Learning and Individual Differences xxx (2009) xxx-xxx

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Learning and Individual Differences

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How do motivational regulation strategies affect achievement: Mediated by effortmanagement and moderated by intelligence

Malte Schwinger a,⁎, Ricarda Steinmayr b,1, Birgit Spinath b,2

a Department of Psychology, University of Giessen, Otto-Behaghel-Str. 10F, 35394 Giessen, Germanyb Department of Psychology, University of Heidelberg, Hauptstrasse 47-51, 69117 Heidelberg, Germany

⁎ Corresponding author. Tel.: +49 641 99 26 255.E-mail addresses: malte.schwinger@psychol.uni-gies

ricarda.steinmayr@psychologie.uni-heidelberg.de (R. Stbirgit.spinath@psychologie.uni-heidelberg.de (B. Spinat

1 Tel.: +49 6221 547728.2 Tel.: +49 6221 547355.

1041-6080/$ – see front matter © 2009 Elsevier Inc. Aldoi:10.1016/j.lindif.2009.08.006

Please cite this article as: Schwinger, Mmanagement and moderated by intelligenc

a b s t r a c t

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Article history:Received 2 February 2009Received in revised form 20 August 2009Accepted 26 August 2009Available online xxxx

Keywords:Self-regulated LearningMotivational RegulationRegulation of Motivation

It was assumed that the effect of motivational regulation strategies on achievement is mediated by effortmanagement and moderated by intelligence. A sample of 231 11th and 12th grade German high-schoolstudents provided self-reports on their use of motivational regulation strategies and effort management andcompleted an intelligence test. Students' half-year grades (GPA) were assessed six months later. As expected,motivational regulation strategies were not directly related to GPA but showed positive effects on students'effort management which in turn predicted students' GPA. Intelligence and effort management were equallystrong direct predictors of GPA. Also as expected, more intelligent students benefitted more from using some,but not all, motivational regulation strategies in terms of increased effort management. Implications of thesefindings regarding the effectiveness of motivational regulation strategies are discussed.

sen.de (M. Schwinger),einmayr),h).

l rights reserved.

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© 2009 Elsevier Inc. All rights reserved.

1. Introduction

Although theoretical models of self-regulated learning incorporatemotivational regulation as an important component (Boekaerts &Corno, 2005; Sansone & Thoman, 2005; Zimmerman & Kitsantas,2005), only few studies have addressed the effectiveness of motiva-tional regulation empirically (Schwinger, von der Laden & Spinath,2007; Wolters, 1998, 1999, 2003; Wolters & Rosenthal, 2000). In thispaper, we suggest that direct effects of motivational regulationstrategies on performance are not likely to occur because the inherentaim of motivational regulation is optimizing motivation but notperformance. We rather propose that students who have successfullyregulated their motivation show better effort management (Wolters,1999) which in turn should result in better performance. As a secondissue,we investigate the role of intelligence as a potentialmoderator ofthe effects of motivational regulation strategies. We argue thatintelligence influences achievement not only in a direct way but alsovia better use of motivational regulation strategies.

1.1. Motivational regulation strategies

In the present paper, self-regulation of motivation is defined as themore or less conscious control over one's own motivation which

mostly serves to increase effort and persistence (Wolters, 2003).Whereas most research in the field of self-regulated learning has beenconducted on cognitive and metacognitive regulation strategies, thequestion of how learners keep up their motivation has received lessattention.

In recent years, Wolters (1998, 1999) has presented worksyielding a convincing taxonomy of motivational regulation strategies,resulting in a questionnaire which assesses five different motivationalregulation strategies. Building on the work by Wolters (1998, 1999,2003), Schwinger et al. (2007) constructed a German questionnaireconsisting of eight motivational strategy scales. In their first study, atranslated version ofWolters' original itemswas presented to Germancollege students. Additionally, the students had to answer openquestions referring to how they regulate their motivation in everydaystudy situations. Based on an analysis of the psychometric propertiesand an examination of the open responses, item wordings wererevised and the questionnaire was extended to eight strategies. In twofurther validation studies, this revised questionnaire yielded goodpsychometric properties (CFA, internal consistencies) and alsoshowed substantial correlations with external criteria (e.g., cognitivelearning strategies, goal orientations). Thus, we distinguish eightmotivational regulation strategies in the present article which arefurther explained in the next section.

The strategy of Enhancement of Situational Interest refers to theimaginative modification of a boring activity to make it more exciting,for example, by modifying one's hand-writings in a copying task(Sansone, Weir, Harpster & Morgan, 1992). A second strategy whichalso aims at interest enhancement is Enhancement of PersonalSignificance. The focus here is more on assembling relations betweenthe task and the person's individual interests and preferences

trategies affect achievement: Mediated by effortdoi:10.1016/j.lindif.2009.08.006

3 This argument refers to motivational regulation in general. However, particularstrategies might also have a direct, positive influence on students' performance as theyfacilitate cognitive processing during task completion. With respect to the motiva-tional strategies presented here, this rationale is suitable for the strategies Environ-mental Control and Proximal Goal Setting.

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(Leutner, Barthel, & Schreiber, 2001). In Wolter's (1999) originalquestionnaire, these two strategies were combined into one singlestrategy named interest enhancement. However, in the study ofSchwinger et al. (2007), a confirmatory factor analysis yielded supportfor the assumption of two separate interest-related motivationalregulation strategies.

The next three strategies emphasize goals. Students motivatethemselves with goal-oriented self-talk by anticipating their overallgoal. Wolters (1999) distinguished between Mastery Self-Talk andPerformance Self-Talk. A student who regulates his motivation byMastery Self-Talk will highlight the goal to enlarge his competenceand master challenging tasks. Following recent developments in goaltheory research (e.g., Elliot, 1999, 2005), in the present paper Per-formance Self-Talk is further subdivided into Performance-approachSelf-Talk and Performance-avoidance Self-Talk (Schwinger et al.,2007).

The sixth strategy is named Self-Consequating and relies on theprinciples of behavioural reinforcement. A person can motivateoneself by self-reinforcement for having reached a particular goalsuch as the daily amount of homework in mathematics. Manystudents report to combine Self-Consequating with the seventhstrategy of Proximal Goal Setting (Schwinger et al., 2007; Wolters,2003). This strategy consists of breaking a long-term goal (e.g.,completing an extensive and complex task) into smaller subgoalswhich are more easily achieved. The motivation to work for aproximal, easily reachable goal is often higher than for a distant,more complex goal (Bandura & Schunk, 1981). The eighth and finalmotivational strategy is Environmental Control, which refers tobeneficial arrangements of the working environment (e.g., to workin a quiet place such as a library; Zimmerman & Martinez-Pons,1986).

1.2. Effects of motivational regulation strategies on achievement

In the motivational regulation literature, there is the assumptionthat the use of motivational regulation strategies has positive effectson effort and persistence as well as positive effects on subsequentperformance (Boekaerts, 1996; Wolters, 1999, 2003). However, theempirical basis for these assumptions is weak (Leutner et al., 2001;Sansone et al., 1992; Sansone, Wiebe & Morgan, 1999). The only twofield studies investigating motivational regulation strategies weknow of are the studies by Wolters (1998, 1999). Wolters (1998)combined several strategies under the label of extrinsic (PerformanceSelf-Talk and Self-Consequating) vs. intrinsic regulation strategies(Mastery Self-Talk, Interest Enhancement, and Efficacy Enhancement).Whereas extrinsic regulation strategies proved to be a weak butsignificant predictor of students' course grades, no effects werefound for intrinsic regulation strategies. In a second study, Wolters(1999) examined the relation between five single motivationalregulation strategies, learning strategies, effort management, andGPA in a sample of 9th and 10th grade students. Effort managementis not a measure of actual effort in a specific learning situation, butrather assesses the overall motivation of a student, i.e. hiswillingness to invest effort in learning. Thus, effort management isan adequate measure to reflect the overall, situational independenteffect of motivational regulation strategy use. In Wolters' study, allmotivational regulation strategies showed moderate positive corre-lations with effort management but only Performance Self-Talkcorrelated significantly with students' GPA.

These results seem to indicate that motivational regulationstrategies might at best have weak effects on achievement. Weargue, however, that the importance of motivational regulationstrategies for performance is underestimated when looking at thedirect association between motivational regulation strategies andperformance. It seems trivial to emphasize that motivationalregulation strategies primarily aim to influence motivation. They

Please cite this article as: Schwinger, M., et al., How do motivatiomanagement and moderated by intelligence, Learning and Individual Di

do not aim to influence performance directly.3 Rather thansuggesting direct effects, we suppose that motivational regulationleads to an increased motivation, understood as the willingness toengage in and sustain a high level of effort in completing a task,which in turn should lead to better performance. This reasoning is inline with theoretical considerations by Wolters (2003) and also witha theoretical model proposed in a recent meta-analysis on thepredictive power of study habits, skills, and attitudes (Credè &Kuncel, 2008). However, as far as we know, the mediated impact ofmotivational regulation strategies on performance has not yet beentested empirically.

1.3. Intelligence as a moderator of the effects of motivational regulationstrategies

To our knowledge, a study by Sansone et al. (1999) is the only onethat tested moderator effects of personality variables (hardiness andconscientiousness) on the effectiveness of motivational regulationstrategies (see Dewitte & Lens, 1999, and Sansone & Thoman, 2006,for a theoretical discussion). In the present article, we seek to broadenempirical research on this topic by focusing on themoderator functionof intelligence.

Besides motivation, intelligence is known to be one of the mostimportant prerequisites for learning and achievement (cf. Kuncel,Hezlett, & Ones, 2004). We argue that intelligence influencesachievement not only in a direct way but also indirectly via betteruse of motivational regulation strategies. Some intelligence theoristseven include self-regulation capacities as a central element in theirdefinition of intelligence (Grigorenko & Sternberg, 2001; Sternberg,2005). They emphasize that intelligent behaviour can be describedbest as successful self-regulation. Other researchers at least supportthe idea that more intelligent persons should have self-regulationadvantages compared to less intelligent persons for several reasons(Johnson, Im-Bolter, & Pascual-Leone, 2003). More intelligent personsshould have a greater variety of strategies at their disposal and shouldbe better at applying these strategies effectively (Calero, García-Martín, Jiménez, Kazén, & Araque, 2007).

In this article, it is proposed that the overall assumed interactionbetween intelligence and self-regulation strategies will be found formost but not all kinds of motivational regulation strategies. Wesuppose that the quality of strategy application is of foremostimportance for their effectiveness and that motivational regulationstrategies vary in the degree to which a qualitative variation in theirapplication is possible. Some strategies come close to “all or nothing”strategies whereas others encompass a wide scope of means toinfluence effort. Thus, higher intelligencewill be an advantage only forthe use of strategies with a relatively wide scope of means to applyingthem.

Specifically, we suppose the impact of goal-oriented strategies(Mastery, Performance-approach, and Performance-avoidance Self-Talk) on effort management not to depend on the quality of strategyapplication, since goal-oriented self-talk refers to a relatively simpleand particularly inflexible cognitive process. These strategies providelittle scope in application, i.e., these are all or nothing strategies. Theremaining motivational regulation strategies each are strategies witha wide scope in application, so their effectiveness is supposed to bemoderated by students' intelligence. An example refers to the strategyof Proximal Goal Setting. One can certainly suggest that setting toomany as well as too few proximal goals will probably not yield thedesired result. A student who sets too many proximal goals is

nal regulation strategies affect achievement: Mediated by effortfferences (2009), doi:10.1016/j.lindif.2009.08.006

5 Multicollinearity is assumed to lead to biases when estimating the standard errorsof the regression coefficients, which in turn can produce strange, misleading, or

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supposed to feel disrupted from task completion, while a studentwho sets too few proximal goals is assumed to lose motivation overtime as his next subgoal is always still too far to reach. Therefore, toget the optimal motivational effect using the strategy of ProximalGoal Setting, it is critical to fix the personally adequate number ofsubgoals.

1.4. Aims and hypotheses

We address two research questions. First, we want to show thatmotivational regulation strategies affect performance mediated byimproved motivation, operationalized as effort management. Second,we seek to analyse intelligence as a potential moderator of theeffectiveness of motivational regulation strategies. More intelligentpersons should have advantages in applying the strategies of En-hancement of Situational Interest, Enhancement of Personal Significance,Self-Consequating, Environmental Control, and Proximal Goal Setting.No interaction with intelligence is expected for the three goal-oriented self-talk strategies.

2. Method

2.1. Sample and procedure

The sample consisted of 231 11th and 12th graders (139 girls)attending a German school preparing for university (Gymnasium)located in a mid-sized town. Average age was 16.82 years (M=16.82,SD=.75), ranging from 16 to 19. Testing was voluntary, but apartfrom students being ill at the day of testing, all students in grades 11and 12 participated. All students completed a questionnaire whichconsisted of a wide array of scales assessing demographic, cognitive,personality, and motivation variables. Here, we will focus only onthose variables and results which are crucial for the present study(other studies that rely on this sample are published as Steinmayr &Spinath, 2007, 2008).

2.2. Measures

2.2.1. IntelligenceIntelligence was assessed by means of the German version of

Cattel's Culture-Fair-Test 3 (CFT 3; Weiß, 1971). The CFT measuresnon-verbal, fluid intelligence. The test was administered in its shortversion which is highly correlated with the full test version (r=.92;Weiß, 1971).

2.2.2. Motivational regulation strategiesThe use of motivational regulation strategies was assessed with

the questionnaire by Schwinger et al. (2007). All items are provided inAppendix A. Itemswere answered on a five-point scale ranging from 1(rarely) to 5 (very often).

2.2.3. Effort managementStudents' effort management4 was assessed by a subscale with the

same name of the German version of the Motivated Strategies forLearning Questionnaire (MSLQ, Pintrich, Smith, Garcia & McKeachie,1993; Wild & Schiefele, 1994). The scale contains eight items, forinstance, “I work hard to do well in this class even if I don't like whatwe are doing.” All items were answered on a five-point scale rangingfrom 1 (rarely) to 5 (very often).

4 Effort management proved to be a valid indicator of students' motivationalendeavours as it showed substantial correlations to other motivational constructs inour dataset like, for instance, need for achievement (r=.30), mastery goal orientation(r=.29), and the achievement (r=.49) subscale of the PRF (Stumpf et al., 1985).

Please cite this article as: Schwinger, M., et al., How do motivatiomanagement and moderated by intelligence, Learning and Individual Di

2.2.4. Academic achievementAbout five months after testing, the school provided report cards

of the half-year term. Grade point average (GPA) was computed asthe mean of all available grades, not including grades in Sports andMusic/Art. In Germany, grades range from 1 to 6 with 1 indicatingoutstanding and 6 insufficient performance. To facilitate reading andinterpretation, grades were recorded so that higher numbers repre-sented better performance.

3. Results

3.1. Descriptive statistics

The scale means, standard deviations, internal consistencies, andintercorrelations can be seen in Table 1. Regarding the meanfrequency with which students reported using the eight motivationalregulation strategies the results parallel previous findings by Wolters(1999) and by Schwinger et al. (2007). Most of the eight motivationalregulation strategies are positively correlated to a moderate degree.There are moderate to high correlations between motivationalregulation strategies and Effort Management, with the two interestenhancement strategies as an exception. Effort Management, on theother hand, is significantly correlated with GPA, whereas most of themotivational regulation strategies are not. Intelligence is positivelycorrelated with GPA, but does not correlate significantly with any ofthe motivational regulation strategies.

3.2. Effort Management as mediator of the effect of motivationalregulation strategies on achievement

To test the hypothesized mediation effect, a path diagram wasspecified in AMOS 16, in which GPAwas predicted by themotivationalregulation strategies and Effort Management as well as by intelligence.The moderate intercorrelations among motivational regulationstrategies imposed a statistical problem because we run the risk ofmulticollinearity.5 To handle the multicollinearity in our data, wetherefore specified the path diagram with a newly created latentvariable named Motivational Strategies Index (see Fig. 1).

Results showed a significantly positive path from the latentvariable Motivational Strategies Index to Effort Management (ß=.58,path a), and a significant path from Effort Management to GPA(ß=.29, path b). As expected, the beta-coefficient for the direct pathfrom Motivational Strategies Index to GPA did not reach statisticalsignificance (ß=−.14). As a second direct predictor for GPA, theeffect of intelligence on GPA (ß=.25) was similar to the one of EffortManagement. Also as expected, intelligence did neither predict EffortManagement nor the latent variable Motivational Strategies Index.Thus, corresponding paths were not included in the final model.

Using the Sobel test (Sobel, 1982) which overcomes some of thelimitations regarding traditionalmethods for testingmediation (Baron& Kenny, 1986) we determined whether the indirect effect from theMotivational Strategies Index to GPA significantly differed from zero.To conduct this test, the product of paths a and b (see above) is dividedby its standard error, resulting in a t-value. As indicated by the Sobeltest (t[230]=2.98, p<.01), the effect of the latent variable Motiva-tional Strategies Index on GPA was significantly mediated by EffortManagement.6

uninterpretable results (see Marsh, Dowson, Pietsch & Walker, 2004, for an illustrativeexample).

6 To declare a mediation effect, some authors require the direct effect be significantwhen the mediator is not included in the model (which was not the case in our study);otherwise one should just speak of an indirect effect. However, as this requirement hasbeen frequently criticized (e.g., MacKinnon, 2000; Shrout & Bolger, 2002), we keep oncalling the indirect effect a mediation effect.

nal regulation strategies affect achievement: Mediated by effortfferences (2009), doi:10.1016/j.lindif.2009.08.006

Table 1Scale means, standard deviations, reliabilities, and intercorrelations.

α M (SD) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Enhancement of sit. interest (1) .84 2.45 (.77) 1Enhanc. of pers. significance (2) .86 2.81 (.95) .55⁎⁎ 1Mastery self-talk (3) .71 3.01 (.77) .29⁎⁎ .33⁎⁎ 1Perf.-approach self-talk (4) .82 3.60 (.75) .14⁎ .13⁎ .49⁎⁎ 1Perf.-avoidance self-talk (5) .68 2.59 (.74) .28⁎⁎ .17⁎⁎ .32⁎⁎ .47⁎⁎ 1Environmental control (6) .69 3.14 (.79) .36⁎⁎ .29⁎⁎ .42⁎⁎ .25⁎⁎ .20⁎⁎ 1Self-consequating (7) .81 3.51 (.89) .25⁎⁎ .24⁎⁎ .43⁎⁎ .35⁎⁎ .21⁎⁎ .32⁎⁎ 1Proximal goal setting (8) .74 3.34 (.59) .47⁎⁎ .27⁎⁎ .37⁎⁎ .17⁎⁎ .12 .47⁎⁎ .36⁎⁎ 1Effort management (9) .78 3.36 (.55) .07 .09 .48⁎⁎ .37⁎⁎ .23⁎⁎ .44⁎⁎ .27⁎⁎ .28⁎⁎ 1GPA (10) .85 3.09 (.69) −.16⁎ −.02 .01 .03 −.10 .13 −.04 −.07 .18⁎⁎ 1Intelligence (11) .78 28.15 (4.30) −.01 .03 −.01 −.07 −.11 −.10 .02 −.05 −.12 .23⁎⁎

⁎ p< .05.⁎⁎ p<.01.

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3.3. Intelligence as moderator of the effects of motivational regulationstrategies on effort management

To test the assumed moderator function of intelligence, weconducted hierarchical regression analyses with Effort Managementas criterion variable. Guidedby our hypotheses,we computed two sumvariables with the goal-oriented self-talk strategies on the one hand,and the remaining five motivational strategies on the other hand.These sum variables were then used to predict Effort Management. Inthe first step, motivational regulation strategies as well as intelligencewere entered into the regression model. Second, the product term ofthe two variables was added to the model. To avoid multicollinearity,all predictor variables were z-standardized.

The interaction between intelligence and the sum variable goal-oriented self-talk strategies did not show statistical significance (seeTable 2). However, there was a significant interaction betweenintelligence and the sum variable of the remaining five motivationalstrategies (ß=.10, p<.05 one-tailed). To analyse the interactioneffects in more detail, additional simple slope analyses werecalculated (Aiken & West, 1991). These analyses showed significantsimple slopes both for students with high intelligence (t[227]=4.96,p<.01), i.e. 1 SD above the mean, and for students with lowintelligence (t[227]=2.55, p<.05), i.e. 1 SD below the mean, albeit

Fig. 1. Path model of the relationship between the latent motivational strategies index, efRMSEA=.07). *p< .01.

Please cite this article as: Schwinger, M., et al., How do motivatiomanagement and moderated by intelligence, Learning and Individual Di

the slope for the high intelligent ones showed a slightly steepergradient (see Fig. 2).

4. Discussion

This study followed two purposes. First, we provided empiricalevidence for the assumed theoretical sequence of motivationalregulation strategies, effort management, and achievement. Takingthis mediation approach, we tried to refine the simplified assumptionthat motivational regulation strategies should lead directly to betterachievement. Our second goal was to show that students' intelligencefunctions as a moderator for the effect of motivational regulationstrategies on effort management. In line with our predictions, thismoderation was found for some but not all strategies.

4.1. Mediated effects of motivational regulation strategies

Altogether, the path analysis results support our view of a “step-by-step” sequence with respect to the effects of motivationalregulation strategies. Apparently, the use of such self-regulationstrategies does not lead directly to better achievement, as most of themotivational strategies were not directly related significantly to GPA.However, most of the motivational regulation strategies correlated

fort management, intelligence, and GPA (χ2[36, N=231]=74.728, p=.00; CFI=.93;

nal regulation strategies affect achievement: Mediated by effortfferences (2009), doi:10.1016/j.lindif.2009.08.006

Table 2Hierarchical regressions for the prediction of effort management.

Step 1 Step 2

Predictors B (SE) ß B (SE) ß

Sum self-talk strategies .12 (.02) .45⁎⁎ .12 (.02) .45⁎⁎

Intelligence −.05 (.04) −.09 −.05 (.04) −.09Sum self-talk strategies× .01 (.02) .01intelligenceR2/ΔR2 .21 .21/.00

Sum remaining strategies .06 (.01) .33⁎⁎ .06 (.01) .32⁎⁎

Intelligence −.07 (.04) −.11⁎ −.07 (.04) −.11⁎

Sum remaining strategies× .02 (.01) .10⁎

intelligenceR2/ΔR2 .12 .13/.01

⁎ p<.05 one-tailed.⁎⁎ p<.01.

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significantly with the amount of effort management. Higher effortmanagement, in turn, was related to better achievement. Takentogether, the path analysis results showmeaningful indirect effects ofmotivational regulation strategies on achievement, which are medi-ated by an increased readiness to engage more in learning thanusually. In this regard, our analyses supplementWolters' (1998, 1999,2003) empirical findings and theoretical considerations by providing

Fig. 2. Intelligence as a moderator of the effect of two groups of motivational regulationstrategies on effort management.

Please cite this article as: Schwinger, M., et al., How do motivatiomanagement and moderated by intelligence, Learning and Individual Di

an empirical test of the assumed indirect effect of motivationalregulation on achievement.

Although the path analysis results confirmed the mediationhypothesis, the correlational findings point to some notable distinc-tions among the single regulation strategies. First, the two interestenhancement strategies were not significantly related to effortmanagement. Moreover, the strategy Enhancement of SituationalInterest even showed a negative relation to GPA. These surprisingresults are akin to previous findings by Schwinger et al. (2007) whoconcluded that this particular motivational strategy might be a rathertricky one because success depends on both creativity and the amountof time needed to adequately implement the strategy into one's ownlearning process. In fact, as these results contradict positive findings bySansone, Wiebe, and Morgan (1999), knowing the specific action astudent has undertaken to enhance his situational interest might becrucial for evaluating the effectiveness of this particular motivationalstrategy.

4.2. Moderated effects of motivational regulation strategies

The results by and large support our assumption that intelligencemoderates the effects ofmotivational regulation on effort exertion andachievement. As expected, the effects of the three goal-oriented self-talk strategies on effort management were not found to be moderatedby intelligence. This corroborates our belief that students should profitfrom using these strategies regardless of their cognitive abilities. Goal-oriented self-talk appears to be an “all or nothing” strategy and seemsto be relatively easy to apply.

Also as expected, the effect of the sum variable of the remaining fivemotivational regulation strategies on effort management was shown tobe significantly moderated by students' intelligence: The moreintelligent a student is, the more he or she profits from applying thesemotivational strategies. This result supports the idea that a qualitativelybetter application of a motivational regulation strategy is important forthe strategies' effects. Moreover, these findings parallel empiricalevidence in the field of cognitive regulation strategy research (Leutner,Leopold, & den Elzen-Rump, 2007; Rickards & August, 1975).

4.3. Limitations and suggestions for future research

Our study is limited in several ways. First, we only analysed 11thand 12th grade students. Therefore, we cannot say if the resultsreplicate for older or younger students.We agree withWolters (2003)who concluded that additional research is needed to comparestudents' regulation of motivation at early ages and older age levels.Second, and probablymost important, we did not ask for the quality ofmotivational strategy application. The problem with assessing thequality of strategy application is that self-reports are probably not veryvalid. We suggest that further research should try and capture thequality of strategy application via behaviour observations. Third, weexamined only intelligence as a potential personal moderator variableof the effects of motivational regulation strategies. In future studies,more personal attributes, such as goal orientations (Schwinger &Wild,submitted for publication; Spinath & Spinath, 2005), should be takeninto account.

Appendix A. Motivational Regulation Questionnaire

Enhancement of Situational Interest(07) I make learning more pleasant for me by trying to arrange it

playfully.(12) I try to invent a corresponding game if I have to learn or do

certain tasks.(14) I make myself look for ways to bring more fun to the tasks.(22) I carry out the tasks by highlighting the features that are fun.(28) I consider a way to make work more entertaining.

nal regulation strategies affect achievement: Mediated by effortfferences (2009), doi:10.1016/j.lindif.2009.08.006

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Enhancement of Personal Significance(15) I look for connections between the tasks and my life as such.(06) I strive to relate the learning material to my own experiences.(23) I try to establish relations between work and my personal

interests.Mastery Self-Talk(30) I persuade myself to work intensely for the sake of learning.(17) I persuade myself to keep on learning in order to find out how

much I can possibly learn.(10) I challengemyself to finish the task and thus learn a lot for me

personally.(21) I tell to myself that I should keep on learning in order to learn

as much as possible for me personally.Performance-Approach Self-Talk(27) I call my attention to the fact of how important it is to obtain

good grades.(16) I attempt to call myself to intense work by focusing on

obtaining good grades.(01) I call my attention to the fact of how important it is to do well

in tests and exams.(08) I tell myself that I should keep on learning if I wish to reach a

good exam.(29) I think about how my grades will worsen if I refrain from

learning.Performance-Avoidance Self-Talk(05) I tell myself that I have to push me more if I do not want to

make a fool of myself.(04) I imagine thatmy classmatesmake fun ofmypoor performance.(25) I think about that itwouldbe very unpleasant forme to perform

worse than the others.Environmental Control(11) I consciously choose such learning timeswhen I can concentrate

especially well.(03) Prior to beginning with work, I strive to eliminate all possible

distractions.(18) I make sure that distractions occur as seldom as possible.Self-Consequating(02) I tell myself that after work I can do something nice, if I first

keep on learning now.(09) I make a deal with myself saying that I will do something

pleasant after I finish work.(19) I promisemyself that, afterwork, I will do something that I like.(24) I put the prospect of any reward to myself in case I finishwork.Proximal Goal Setting(13) I break down the work load in small segments so I get the

feeling that I can handle it more easily.(20) I approach work step-by-step in order to get the feeling that I

proceed well.(27) I tell myself that I canmaster the tasks if I set myself sub goals.

Note. Numbers in parentheses represent the number of the item inthe questionnaire. Items are translated from the original Germanversion by Schwinger et al. (2007).

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