates the associations of different achievement goals and adaptive and maladaptive quality of engagement provided important insights. However, relatively early on in the development of achievement goal theory re- searchers noted that mastery and performance goals are not poles of a continuum, but rather are orthogonal to each other, and may be pursued simultaneously and to varying degrees (Skaalvik, 1997). The notion that achievement goals may be pur- sued together led to investigations concerning the quality of engagement associated with different configurations of mastery and performance goals (e.g., Bouffard & Couture, 2003; Meece & Holt, 1993; Pintrich, 1989, 2000). Overall, the findings seem to suggest that motivational configurations that involve high mastery goals manifest a higher quality of engagement than do motivational orientations that involve low mastery goals. The implication of this generalization is that, when students are strongly oriented to mastery goals, whether or not they are also ori- ented to performance goals is inconsequential. More recently, however, Barron and

1Researchers have also introduced the distinction between approach and avoidance orientations in mastery goals. However, there is still debate whether mastery-avoidance goals are relevant to educational settings. Moreover, the scarce research on mastery-avoidance goals does not allow us to make any generalizations concerning the characteristics of this motivational orientation (Pintrich, 2003).

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Harackiewicz (2000, 2003) found that, among college students, mastery goals and performance goals contribute to different adaptive outcomes (interest and achieve- ment, respectively). This led to the suggestion that, at least for college students, the simultaneous pursuit of high levels of mastery and performance-approach goals would lead to the highest quality of engagement in academic tasks (Elliot & Moller, 2003; Harackiewicz, Barron, Pintrich, Elliot, & Thrash, 2002; Kaplan & Middleton, 2002; McGregor & Elliot, 2002; Ntoumanis & Biddle, 1999; Meece & Holt, 1993; Roeser, Midgley, & Urdan, 1996).

Achievement Goals and Persistence

Research in achievement goal theory has led to important insights concerning the ways by which teachers and school administrators can improve the quality of stu- dents’ engagement (Ames, 1992; Hidi & Harackiewicz, 2000; Kaplan & Midgley, 1999; Maehr & Anderman, 1993; Turner et al., 2002). Yet, one criticism concern- ing this research is the relative lack of attention to level of engagement (Hidi & Harackiewicz, 2000). Whereas researchers continue to explore more issues per- taining to the quality of engagement associated with different configurations of mastery and performance-approach goals (Harackiewicz et al., 2002), much less attention is directed at patterns of level of engagement that are associated with pursuit of different motivational profiles. For example, only little research inves- tigated the patterns of level of engagement that are manifested by students with different configurations of mastery and performance-approach goals across mul- tiple tasks and across experiences of failure and success (Beckmann, Beckmann, & Elliott, 2009; Phan, 2009; Yeo, Loft, Xiao, & Kiewitz, 2009).

Arguably, the researcher who has paid most attention to level of engagement in achievement goal theory is Carol Dweck (1986; Dweck & Leggett, 1988). Dweck suggested that performance goals are endorsed by students who believe that intelligence is fixed (an entity theory). When such students have confidence in their ability, and perceive that they can succeed in demonstrating their high ability (similar to performance-approach goals), they would engage willingly and with effort, and persist in the face of difficulty and failure similarly to students who believe that intelligence is malleable (an incremental theory), who adopt mastery (learning) goals. However, when entity theorists have low confidence in their ability, and are concerned with demonstrating their low ability (similar to performance-avoidance goals), they are at risk of adopting a helpless coping style, have low persistence in the face of failure, or avoid engagement all together.

Dweck and her colleagues conducted several studies in which they investigated the relations between implicit theories of intelligence, achievement goals, and persistence after failure. For example, in a series of studies conducted with fifth- grade students, Mueller and Dweck (1998) praised participants for their success on a set of problems. At first, after receiving success feedback, participants praised for

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their intelligence (and who supposedly adopted performance goals) did not differ in their persistence from participants who were praised for their effort (and who supposedly adopted mastery goals). However, the persistence and the performance of students who were praised for intelligence dropped following an experience of failure, whereas the persistence and performance of students who were praised for effort did not.

In one (Study 4) of another set of studies conducted with college students, Grant and Dweck (2003) tested the relations of mastery goals and different types of performance goals (ability validation, normative comparison, and outcome ori- entation) with several processes including an indicator of persistence. Participants filled a self-report measure of the goal orientations, and then responded to a hypo- thetical scenario of failure by indicating their predictions of their own cognition, emotions and behavior in a similar situation. Participants’ mastery goals were associated with a positive pattern of coping with the failure situation, including a positive association with future planning, and negative correlations with loss of intrinsic motivation and with withdrawal of time and effort. In contrast, ability validation goals (“It is important to me to validate that I’m smart”) were positively associated with withdrawal of time and effort as well as with loss of intrinsic mo- tivation. Neither the performance goals of normative comparison nor of outcome orientation were correlated with withdrawal of time and effort. Normative com- parison was not correlated with either of the dependent variables, and outcome orientation was positively correlated with loss of intrinsic motivation but also with helpseeking. It is interesting to note that it was ability validation goals which were related to a sense of self-worth contingency (i.e., when people’s self-worth is tied to their ability to perform), which is an aspect of the original definition of performance goals (see Ames, 1992; Dweck, 1999; Skaalvik, 1997). Neither normative comparison nor outcome orientation were found to be related to a sense of self-worth contingency.

Dweck and her colleagues (Dweck & Leggett, 1988; Grant & Dweck, 2003; Mueller & Dweck, 1998) suggest that mastery-oriented people see success and failure as information concerning the development of their competencies. Failure provides information about needed amendments for improvements such as in- vestment of more effort or change of strategies. In contrast, performance-oriented people see success and failure as indications of their level of ability, which is conceived of as a stable trait. When this trait is highly related to the person’s sense of self-worth, success signals high ability and enhances the self, whereas failure signals low ability and can threaten self-worth. Thus, after failure, performance- oriented people are hypothesized to reduce level of effort either because they believe that their ability is low, or as a self-handicapping strategy that provides a reason other than low ability for the failure (Urdan & Midgley, 2001). The pre- dictions concerning the associations of multiple goal profiles with indicators of level of motivation are less clear. One possibility is that students with multiple

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goals—in particular mastery and performance-approach goals—may be able to regulate the goal that they highlight in order to continue to engage in the task even if one of the two goals is frustrated. Thus, it may be that the combination of high mastery and high performance-approach would be found to be associated with higher level of motivation in comparison with other configurations in which one goal is more dominant than the other.

The Present Study

The purpose of the present study is to add to our understanding of the patterns of persistence that are associated with adoption of motivational profiles comprising different levels of mastery, performance-approach, and performance avoidance goals. In particular, the study aimed to investigate these patterns in response to events of failure and success. Different from previous studies—most particularly the experiments conducted by Dweck and her colleagues (Dweck & Leggett, 1988; Grant & Dweck, 2003; Mueller & Dweck, 1998), in which participants experienced initial success and then a set-back—in the present study we were interested in the change in participants’ level of engagement (i.e., persistence) after they experience difficulty and failure, and then success.

Hypotheses

Our hypotheses for the study were as follows:

1. The experience of failure will be associated with withdrawal of effort among performance approach–oriented and performance avoidance–oriented stu- dents more so compared with mastery-oriented students.

2. Adoption of mastery goals would be associated more strongly with positive affect and less strongly with negative affect than would be the adoption of performance-approach and performance-avoidance goal orientations.

3. We had no specific prediction with regard to the differential effects of an experience of success following failure on students with different profiles of achievement goal orientation.

METHOD

Participants

Participants were 97 (36 male, 61 female) undergraduate psychology students from a state university in southern Greece, who received extra credit for their participation (age: M = 21.4 years, SD = 2.57 years). The research assistants who helped to administer the research also received extra credit for their participation in a motivation lab. The participants were also provided with a free psychological evaluation (personality profile) in return for their participation. The students were

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also assured of the confidentiality of their responses and were informed that they could withdraw participation at any time during the task.

Procedures

The study was administered to participants by trained research assistants on an individual basis. Participants were invited to a lab, and upon arrival were shown the task which included five assignments. Each assignment asked participants to use seven wooden pieces to construct a specific geometric shape which was provided in an outline. Approximations of the shapes were not considered correct. After arrival at the lab, participants were asked to complete a measure of goal orientation and a measure of positive and negative affect. Then, participants were asked to engage in completing the puzzles. Participants were told that they could spend as much time as they wanted on any one of the puzzles, that they could move on to the next puzzle at their own pace, but that they could not go back to a previous puzzle. After providing the instructions, research assistants pretended to busy themselves on a different project on a computer, but actually monitored the participant’s time spent on each puzzle (using their watch instead of a chronometer so that it was not obtrusive). In addition, they wrote down the actual time when each trial commenced and conducted the actual time calculations after the end of the task. Furthermore, they wrote comments regarding the participant’s behaviors and verbalizations throughout the task.

In four of the five puzzles, the shapes were distorted from the originals to ensure the task was unsolvable. The fifth puzzle was solvable and relatively easy (representing a hope probe). The order of administration of puzzles was as fol- lows: The first three puzzles were unsolvable as it was expected that three failures would be an adequate number to elicit a decrement in persistence (withdrawal trends). After attempting the third puzzle, participants were administered the easy, solvable puzzle, which most students completed correctly in between 30 s and 2 min. Following the easy puzzle participants were administered the final in- solvable puzzle. After stopping to work on the final puzzle, participants were administered a final questionnaire assessing again positive and negative affect.

Measures

Goal orientations. Mastery goals, performance-approach goals and perfor- mance-avoidance goals were assessed using an adapted version of Elliot and Church’s (1997) scales, with a 7-point Likert-type scale ranging from 1 (not at all) to 7 (very much so). All scales were modified to focus on engaging in the puzzles. A sample item assessing mastery goals was “How important is it to you to understand the logic behind solving puzzles?” A sample item assessing performance-approach goals was “How important is it to you to be the only one

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TABLE 1 Descriptive Statistics and Intercorrelations of Goal Orientation Measures in Study 1

Variable M SD Min Max Range 1 2 3

Mastery 3.91 1.34 1 7 5.75 — Performance approach 3.34 1.35 1 7 5.50 .55∗ — Performance avoidance 2.75 1.15 1 7 4.00 .36∗ .69∗ —

∗p < .001.

to solve those puzzles?” A sample item assessing performance-avoidance goals was “Are you concerned that you may not be able to solve as many puzzles as the other students?” Internal consistency estimates were .88 for mastery, .87 for performance approach, and .84 for performance avoidance. Descriptive statistics and intercorrelations of the goal orientation measures are shown in Table 1.

Nonsignificant Kolmogorov-Smirnov goodness-of-fit tests suggested that the distributions of goal orientations within the sample were approximating normalcy. Therefore, goal-orientation groups were formed using median split procedures (cf. Pintrich, 2000). There were 17 mastery-oriented students, 19 with a performance- approach orientation, 8 with a performance-avoidance orientation, and 28 amoti- vated students (having low scores, below the median score, on all goal orienta- tions). In addition, 20 students, who could not be classified into a clear pattern because their scores were neither high nor low in any one orientation, were not included in the study.

Persistence. Students’ persistence was assessed by estimating the time each individual spent with each puzzle. This variable represented the behavioral aspect of engagement (Fredricks, Blumenfeld, & Paris, 2004). Throughout the study, the terms persistence and engagement were used to reflect this behavioral aspect of involvement.

Positive and negative affect. We assessed participants’ affect using the brief version (20-item scale) of the Positive and Negative Affect Schedule (Watson & Clark, 1991). This scale assesses positive affect representing a pleasurable engagement with an activity, or negative affect describing feelings of agitation and distress while being involved in an activity. Alphas were .86 for positive affect and .65 for negative affect at time 1 (at pretask) and .93 and .70 at time 2, for positive affect and negative affect, respectively (at posttest).

Qualitative analysis of affect. The research assistants kept notes on stu- dents’ affective reactions during the sessions by recording all spontaneous

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verbalizations. A team of experts analyzed these verbalizations, and three classes of responses were formed: Responses reflecting (a) negative affect—frustration, (b) positive affect—energization, and (c) calmness—relief, when the task was over. The categories were created by two psychologists with doctoral degrees in social/educational psychology. The development ofoperational definitions for the aforementioned categories followed a reliability analysis. In the end, interob- server agreement was 100% as two disagreements were resolved after discussion across the two raters. There were 44 verbalizations that were coded in one of the aforementioned three categories.

Data Analyses

The differences in the mean time across groups were estimated using omnibus F tests. However, to ensure that the estimated parameters (means) were free of bias, we reestimated the sample means using robust methods (Efron, 1979, 1982, 1985; Efron & Tibshirani, 1993). For each sample mean (each goal group), we created 1,000 resamples with sampling without replacement from the original data and estimated the mean of each bootstrap distribution using the following formula:

Mboot = 1

k

∑ m

∗ (1)

With mboot representing the mean of the bootstrap distribution and m ∗ the mean of

each bootstrap sample (k denotes the number of replications, 1,000 in the present study, which is customary; Chernick, 2007). We then estimated the bias of the mean, which is expressed as the difference between the estimates provided by the sample data and the bootstrap distribution, respectively. The purpose of the method was to create the sampling distribution of a parameter (the mean in the present example) and not rely on the estimates of the sample only (especially in the presence of bias).

We considered a bias of less than 10 s in mean time to be negligible (i.e., a 10-s difference in mean time across trials which lasted approximately 20 min). The results from that simulation indicated that mean bias was negligible (see Table 2). For the mastery goal orientation group in particular, the mean bias was −0.0187 (less than 2s), for the performance approach group 0.0358 s, for the performance avoidance group 0.0014 s, and for the amotivation group −0.0771s. The mean biases per goal group and per trial are shown in Table 2. Thus, those preliminary analyses provided confidence with regard to the sample estimates as being representative of the population of college students.

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TABLE 2 Presence of Bias Between Sample Estimates of Mean and Those of the Bootstrap

Distribution for the Mean

Standard error Sample’s Bootstrap 95% CI of the bootstrap

Trial mean distribution mean Bias of mean distribution mean

Mastery Trial 1 27.41 27.51 0.1001 [21.68, 35.86] 4.196 Trial 2 25.84 25.79 −0.0518 [19.27, 33.03] 4.118 Trial 3 11.47 11.43 −0.0399 [7.108, 16.22] 2.752 Trial 5 �= 10.65 10.57 −0.0831 [7.308, 16.92] 2.724

Performance approach Trial 1 20.4 20.51 0.1122 [14.16, 30.47] 4.722 Trial 2 19.22 19.32 0.0993 [14.30, 27.25] 3.686 Trial 3 9.75 9.754 0.0038 [7.265, 11.69] 1.328 Trial 5 �= 20.42 20.35 −0.0723 [12.58, 38.32] 6.702

Performance avoidance Trial 1 14.1 14.01 −0.0885 [8.815, 21.69] 3.788 Trial 2 10.03 9.95 −0.0787 [6.381, 15.48] 2.598 Trial 3 7.096 7.161 0.0647 [4.233, 10.95] 2.018 Trial 5 �= 13.49 13.6 0.1083 [6.867, 19.21] 3.785

Amotivation Trial 1 23.24 23.08 −0.1551 [17.57, 31.45] 4.125 Trial 2 14.03 14.0 −0.0289 [9.666, 22.70] 3.392 Trial 3 8.632 8.611 −0.0203 [6.168, 12.94] 1.927 Trial 5 �= 9.074 8.97 −0.1041 [5.806, 16.21] 2.661

Notes. The confidence intervals around the mean (bootstrap) are the bias corrected accelerated intervals because T intervals were deemed inappropriate due to the likelihood that some distributions may have deviated from normality.

�=It is referred to as Trial 5 because Trial 4 represented the hope probe (easy task).

RESULTS

Table 3 presents the average number of minutes that participants in each achieve- ment goal group spent attempting to solve the puzzles in each trial. An analysis of interaction effects indicated that there were no statistically significant differ- ences between students having different orientations during the first trial, F(3, 68) = 1.05, ns; however, differences emerged in the second trial, F(3, 68) = 3.831, p < .05; with the mastery-oriented group persisting significantly longer compared with the performance-avoidance and amotivated groups. However, because those comparisons were heavily influenced by inadequate test power (cf. Onwuegbuzie, Levin, & Leach, 2003), we evaluated significant effects with effect size statis- tics (Cohen’s d), considering effects to be significant and meaningful when they

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TABLE 3 Effect Size Analysis of Differences in Persistence Across Goal Orientation Groups

Variable 1 2 3 4

Trial 1 1. Mastery approach — 2. Performance approach −.38 — 3. Performance avoidance −.70∗ −.59∗ — 4. Amotivated −.27 .11 −.80∗ —

Trial 2 1. Mastery approach — 2. Performance approach −.66∗ — 3. Performance avoidance −2.66∗ −1.22∗ — 4. Amotivated −.88∗ −.32 .51∗ —

Trial 3 1. Mastery approach — 2. Performance approach −.55∗ — 3. Performance avoidance −1.04∗ −.48 — 4. Amotivated −.45 −.22 .25 —

Trial 51

1. Mastery approach — 2. Performance approach .61∗ — 3. Performance avoidance .13 −.68∗ — 4. Amotivated −.21 −.86∗ −.39 — ∗Indicates significance effect size on the basis of Cohen’s convention of medium effects. 1Trial 4 acted as a hope probe and all students solved that puzzle within 2 min.

exceeded .5 standard deviation units (see also Cohen [1992] and Howell, 1999). Those standardized differences are shown in Table 3. Overall, mastery-oriented students and performance approach–oriented students were almost always more persistent compared with performance avoidance–oriented students (an exception is Trial 5 for mastery goals, and a marginal difference in Trial 3 for performance- approach goals).

Regulation of Effort in Response to Failure: Trials 1–3

Figure 1 depicts fitted curves for the three groups on the basis of their aver- age persistence scores on the first three puzzles. A visual analysis of Figure 1 suggests that a quadratic function fits the data of mastery-oriented and per- formance approach–oriented students, whereas an exponential function fits the data of performance avoidance–oriented and amotivated students. In combina- tion with the significant differences in persistence between the groups in the various trials, the pattern of data suggests that mastery-oriented, performance approach–oriented, and amotivated students started with a relatively high level of persistence, which was significantly higher than the persistence demonstrated

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FIGURE 1 Data on persistence on the first three trials for college students espousing mas- tery, performance-approach, and performance-avoidance goals. Lines represent best fit using polynomial regression. (Color figure available online).

by performance avoidance–oriented students. Then, following the first failure, mastery-oriented students maintained, or perhaps even slightly increased, their persistence; performance approach–oriented students lowered their persistence slightly, yet enough for it to be significantly lower than the level of persistence demonstrated by the mastery-oriented students; amotivated students lowered their level of persistence to a level that was significantly lower than that demonstrated by mastery-oriented students, but not as low as those demonstrated by performance avoidance–oriented students. Last, performance avoidance–oriented students low- ered their level of persistence slightly, remaining significantly lower than that of the other three groups.

After the second failure, all four groups demonstrated a further drop in persis- tence (expressed with less time spent on a puzzle). However, the significant drop that mastery-oriented students demonstrated still kept them at a significantly higher level of persistence than the performance-approach and performance-avoidance groups, and marginally significantly higher than the amotivated group. The drop in persistence of the performance approach–oriented students kept them marginally significantly higher than the performance avoidance–oriented group, but similar to the level of the amotivated group.

Rebounding From Failure After an Experience of Success: Trial 5

Figure 2 presents fitted curves that combine the scores on students’ average per- sistence in the first three consecutive failure events with the average score of their

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FIGURE 2 Data on persistence for college students having various goal orientations and amotivated students. Persistence on Trial 4 represents enhancements following an easy probe trial (right after the third trial). Lines represent best fit using polynomial regression. The predictive equation for mastery goals was as follows: Y′ = 5.9∗x3 − 45.055∗x2 + 95.755∗x − 27.69. The respective equations for performance-approach, performance- avoidance goals and amotivation were as follows: Y′ = 4.8366667∗x3 − 33.14∗x2 + 64.2533333∗x − 15.09; Y′ = 1.3633333∗x3 − 7.61∗x2 + 9.2166667∗x + 11.13; Y′ = 0.3383333∗x3 − 0.125∗x2 − 11.2033333∗x + 34.23. (Color figure available online).

persistence in the fifth event that followed an experience of success. Thus, the curves provide a view of the pattern of rebound from consecutive experiences of failure of students with different motivational orientations. The findings suggest that among all groups, performance approach–oriented students were those who manifested a significant rebound effect, persisting on average 9 more minutes than their persistence on the previous failed puzzle. This rebound brought their level of persistence close to the levels manifested before the failure experiences and higher than the levels of all other groups. The three other groups, including mastery-oriented students, were not different from each other in level of persis- tence, although the curves suggest the possibility of a slight rebound effect for mastery-oriented and performance avoidance–oriented students. It is interesting to note that similar to the case of performance approach–oriented students, the level of persistence of performance avoidance–oriented students after the success event was also close to the levels manifested before the failure experiences. How- ever, because this level was low to start with, this rebound effect did not manifest itself as significant in statistical terms. Amotivated students remained almost ex- actly at the level of persistence they demonstrated just before the success event (demonstrating a flat profile).

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Affective Changes as a Function of Goal Orientations

Results on affect using the Positive and Negative Affect Schedule pointed to sig- nificant between-group differences in positive affect but not in negative affect at pre- and posttest. The omnibus F test at pretest was F(3, 75) = 4.847, p < .01. The respective test at posttest was F(3, 75) = 4.465, p < .01. In particular, the amotivated group had significantly less positive affect compared with the mas- tery group (at Time 1) and compared with the performance approach–oriented group (at Time 2). No other differences emerged between goal orientation groups.

Qualitative Analysis of Behaviors Associated With Goal Orientations During Task Engagement

We conducted an informal qualitative analysis using as a unit of analysis only covert behaviors (verbalizations; n = 44). Sixteen students contributed data that were indicative of their experience while engaging in the task. Among students, 3 were classified as being calm, 4 as having experienced positive affect, and 9 as having experiencednegative affect during the task.

Seven (78%) of the participants in the negative affect/frustration category were performance-oriented (either approach or avoidance), and; there were no mastery- oriented participants in this category. The following are sample verbalizations for this group: “These are stupid puzzles”; “I can’t solve them, I am tired, and besides, I’ve been sick this week”; “Others will think I am dumb, did Carol solve them?; “Will you tell me at the end how well I did”; “Are you kidding me, I am either stupid or those are insolvable, if I could take them at home I am sure I could solve them.”

The respective percentages of goal orientations in the category of positive affect/energization were 3 (75%) mastery-oriented participants and 0 performance- oriented participants. The following are sample comments from participants in this category: “I would like to try them again”; “I’m going to solve them, are you sure there is a solution?”;“I will stay here until I’ll solve them.”

Last, the third category of calmness and relief when the task was over was fully populated by amotivated participants. These participants made comments such as “I am glad it is over” and were whistling when the task was over. Overall, the findings suggested that performance approach–oriented participants were more likely to give up but also quicker to be energized following a success in com- parison with mastery-oriented participants. Furthermore, the qualitative analysis suggested that performance-oriented students experience substantially elevated negative affect during the task. Nevertheless, the aforementioned findings re- flect a small fraction only of the full sample and should, thus, be interpreted cautiously.

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DISCUSSION

The purpose of this study was to investigate the regulation of effort of participants with different goal orientations in a series of tasks involving a prolonged experience of failure and then an experience of success. The polynomial analyses, which evaluated the potentially dynamic growth–decline pattern of student motivated effort, suggest that different goal orientations are indeed associated with different patterns of persistence when experiencing failure, as well as when experiencing hope from the occurrence of success.

The findings indicate that performance avoidance–oriented participants were those who persisted the least, already on the initial task. Furthermore, these stu- dents seemed to have maintained, and even slightly lowered, their already low level persistence throughout the failure experiences. Last, these students increased their effort after experiencing success, but only to the relatively low level they demonstrated at the initial trial. Performance-avoidance goals are associated with the belief that ability is a fixed entity (Dweck, 1999), that achievement is indica- tive of level of ability, and that effort and ability have an inverse relationship: the more effort one needs to expend in a task, the less ability one has (Jagacinski & Nicholls, 1984; 1987; Nicholls, 1984). Because performance-avoidance oriented people are concerned about not demonstrating low ability, they seem to adopt a defensive approach toward investment of effort that manifests in low persistence already at the initial task. Such a defensive approach seems to manifest in these students’ comments which indicate stress and negative emotions (Pekrun, Goetz, Titz, & Perry, 2002). The finding that with repeated failure these students’ level of persistence decreased may be only slightly the result of a floor effect. And even an experience of success does not seem to overcome their reluctance to expend much effort in attempting to solve the task.

In comparison, performance approach–oriented students started the task with a willingness to expend effort. This willingness dropped somewhat after failure in the first trial and significantly after the second failure. Performance approach–oriented students also believe that achievement on a task is indicative of one’s ability. However, unlike performance avoidance–oriented students, they are concerned with demonstrating high ability in the task. One failure seems to lead to some loss of hope for achieving this goal. A second failure gives this hope a serious blow—and drives their willingness to expend effort to a level similar to that of per- formance avoidance–oriented students. This pattern of thinking relates to Carver and Sheier’s thesis that individuals who experience difficulties in making progress towards a goal seem to develop a pattern of thinking that is reflected in subse- quent expectations of failure and distress (Carver & Scheier, 1990; Linnenbrink, 2005; Pomerantz, Saxon, & Oishi, 2000). Thus, these students feel stressed and frustrated by their failure. However, although they share negative and defensive emotions when experiencing failure with performance avoidance–oriented stu- dents, the findings also suggest that the sense of hope about demonstrating high

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ability of performance approach–oriented students did not perished completely. This sense of hope was relatively easily rekindled by the experience of success, and brought these students’ willingness to expend effort almost back to its initial level. Moreover, these students also reported overall positive affect at the end of the experiment, despite their expression of negative affect during their experiences of failure. It may be, as Elliot (1997; Elliot & Church, 1997) have suggested, that performance-approach goals have some basis in dispositional achievement motiva- tion that makes performance-approach oriented students sensitive and responsive to cues of success, which lead to renewed energy and positive affect. This finding also agrees with the work of Wrosch et al., 2003) who demonstrated that elderly in- dividuals who reengage on previously unattainable goals seem to have better well- being (self-mastery and emotional balance) compared with those who abandon their goals (see also the work by Duke, Leventhal, Brownlee, & Leventhal, 2002). In a similar line of research, Baumeister and his colleagues demonstrated that when esteem is threatened in a challenging task (as in performance oriented individuals), the overriding concern of the individual is to protect self-esteem. However, be- cause the cognitive resources of the person are allocated into “bolstering positive self illusions” (Lambird & Mann, 2006), rather than on accessing self-knowledge, the outcome is self-regulation failure (Baumeister, Heatherton, & Tice, 1993).

Mastery-oriented students focus on figuring out the task and how to solve it. The mastery-oriented students in the present study started the task with much willingness to expend effort to achieve this goal. For them, an initial experience of failure seemed to provide feedback that their efforts were not enough for achieving this desired understanding—they were willing to expend even more effort in attempting the second task. Moreover, they reported positive affects and made comments that indicated their excitement and enjoyment despite their experiences of failure. However, these students also seemed to lose hope for achieving their goal after a second failure—although the drop in level of persistence is not as severe as that displayed by the performance approach–oriented students. More interesting is the finding that an event of success may not rekindle the hope for figuring out the task as strongly as it does to the hope for demonstrating high ability among performance approach–oriented students. This may be the consequence of different interpretations of the meaning of success among performance approach–oriented and mastery-oriented students. Under a performance-approach goals framework, students interpret success as indicating their level of ability. Such an interpretation is likely to boost hope for demonstrating this ability. However, under a mastery goals framework, success is interpreted as indicating successful employment of effort and strategies. The mastery-oriented participants in the present study have already attempted to increase effort after failure, but to no avail. It may be that their attribution for the event of success did not include low effort. Because the success on the fourth trial was significantly easier than the other trials (2 min of effort in comparison with 30 min and 32 min in the failed attempts), this experience may not have been enough to convince these participants that they have made progress in

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their goal of figuring out the task, which would have prompted more engagement. Considering that the puzzles were unsolvable, and that previous expenditure of effort did not lead to more success, the lack of significant increase in effort that was demonstrated by mastery-oriented students after the event of success could be actually perceived as an adaptive response.

Last, the amotivated students started the task with a relatively high level of persistence. This may have been simply a consequence of the novelty of the task—an indication of engagement in something new or just the mere fact that they agreed to participate in the task and may have felt obliged to engage. However, once they experienced failure, they quickly lost interest and may have attempted the following trials out of duty and a desire to receive the extra credit. This is supported by their lowest level of positive affect and by their comments, which indicated negative affect and a desire to leave the situation. Even an experience of success does not seem to change their disinterest. Such avoidance tendencies attributable to goal orientations at the classroom level have been previously reported (e.g., Patrick, Anderman, Ryan, Edelin, & Midgley, 2001; Turner et al., 2002; Turner, Meyer, & Schweinle, 2003).

The present study is limited for several reasons. One is the relatively small sample size, which led to a small number of participants in each group, which, in the presence of individual large differences may result in overlapping distributions (thus, masking real mean differences). We tested for the presence of this potential problem by bootstrapping the parameters of interest (i.e., mean) to ensure that little bias was present. The bias in the mean time observed across categories was negligible (−0.01465 of a second). However, in goal group formation, the initially small sample size may have been the reason for the nonrepresentation of a group that was high on both mastery and performance-approach goals. Some researchers suggest that it is this motivational profile which is the most adaptive for competitive tasks (Barron & Harackiewicz, 2000). The absence of this group from the present sample prevented us from investigating the pattern of persistence associated with this motivational profile. Future research should address this limitation. Another limitation is associated with the sampling procedure. Participants volunteered to be part of the study suggesting that they were already motivated to engage (see work on assigned vs. natural goals by Van Yperen, 2003). Those qualities partially represent an approach orientation and may have been responsible for the positive correlation linking mastery and performance-approach goals, although these goals are rather consistently reported to be positively correlated in studies using Elliot’s scale (e.g. Elliot et al., 1999; see also E. Anderman & Wolters, 2006). Another limitation of our study is with regard to the unsystematic way of approaching students’ verbalizations. Although these verbalizations reflect quality indicators of their experience, we did not perform a systematic qualitative analysis of those verbalizations (because our study was not designed with that focus). Thus, the findings from our unsystematic qualitative analysis should be interpreted with caution.

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In conclusion, the findings suggest quite clearly that adoption of different achievement goals for a task is associated with different patterns of engagement and affect in a prolonged, complex task (L. Anderman, 1999; Tyson, Linnenbrink- Garcia, & Hill, 2009; Yeo et al., 2009). Performance-avoidance goals and having no achievement goals are associated with maladaptive patterns of persistence. In comparison, mastery goals seem to be adaptive in contributing to a high level of initiation of effort, and of maintenance of effort in events of failure. Performance- approach goals also seem to be associated with a relatively adaptive pattern of mo- tivation; however, in repeated events of failure, they seem to be less adaptive than mastery goals are, in terms of maintaining a high level of persistence and in the na- ture of the emotional experience which accompanies engagement (Urdan & Maehr, 1995; Utman, 1997). Yet, performance-approach goals seem to be associated with a quick rebound after an event of success, something not found for mastery goals, in the absence of help seeking (Karabenick, 2004). This difference in response to success after failure is likely the result of the different attributions and focus of engagement that are associated with the different achievement goals, probably in combination with the nature of the specific tasks used in the present study.

What should the educator take in from our study? First that mastery goals are adaptive, a finding that is consistently replicated in the literature. Second that per- formance approach goals, regardless of their variability and emotional turbulence, seem to be keen to probes of hope. Thus, individuals motivated by performance approach goals only need frequent cues of hope and self-esteem boosts to maintain high levels of behavioral engagement with a task. Thus, behavioral engagement was highly predicted by performance approach individuals, more so compared to any other motivational group. The present findings add to the relatively small body of literature, which suggests that performance approach goals are adaptive, under certain circumstances (when hopeful thinking takes place) but at a high cost (Midgley, Kaplan, & Middleton, 2001). The present findings add that the emotional experience from adopting performance-approach goals is poor when these individuals face failure and in comparison with mastery-oriented individu- als. However, the effects of hope outgrow any limitation of their self-regulatory functioning. Future studies should examine the rebound phenomenon in tasks that involve learning and attempt to replicate our findings with regard to performance approach goals.

AUTHOR NOTES

Georgios D. Sideridis, Ph.D., is an associate professor of research methods and applied statistics in the Department of Psychology at the University of Crete. His research interests lie in the interplay between personal and contextual factors that affect motivation in students with and without learning disabilities. In particular, he is interested in the motivational propensities of goals that include normative evaluations in their construct. Avi Kaplan, Ph.D., is an associate professor of

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educational psychology at Temple University. His research interests lie in the areas of (a) student motivation and self-regulation, (b) learning environments, and (c) self and identity development.

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