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Developmentalchangesincognitivepersistenceandacademicachievementbetweengrade4andgrade8
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DOI:10.1007/s10212-014-0211-z
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European Journal of Psychology ofEducationA Journal of Education andDevelopment ISSN 0256-2928 Eur J Psychol EducDOI 10.1007/s10212-014-0211-z
Developmental changes in cognitivepersistence and academic achievementbetween grade 4 and grade 8
Krisztian Jozsa & George A. Morgan
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Developmental changes in cognitive persistenceand academic achievement betweengrade 4 and grade 8
Krisztian Jozsa & George A. Morgan
Received: 21 February 2013 /Revised: 13 January 2014 /Accepted: 24 January 2014# Instituto Superior de Psicologia Aplicada, Lisboa, Portugal and Springer Science+Business Media Dordrecht2014
Abstract This study describes changes in cognitive persistence, a key measure of masterymotivation, between the ages of 10 (grade 4) and 14 (grade 8). Prior research in the field ofmastery motivation has focused mainly on early childhood. No longitudinal research findingshave been published about age changes in mastery motivation during the school years. Thesample of this longitudinal study consisted of 372 students in Hungary and was representativeof the Hungarian school population regarding parents’ level of education. Participants were in25 school classes, each from a different town. The instrument was the cognitive persistencescale of the Hungarian version of the Dimensions of Mastery Questionnaire (DMQ). Gradepoint average (GPA), parents’ level of education, the size of the class and of the town wereanalyzed as background variables. Cognitive persistence based on students’ self-reports de-creased substantially in 61% of the students, did not change in 33%, and increased in only 6 %over the four school years. Changes in cognitive persistence correlated with changes in GPA.Significant differences were found among school classes in both the average level of cognitivepersistence and in howmuch it changed. However, neither cognitive persistence nor changes incognitive persistence correlated with parents’ level of education or town size. Aspects of theschool and classroom climate seem to substantially impact changes in students’ cognitivepersistence. In addition, this study considered educational applications and for further research.
Keywords Cognitive persistence . School achievement .Masterymotivation
This study presents findings of a longitudinal research study that followed the development ofcognitive persistence for a period of 4 years, between ages 10 and 14. It describes the changesthat took place in self-ratings of cognitive persistence from the fourth grade to eighth grade andthe variability of changes in persistence that were found between school classes of students.
Eur J Psychol EducDOI 10.1007/s10212-014-0211-z
K. Jozsa (*)Institute of Education, University of Szeged, 30-34. Petőfi Sándor sgt., Szeged 6722, Hungarye-mail: [email protected]
G. A. MorganSchool of Education, Colorado State University, 209 Education Building, Fort Collins, CO 80523-1588,USAe-mail: [email protected]
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The objective of the study was, furthermore, to explore the relationships between the changesin cognitive persistence, school achievement, and background variables.
Mastery motivation
Mastery motivation inclines children to practice and acquire a new skill or ability and thus hasa fundamental impact on cognitive, social, and psychomotor development (MacTurk andMorgan 1995; Messer 1993; Wang and Barrett 2013). Some studies indicate that masterymotivation may be a better predictor of cognitive development than intelligence, hence playinga crucial role in school achievement (Józsa and Molnár 2013; Yarrow et al. 1975).
Morgan et al. (1990) proposed that mastery motivation is initially a multifaceted, intrinsicpsychological force that stimulates an individual to attempt to master a skill or task that is atleast moderately challenging for him or her. Morgan et al. (1995) identify three maininstrumental measures of mastery motivation: (1) cognitive persistence, which reflects a child’smotivation to persist at and master cognitive and school-related tasks; (2) gross motorpersistence, the motivation to master athletic skills; and (3) social persistence, the motivationto master interpersonal relations with adults and with peers. In addition to these instrumentaldimensions, Barrett and Morgan (1995) emphasize the importance of the affective or expres-sive aspects of mastery motivation, highlighting the role of mastery pleasure and frustrationafter failure.
Shonkoff and Phillips (2000) state that mastery motivation is a key factor in personalitydevelopment. They highlight the importance of research in this field, stating that assessment ofmastery motivation should be an important part of the evaluation of childhood development. Inspite of the crucial importance of the construct, rather few empirical studies about masterymotivation, especially of school-age children, have been published, even though the literaturehighlights the need for further research (McCall 1995; Busch-Rossnagel and Morgan 2013).
Historically, most mastery motivation studies were focused on task persistence in infantsand preschoolers, but the current study deals with the cognitive persistence measure of masterymotivation in school-age children. Morgan (1997) developed a questionnaire to measure theinstrumental and expressive aspects or dimensions of mastery motivation for school-agechildren. A key measure or scale, cognitive persistence, from the Dimensions of MasteryQuestionnaire (DMQ) was used in the current study.
Age changes in the mastery motivation during school years
In cross-sectional studies of Chinese and American students between 7 and 17, J. Wang(personal communication, May 10, 2012) found age decreases in cognitive persistence in bothof these cultures similar to those in a Hungarian study by Józsa (2007).
Cross-sectional measures of mastery motivation were collected in grades 2, 4, 6, 8, and 10in Hungary (Józsa and Molnár 2013). The self-report questionnaires were administered to7,410 students. Reports by teachers of about 3,504 of these students and by parents of about3,843 of these students were also collected. All three groups showed a significant decrease incognitive persistence between grades 4 and 10.
Because these studies were cross-sectional, age changes for individual children and groupsof children in specific schools could not be studied. The current study follows Hungarianstudents and school classes longitudinally over a 4-year period from age 10 to 14.
The concept of mastery motivation is clearly related to the concept of intrinsic motivation,but research in the two fields has developed separately, in part because studies of intrinsic
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motivation have focused on older children and teens, while mastery motivation research has,until recently, focused on infants and preschool children. Although mastery motivation hasusually been assumed to be initially intrinsic in young infants, the definition and focus ofmastery motivation research has been on a child’s persistent attempts to master challengingtasks, whether the reward comes from within or whether extrinsic rewards are offered (seeMcCall 1995).
Age changes in intrinsic and other aspects of motivation
A number of researchers also have found a progressive and significant decline in students’ reportsof motivation from primary school through middle school and into high school, especially inintrinsic motivation (e.g., Gottfried et al. 2001; Harter 1981; Lepper et al. 2005; Newman 1990).For example, Lepper et al. (2005) in a cross-sectional study found a significant linear decrease inself-reported intrinsic, but not extrinsic motivation, from the third grade to eighth grade. In one ofthe few longitudinal studies about age changes in motivation, Gottfried et al (2001) found a lineardecline in academic intrinsic motivation for school in general from 9 to 17.
Gottfried and others have also found similar declines in several specific aspects ofmotivation in several cultures. For example, in the USA, Gottfried et al. (2001) found lineardeclines from 9 to 17 in academic intrinsic motivation for math, science, and reading, but notsocial studies. Lau (2009) found a decline in Hong Kong, Chinese students in reading relatedmotives throughout the school years. Zanobini and Usai (2002) discovered a decrease ofacademic self-concept and motivation in Italy. Spinath and Steinmayr (2008) found longitu-dinal declines in German students’ intrinsic motivation in general and in math, but not Germanlanguage motivation, over a much shorter 1-year period from the end of the second grade (age9) to the end of the third grade. In addition, Józsa (2002) showed a decline in motivation tolearn between grade 7 and grade 9 in Hungary.
Thus, Hungarian as well as international studies have shown that students’ self-reports oftheir motivation significantly decrease during the years spent in school. This tendency hasbeen demonstrated in different subject areas as well as in intrinsic motivation more broadly.Moreover, it has been found in various cultures and educational systems. A common finding ofthese studies was that the decrease seems to start between grade 3 and grade 6 and thencontinues into secondary school.
Aim of this study
Based on prior research on mastery motivation, it can be assumed that cognitive persistencedecreases during the school years. However, these studies used cross-sectional data collection,which provides a reliable estimate about the changes taking place in the studied populations,but cannot be used to analyze individual changes and factors driving these changes. In order toexplore the underlying reasons for changes in individual students and how such differencesrelate to school classes and background variables, a longitudinal method has to be used.
The aim of this research was to explore developmental changes in cognitive persistencebetween age 10 (grade 4) and age 14 (grade 8) in Hungarian schoolchildren. Decline incognitive persistence was hypothesized on the basis of the literature; however, we faced someopen questions:
1. What percentage of students shows an increase in or stable maintenance of cognitivepersistence in spite of the general tendency?
Development of cognitive persistence between grades 4 and 8
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2. How is the change in motivation related to school achievement?3. What differences can be identified between school classes in the change of motivation?4. How are changes in motivation related to parents’ level of education, class size, and town
size?
Method
This study used a longitudinal research design. The first data collection was conducted at thebeginning of grade 4 (October 2007), and the second was conducted 4 years later at thebeginning of grade 8; 93 % of the fourth graders were in the sample at the eighth grade(October 2011). Only the data of those students who participated in both data collections wereused in the analyses.
Participants
The research involved 25 schools, one class from each school, from one county in NorthernHungary. The towns varied in population and in the family background of the parents. Thecurriculum is the same at all these schools. There is only one teacher for each class in thefourth grade, but there are different teachers for different subjects in the eighth grade.
Parental education was classified into five groups, according to their highest level ofattainment: (1) 8 years of primary school, (2) vocational school, (3) academic high school,(4) BA degree, and (5) MA degree. The parents’ level of education was compared to that of anearlier study conducted on a nationally representative sample (Nikolov and Józsa 2006).Hence, a minor sample correction, which involved excluding 4 % of the sample, resulted ina sample representative of all Hungarian parents’ level of education. This corrected samplecontained 372 students in total.
The size of the towns varied from 2,050 to 39,500, and the school class size varied from 8to 29 with a median of 17. The smallest towns usually had smaller class sizes.
Instrument
We used the cognitive persistence scale from the Hungarian student version of the DMQ(Morgan 1997; Morgan et al. 2013) in this study. Students provided a self-report of theircognitive persistence on five-point Likert scales. We used the cognitive persistence scalebecause it has obvious relevance to school achievement and has been found to predictstudents’ GPA (Józsa and Molnár 2013).
The scale consisted of eight positive statements; for example, “I repeat a new problemuntil I can do it well,” “I complete my school work, even if it takes a long time to finish,”“I work for a long time trying to do something hard,” and “I will work for a long timetrying to solve a problem for school.” Cronbach’s alpha of the eight items was 0.76 ingrade 4 and 0.79 in grade 8.
A scale mean was calculated for each participant; then, a linear transformation was con-ducted on the mean, using the formula (x−1)×25. This way, the scale would range between 0and 100, called percentage points (%p). Correspondences between the 1 and 5 values of thescale and the percentage points are as follows: 1=0%p, 2=25%p, 3=50%p, 4=75%p, and 5=100 %p. This index is commonly used in Hungarian research reports because it is felt that a 0–100 scale is easier to understand than Likert scale means.
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A background questionnaire (regarding family background and school achievement) wascompleted by the teachers for each child at both data collection points.
Results and discussion
Age changes in cognitive persistence
In this longitudinal study, the average level of cognitive persistence in grade 4 was 67 %p.After 4 years, by the beginning of grade 8, cognitive persistence had dropped to 52 %p.Standard deviations in both data collections were 17 %p, which means that the magnitude ofindividual variability on cognitive persistence did not change from the fourth to eighth grade(F=1.05, p=0.99). However, motivation decreased significantly (15 %p, t=24.20, p<0.001),with a large effect size of d=0.89.
This 15 %p decline seems to be even larger than the 10 %p decline for Hungarian childrenin a cross-sectional study (Józsa 2007). Other studies of mastery motivation have found asomewhat similar decline for American children and for Chinese students at roughly these ages(Morgan et al. 2013). Whether any apparent differences were due to sampling, cross-culturalvs. longitudinal method, or cultural differences is unknown at this time. It is difficult tocompare the degree of decline with other studies because the ages and measures are usuallysomewhat different and almost all designs were cross-sectional. Harter (1981), Newman(1990), and Lepper et al. (2005) found a similar decline in intrinsic motivation in the classroomfrom the third grade to seventh to ninth grade in several American samples in various parts ofthe country. In a longitudinal study, Gottfried et al. (2001) found a gradual linear decline fromages 9 to 17 in academic intrinsic motivation for school in general in the USA; they also founda steeper decline in academic intrinsic motivation for math between ages 9 and 16 or 17.
The distribution curve of cognitive persistence in grade 4 is somewhat asymmetric (skew-ness=−0.369). Figure 1 shows that while many students felt strongly motivated in grade 4,their proportion decreases substantially by grade 8 along with an increase in the number ofthose with lower motivation.
The level of cognitive persistence in grade 4 correlated with the level of cognitivepersistence in grade 8 (r=0.394, p<0.01). This size correlation suggests that the motivationof all individual students did not change the same way. The students who were the mostmotivated in grade 4 are not necessarily the most motivated ones in grade 8. The 0.394correlation means that only 16 % of the individual difference variability in grade 8 was
Fig. 1 Distribution of cognitive persistence in grade 4 and grade 8
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explained by the differences found in grade 4. This level of correlation between the two datacollection points confirms that there were individual changes in cognitive persistence.
Based on the above findings, it is worth looking at the proportion of students that showedstable or increasing cognitive persistence as well as the proportion that decreased. To conduct thisanalysis, the difference between the fourth and eighth grade ratings was calculated as a separate,difference variable (DCP) with a mean of −15 %p and with a standard deviation of 16 %p. Inthose cases where this value was positive, the student felt more motivated in grade 8 than 4 yearsearlier; in those cases where it was negative, the student felt less motivated in grade 8. In case of anear 0 value, no substantial change in motivation could be detected. Figure 2 displays thedistribution of this variable.
Which differences should be considered substantial? We decided to base a “substantialdifference” on the measurement error of the difference. An increase or decrease was consid-ered substantial when the difference of the grade 4 and grade 8 cognitive persistence valuesexceeded the measurement error. The measurement error was calculated on the basis of thestandard error of measurement of the difference variable, which was 8 %p. Hence, if astudent’s cognitive persistence changed more than 8 %p, the difference was consideredsubstantial. The results suggested that 61 % of the sample showed a substantial motivationdecrease, 33 % showed no significant change, and 6 % showed a substantial motivationincrease over the 4-year period.
Our results demonstrated that the motivation of individual students changes in diverseways. As a first step in analyzing the underlying reasons for these changes, we examined howthe direction of the change was related to the original level of cognitive persistence. Based onthe results of the initial data collection, four quartiles were set up, each containing around 90students. The first quartile consisted of students who reported the lowest level of motivation ingrade 4. Their cognitive persistence was between 14 and 58 %p, while in the second quartile, itwas between 59 and 68 %p; in the third, it was between 69 and 77 %p, and the most initiallymotivated quartile reported cognitive persistence between 78 and 100 %p.
The cognitive persistence of the least motivated grade 4 quartile decreased only 3 %p froma mean of 46 to 43 %p, while in the second quartile, it declined 12 %p, in the third, it declined22 %p, and in the fourth, it declined 29 %p. The difference between the quartiles in thedecrease rate was significant (F=42.1, p<0.001). In general, the more motivated students werein grade 4, the more their motivation declined by grade 8. The decrease in the most motivatedquartile (29 %p) is almost double of the decrease in the whole sample (15 %p). The initial level
Fig. 2 Individual changes in cognitive persistence (difference between the level of motivation in grade 4 andgrade 8)
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of motivation is related to the degree of change (r=−0.561). We also analyzed the proportionof those students who reported no change or increased their persistence in each quartile (seeTable 1). In the lowest quartile, cognitive persistence declined in 45 % of the cases, did notchange in 40 %, and increased in only 15 %. In the fourth quartile, with the originally mostmotivated students, we detected a decline in cognitive persistence in 78 % of the cases and anincrease in only 1 % of the cases. Although regression to the mean may well be an importantpart of the reason that the persistence scores of the top quartile declined, nearly half of thestudents in the bottom quartile also rated their persistence lower.
Relationships of changes in cognitive persistence to school achievement and backgroundvariables
Studies have verified that school itself, with the atmosphere of the class and the evaluation ofstudents’ efforts, has a great impact on motivation (Babad 2009; Good and Brophy 2008;Rosenthal 2002). Individual, classroom, and school-level factors all have been found toinfluence school climate (Koth et al. 2008). Unfortunately, in the current study, we were notable to observe classrooms or obtain data about teacher or school climate differences.However, we were able to study the differences in parent’s education, the size of the town,and the size of the classes which participated, and we were able to obtain student’s GPA at bothgrade 4 and grade 8.
The following sections deal in detail with two factors that may influence children’smotivation in school. First, findings about the relationship between cognitive persistence andschool achievement are discussed. Second, the differences in cognitive persistence betweenschool classes are analyzed, and the relationships with background variables are explored.
Relationships between cognitive persistence and school achievement
Other studies have found that school achievement and motivation are interrelated. On the onehand, motivation may influence later school achievement, but on the other hand, schoolsuccesses and failures may have an effect on motivation to learn and influence its changes(e.g., Covington 2000; Steinmayr and Spinath 2009). A considerable number of studies haveexplored the relationship between school achievement and motivation, both internationally andin Hungary (e.g., Gottfried 1985; Józsa and Molnár 2013; Lloyd and Barenblatt 1984).However, publications about this relationship in the field of mastery motivation has beenrather limited (Józsa and Molnár 2013). We have not found longitudinal studies in theinternational literature that have analyzed the relationship between the dimensions of masterymotivation and school achievement.
For our analysis of the relationship between cognitive persistence and school achievement,represented by school grades, we clustered the students into four quartiles based, this time, ontheir grade 4 school grades (GPA). The first quartile consisted of the students with the lowest
Table 1 The changes in cognitivepersistence based on quartile of 4thgrade ratings
4th grade scores Decrease (%) Stable (%) Increase (%)
Bottom quartile 45 40 15
2nd quartile 57 39 4
3rd quartile 68 29 3
Top quartile 78 21 1
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GPA, below 3.5 on a five-point grading scale. The second quartile contained the students witha GPA between 3.5 and 4.0, the third those between 4.0 and 4.5, and the fourth had studentswho had a GPA over 4.5.
The cognitive persistence scores of the fourth grade students in each GPA quartile were 59,66, 68, and 75 %p. The differences among the four grade 4 GPA subsamples regardingcognitive persistence were overall significant (F=14.27, p<0.001), and Tukey’s b post hoctests found the following percentage point differences: 59<66, 68<75. Likewise, the overalldifference in persistence among these fourth grade GPA quartiles at grade 8 was significant (F=11.25, p<0.001); the post hoc differences in cognitive persistence were as follows: 45<52, 53<59. Hence, the results clearly suggest that there is a relationship between school achievementand cognitive persistence at both ages. The students in the first GPA quartile had a significantlylower cognitive persistence than the other three GPA subsamples. The difference between thesecond and third GPA quartiles was not significant. However, the top GPA quartile had asignificantly higher cognitive persistence than all the other GPA quartiles.
Figure 3 shows that there were no significant differences between the GPA quartiles in thedegree of motivation decline from grade 4 to grade 8 (F=0.273, p=0.845). This means that themotivation of high-achieving students dropped to roughly the same degree as that of themiddle and low performers. So the decrease of cognitive persistence does not seem to dependon the initial GPA.
School achievement correlated with cognitive persistence at 0.327 in grade 4 and 0.408 ingrade 8. Both of these correlations were statistically significant (see Table 2). The relationshipbetween GPA and cognitive persistence was not significantly different in grade 4 and 8 (z=1.90, p>0.05).
Gottfried (1985) and Lepper et al. (2005) found significant but somewhat weaker correla-tions between intrinsic motivation and achievement measured by both GPA and test scores.Lloyd and Barenblatt (1984) and Józsa (2007) found similar size correlations to those in thecurrent study. In addition, Józsa (2002) found that parent and teacher perceptions of thestudents’ cognitive persistence were much more strongly related to GPA; in the case ofteachers, the correlation coefficient was 0.81, and in the case of parents, it was 0.61. One ofthe reasons for this could be that parents and teacher do not differentiate sharply betweenmotivation and performance, as measured by school achievement. They know GPA andassume that high achievers are motivated.
School achievement seems to be more stable over time than motivation; the correlationbetween grade 4 and grade 8 values in GPA was 0.782, while it was 0.394 for cognitivepersistence (see Table 2). Nagy (2010) has shown that grades, and hence GPA, are the moststable school-related indices. They change relatively little even when substantially changes canbe found in students’ skills and standard test scores. Gottfried et al. (2013) also found greaterstability from 9 to 17 for achievement than intrinsic motivation in the area of math.
Fig. 3 Decline in cognitive persis-tence by grade 4 GPA quartiles
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A significant but relatively weak correlation (0.223) was found between the changes ofcognitive persistence and the changes in GPA. This means that in general, those students whoimproved their school achievement also became more motivated, and similarly, those whosuffered a decline in achievement also lost motivation. Gottfried et al. (2013) also found acorrelation between change in motivation and change in achievement (in math) over time.
Our data suggest that students are evaluated more strictly in grade 8 than in grade 4,because the GPA of the whole sample was significantly lower in the eighth grade (3.66) asopposed to the 3.99 GPA of grade 4 (effect size: d=0.55, see Table 3). Next, we discuss thechanges of GPA in the three different subsamples, described earlier, based on their change inmotivation: decreased, increased, or no substantial change.
No statistically significant difference was detected in grade 4 GPA between those whoselater cognitive persistence decreased, increased, or did not change later (F=0.72, p=0.49).Hence, the initial school achievement does not predict later changes in motivation. However,in grade 8, the three motivation change subsamples (substantial decrease, increase, or nochange) could be distinguished well as indicated by the significant difference in GPA (F=6.87,p=0.01). Table 3 shows that those students who demonstrated a substantial decline inmotivation also dropped in GPA from 4.00 to 3.53 (d=0.79). The decrease in schoolachievement was also significant, but less drastic in the group which maintained their levelof motivation (d=0.26). In contrast, the third group, which had a higher cognitive persistencein grade 8 than they had in grade 4 maintained their achievement (t=0.24, p=0.81, d=0.05).
Differences between schools
The study involved 25 school classes from 25 schools, each in a different town. Studentsexperience different teaching styles, methods, and classroom climates at different schools.Therefore, it is not easy to identify fully school factors that modify the motivation of students.Nevertheless, between-school variance can be studied.
Figure 4 clearly indicates that there were significant differences in cognitive persistencebetween the schools in both grade 4 (F=3.96, p<0.001) and grade 8 (F=1.66, p<0.01). Asmentioned above, the overall mean value of cognitive persistence in grade 4 was 67 %p. In
Table 2 Intercorrelations amongstudents GPA and cognitive persis-tence (CP) in grades 4 and 8
CGPA changes in GPA*p<0.05; **p<0.01
Correlations GPA4 CP4 GPA8 CP8 CGPA
GPA at grade 4 –
CP at grade 4 0.327** –
GPA at grade 8 0.782** 0.275** –
CP at grade 8 0.296** 0.394** 0.408** –
Change in GPA −0.147** 0.017 0.501** 0.237** –
Change in CP −0.037 −0.561** 0.108* 0.528** 0.223**
Table 3 GPA means and standarddeviations (in parentheses) bychanges in cognitive persistence
CP cognitive persistence
Changes in CP Grade 4 Grade 8 t p d
Increase 4.17 (0.70) 4.20 (0.84) 0.24 0.81 0.05
Stable 3.94 (0.90) 3.80 (0.97) −2.71 0.01 0.26
Decrease 4.00 (0.81) 3.53 (0.94) −11.92 0.01 0.79
Total 3.99 (0.84) 3.66 (0.96) −10.66 0.01 0.55
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comparison, the school with the lowest mean rated their persistence 54 %p, while the studentsin the school with the highest motivation rated themselves 85 %p. In grade 8, after an averagedecrease of 15 %p, the whole sample mean was 52 %p, with a 38 %p mean in the leastmotivated school class and 63 %p in the most motivated school class. There was a relativelyhigh correlation (r=0.658) between the fourth and eighth grade school means on cognitivepersistence. Thus, in general, schools with a high cognitive persistence ratings at grade 4 hadrelatively high mean ratings at grade 8. However, for the eight schools with the highestpersistence ratings at the fourth grade, there was no correlation (r=0.076) between the fourthand eighth grade motivation ratings. The mean decline in some of these eight schools with ahigh fourth grade motivation was large, but in others, the decline was much less (see Fig. 4).
Although, all classes had a mean decline, it varied from 5 to 29 %p. These decreases werenot equivalent (F=1.89, p<0.01). There were large differences in the change between thoseclasses that showed a similar level in grade 4. For instance, three classes all had a mean initiallevel of motivation of 73 %p, but they demonstrated different decreases: 9, 15, and 29 %p (seeFig. 4).
Parents’ education, town, and class size
The average family background of the school classes was diverse, as indicated by significantdifferences in both the mothers’ and the fathers’ level of education (e.g., mothers’ level ofeducation, F=5.58, p<0.001). Although it might be expected that different family educationalbackgrounds could partly account for the variance in self-ratings of cognitive persistence, ourresults did not reflect any significant correlations between students’ cognitive persistence ateither grade 4 or 8 and either the mothers’ or the fathers’ level of education (see Table 4). Wefurther examined whether the parents’ level of education seemed to have an effect on thechange in students’ motivation; no relationship was detected. The motivation of students fromdifferent family educational backgrounds had similar levels of decrease (F=0.58, p=0.68).
Fig. 4 Decline of cognitive persis-tence by school classes. Note thatthe full column height indicatesmotivation in grade 4. The blackcolumn is the degree of decline andthe white column is the motivationin grade 8
Table 4 Correlations of parentseducation, size of class, and townwith GPA and cognitive persistence(CP) in grades 4 and 8
*p<0.05; **p<0.01
Correlations FaEd MoEd Town size Class size
GPA at grade 4 0.323** 0.368** 0.209** 0.125*
CP at grade 4 0.086 0.019 0.084 0.164**
GPA at grade 8 0.270** 0.332** 0.138** 0.105*
CP at grade 8 0.071 0.070 0.015 0.039
Change in GPA −0.022 0.015 −0.071 −0.007Change in CP −0.018 0.031 −0.075 −0.126*
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However, as might be expected, there were significant correlations at both grades 4 and 8between father’s and mother’s education and student’s grade point average. Thus, it seems thatparent educational level may influence students’ academic achievement but not their self-perceived motivation or the decline in motivation that was observed for most students.
Next, we examined relationships with the size of town, which varied from very smallvillages to medium-sized cities. Remember that only one class in one school in each town wasselected for the study. Parents’ education was related to the size of the town (r=0.350 and0.331 for mother’s and father’s education, respectively). Hence, more educated parents tendedto live in larger towns. As shown in Table 4, student GPA was moderately related to size oftown, indicating that students in larger towns tended to have higher GPAs. However, there wasno relationship between town size and fourth or eighth grade cognitive persistence or declinein persistence from the fourth to eighth grade. Thus, it seems that neither town size nor parents’education is a significant factor in the decline in motivation from grade 4 to grade 8.
Some prior studies support these findings. For example, Lloyd and Barenblatt (1984) andHowse et al. (2003) found little relationship between parent’s level of education or SES andchildren’s motivation. Furthermore, several studies have found few ethnic differences inintrinsic motivation (Gottfried 1985; Lepper et al. 2005; Newman 1990; Stipek and Ryan1997). These findings speak to the very broad nature of the decline in motivation acrossdifferent groups and suggest that any program or intervention to increase mastery motivationor intrinsic motivation in school should include all children, not just target children of low-income parents.
Finally, we examined the relationships of class size with cognitive persistence and GPA. Thesize of each class was based on the number of students who were present in the same schoolclass in the fourth and eighth grade and completed the questionnaire in both years. There weremodest correlations (0.17–0.21) between parents’ education and size of the class. As shown inTable 4, there also weremodest statistically significant correlations between class size and grade4 GPA and cognitive persistence. At the eighth grade, there was a weak (r=0.105) butsignificant correlation of GPA with class size but no relationship to cognitive persistence (r=0.039). There was, however, a significant weak negative relationship (r=−0.126) with changein persistence. Thus, students in larger classes had slightly higher GPAs and were slightly moremotivated at the fourth grade, which declined a little less than those of students in smallerclasses.
Town size and class size were correlated (0.585), so small villages had smaller classes.There seems be a pattern of bigger towns with somewhat larger classes and more educatedparents having somewhat higher GPAs. However, the effect on cognitive persistence and thedecline in motivation from the fourth to eighth grade seem minimal at best. There must beother factors that have more influence on students’ self-perceived motivation to solve problemsand master school tasks. Such factors are probably related to school and classroom climate, butunfortunately, this study did not have measures of such variables.
Conclusion
This study explored changes in cognitive persistence between grade 4 and grade 8. Thesefindings, using a longitudinal research design, reinforced the findings of prior, mostly cross-sectional research, i.e., students’ motivation declines significantly in this period. The decreasein this aspect of mastery motivation, also found in cross-sectional studies, was detected in61 % of the sample in this study. However, our findings also suggest that this decline is notuniversal. One third of the sample did not have any substantial change, and 6 % even showed a
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substantial increase in cognitive persistence over this time period. Hence, the public andresearchers need to be cautious about over generalizing the conclusion that motivation declineswith age.
It was interesting that the most substantial decline was found in those students and classeswho were the most motivated in grade 4. Conversely, a substantial increase in motivation wasfound in 15 % of the initially least motivated students. It seems both theoretically andpractically possible to increase the level of motivation during the years in school. Hence, itmay be that well-designed intervention programs can effectively influence the change ofstudents’ motivation if implemented properly. A good practical example of this would bethe training program recently launched in Hungary (Fejes 2012).
We detected medium-strength correlations between the level of cognitive persistence andGPA, one of the indicators of school achievement. A change in motivation is also modestlyrelated to a change in grades, i.e., deteriorating grades correlate with declining motivation.
Prior research suggests that there are large differences in the level of motivation amongschool classes (Józsa 2007; Koth et al. 2008). This study contributed to these findings,demonstrating that there are also significant differences among schools in developmentalchanges in motivation over a 4-year period. Although every class in the sample had a meannegative change in motivation, significant differences were found in the degree of decline. Halfof the classes contained at least one student with an increasing motivation, whereas the samplecontained only one class where the level of motivation declined in all students.
Cognitive persistence at grades 4 and 8 was not related to the size of the town or to theeducation of the parents and only modestly related to class size at grade 4, but not grade 8.These low relationships indicate that teachers and classroom processes probably have moreimpact on the motivation of students than do demographic factors.
A limitation of the study is using a self-report questionnaire. To what degree does the self-reported decline in motivation correspond a real behavioral decrease? Students go throughsignificant changes in this time period of life regarding their reading comprehension and self-perceptions which may influence their interpretation of the statements. However, a similardecline has also found in students’ cognitive persistence in studies that surveyed teachers andparents (Józsa and Molnár 2013). Other studies have found that self-ratings of some aspects ofmotivation such as intrinsic motivation decline but extrinsic motivation does not (e.g., Harter1981; Lepper et al. 2005; Newman 1990). So older children are not just generally morenegative about their motivation than younger children.
Another possible limitation mentioned earlier is the statistical regression effect or regressionto the mean, which is the tendency for high scores on the initial measurement to regress towardthe mean on a later assessment, and, for low initial scores, to increase on the secondassessment. In this study, students in the highest quartile did usually decline substantially oncognitive persistence, but the lowest quartile also decreased. Thus, although regression couldbe a partial explanation for the overall decline, it is unlikely to be the only major reason.
This study only comprised two points, ages 10 and 14, which limits our knowledge ofdevelopmental change, because there is no any information between the time points. Futureresearch should include more time points and should apply more sophisticated statisticalmethods like structural equation and growth curve modeling techniques.
Our results imply several questions for further research. The most important is why thereare such large individual and school class differences in the change in cognitive persistence.How did the experiences of those students whose motivation decreased and those have whosemotivation increased differ? What roles do teachers, classmates, and classroom processes playin this change? Could the number of students with decreasing motivation be reduced and thosewith increasing motivation be increased? If yes, how? How could an understanding of these
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questions be applied to the construction of motivation intervention programs? These questionsshould be answered with classroom-based research.
Acknowledgments This research was supported by the Hungarian Scientific Research Fund OTKA-K83850application.
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George A. Morgan School of Education, Colorado State University, 240 Education Building, Fort Collins, CO80523-1588, USAhttp://www.colostate.edu/
Current themes of research:
-Over the past 35 years, I have conducted a program of research on children’s motivation to master skills andsolve problems.-With colleagues in the USA and abroad, I developed the Dimensions of Mastery Questionnaire used in thisstudy and others.-I have also published textbooks on research methods and on the use and interpretation of SPSS.
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Most relevant publications in the field of Psychology of Education:
Busch-Rossnagel, N. A. & Morgan, G. A. (2013). Introduction to the mastery motivation and self-regulationsection. In K. C. Barrett, N. A. Fox, G. A. Morgan, D. J. Fidler, & L. A. Daunhauer. (Eds.) Handbook of self-regulatory processes in development: new directions and international perspectives (pp. 247-264). New York:Psychology Press.
Morgan, G. A., Wang, J., Liao, H.-F, & Xu, Q. (2013). Using the Dimensions of Mastery Questionnaire to assessmastery motivation of English- and Chinese- speaking children: psychometrics and implications for self-regulation. In K. C. Barrett, N. A. Fox, G. A. Morgan, D. J. Fidler, & L. A. Daunhauer. (Eds.) Handbook ofself-regulatory processes in development: new directions and international perspectives (pp. 305-335). NewYork: Psychology Press.
Wang, P.-J., Morgan, G. A., Hwang, A.-W., & Liao, H.-F. (2013). Individualized behavioral assessments andmaternal ratings of mastery motivation in mental-age matched toddlers with and without motor delays.Physical Therapy, 93, 79-87.
Sparks, T. A., Hunter, S. K., Backman, T. L., Morgan, G. A., & Ross, R. G. (2012). Maternal parenting stress andmothers’ reports of their infants’mastery motivation. Infant Behavior and Development, 35, 167–173. doi:10.1016/j.inbeh.2011.07.002.
MacTurk, R. H., & Morgan, G. A. (Eds.). (1995). Mastery motivation: origins, conceptualizations andapplications. Norwood: Ablex.
Krisztian Jozsa Institute of Education, University of Szeged, 30−34. Petőfi Sándor sgt., 6722 Szeged, Hungaryhttp://www.staff.u-szeged.hu/~jozsa/indexe.html
Current themes of research:
-His major field of research is mastery motivation.-He is interested in age changes in mastery motivation and the relationship between mastery motivation andcognitive development.-Currently, he works on a computer-based assessment of mastery motivation for 4–8-year olds. He runs preschooland elementary school skills improvement programs in Hungary and has publications in the field of readingcomprehension.
Most relevant publications in the field of Psychology of Education:
Józsa, K., & Molnár, É. (2013). The relationship between mastery motivation, self-regulated learning and schoolsuccess: a Hungarian and wider European perspective. In K. C. Barrett, N. A. Fox, G. A. Morgan, D. J.Fidler, & L. A. Daunhauer (Eds.), Handbook of self-regulatory processes in development: new directions andinternational perspectives. (pp. 265–304.) New York: Psychology Press.
Janurik, M., & Józsa, K. (2012). Findings of a three months long music training programme. HungarianEducational Research Journal 2(4),
Józsa, K., & Steklács, J. (2012). Content and curriculum aspects of teaching and assessment of reading. In B.Csapó, & V. Csépe (Eds.), Framework for diagnostic assessment of reading (pp. 129–182). Budapest:Nemzeti Tankönyvkiadó.
Nikolov, M., & Józsa, K. (2006). Relationships between language achievements in English and German andclassroom-related variables. In M. Nikolov & J. Horvath (Eds.), UPRT University of Pécs Roundtable:empirical studies in english applied linguistics (pp. 179–207). Pécs: Lingua Franca Csoport.
Zsolnai, A., & Józsa, K. (2003). Possibilities of criterion referenced social skill development. Journal of EarlyChildhood Research, 1, 181–196.
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