IMPACT OF STUDENTS’ AND THEIR SCHOOLMATES’ ACHIEVEMENT MOTIVATION ON THE STATUS AND GROWTH IN MATH AND LANGUAGE ACHIEVEMENT OF BOYS AND GIRLS ACROSS GRADES 7 THROUGH 8

————— Eva Van de gaer is a Postdoctoral Research Fellow at the Centre for Educational Effectiveness and Evaluation. Georges Van Landeghem is a Researcher at the Centre for Educational Effectiveness and Evaluation and at the Policy oriented Research Centre for Study and School Careers. Heidi Pustjens is preparing a Ph.D. at the Centre for Educational Effectiveness and Evaluation. Jan Van Damme is Professor at the Faculty of Psychology and Educational Sciences, Department of Educational Sciences and is the director of the Centre for Educational Effectiveness and Evaluation. Agnes De Munter is Professor at the Centre for Methodology of Educational Research and at the Centre Women and Education. She is the director of the Centre Woman and Education. All are attached at the Katholieke Universiteit Leuven. Heidi Pustjens’ work is supported by the Research Fund of the Katholieke Universiteit Leuven. Eva Van de gaer thanks the Flemish Fund for Scientific Research (FWO) for funding her current work. The authors thank the editors for inviting them to contribute to this special issue. Correspondence concerning this article should be addressed to Eva Van de gaer, Centre for Educational Effectiveness and Evaluation, Katholieke Universiteit Leuven, Dekenstraat 2 bus 3773, 3000 Leuven. E-mail: Eva.Vandegaer@ped.kuleuven.be IMPACT OF STUDENTS’ AND THEIR SCHOOLMATES’ ACHIEVEMENT MOTIVATION ON THE STATUS AND GROWTH IN MATH AND LANGUAGE ACHIEVEMENT OF BOYS AND GIRLS ACROSS GRADES 7 THROUGH 8

Compositional effects and achievement
Since the conclusion of Coleman, Campbell, Hobson, McPartland, Mood, Weifeld, and York (1966) that student body characteristics are more impor-  tant factors explaining academic achievement than teacher and school characteristics, there has been a continuing interest among educational researchers in the impact of background characteristics of fellow students on individual academic achievement. Especially the impact of the ability (i.e., intelligence) and to a lesser extent the impact of the socio-economic status of class and schoolmates on academic achievement has received a lot of attention (Hanushek, Kain, Markman, & Rivkin, 2003;Ireson, Hallam, Hack, Clark, & Plewis, 2002;Kerckhoff, 1986). Research on ability grouping, for instance, has shown that students in high ability (class) groups achieved higher than students in low ability class groups (e.g., Gamoran, Nystrand, Berends, & LePore, 1995). The effect of classmates' ability over and above the effect of an individual's ability on academic achievement is also called a compositional or contextual effect. Generally speaking, a compositional or contextual effect can be defined as the effect of a group level (e.g., a class or a school) aggregate variable of an individual level variable over and above the effect of the same individual level variable on a certain outcome variable (Burns & Mason, 2002;Harker & Tymms, 2004). The present study addresses the question whether the achievement motivation of schoolmates is an important predictor -over and above the individual's achievement motivation -of academic achievement during early adolescence.
Schools and classes are important social environments where students socially interact with each other, make friends, and influence each other as in peer groups. Therefore, it is not surprising that class and school peers exert a strong influence on achievement beliefs and behaviour, especially in early adolescence, during which the influence of peers and the need to be accepted by peers is strong (Ryan, 2001). "Although the processes through which peers and friends influence each other are not fully understood, […] the desire to affiliate with friends and peers can undermine, enhance, or have little effect on motivation and achievement. Much depends on the academic and motivational orientations of the friends and peers with whom students wish to affiliate" (Urdan & Schoenfelder, 2006, p. 342). Research has indeed demonstrated that students whose friends cared more about school learning and achievement had better outcomes than students whose friends showed little interest in learning (Berndt, 1999;Chen, 1997). Based on these findings, we expect that students will achieve higher in schools composed of students with higher achievement motivation. Moreover, we expect that this will be true even after controlling for individual students' achievement motivation (and background characteristics), i.e., that we will find a compositional (or contextual) effect. The achievement motivation of class and schoolmates may create a learning 'climate' or a learning 'culture' that facilitates or impedes learning above and beyond what would be expected on the basis of the individual student's achievement motivation and background characteris-tics. Dar and Resh (1994) suggest three (mediating) mechanisms through which group composition impact student learning. Although these mechanisms have been proposed, mainly to explain compositional effects, such as, ability grouping and socio-economic grouping, they can also be applied to the explanation of the compositional effect of achievement motivation. A first mechanism involves peer context. Differences between classes and schools in the mean achievement motivation of its members are associated with different peer contexts that may enhance or impede student learning. In educational settings, such as, schools and classes students use their peers as a normative reference group and through processes of socialisation and peer pressure, students internalise the values and norms of the educational setting (Barth, Dunlap, Dane, Lochman, & Wells, 2004;Wilkinson, Hattie, Judy, Townsend, Fung, Ussher, Thrupp, Lauder, & Robinson, 2000). Schools and classes consisting of students with high/low levels of achievement motivation may promote these high/low levels of achievement motivation in individual students that may, in turn, have positive/negative effects on students' academic achievement (e.g., Berndt, 1999;Chen, 1997;Ryan, 2001). Second, teachers may have higher expectations of class groups and schools consisting of students with higher levels of achievement motivation. These expectations may impact their teaching styles and teachers' behaviour and indirectly students' academic achievement. Third, in schools and classes consisting of students with high levels of achievement motivation, it may be possible for teachers to use more effective teaching styles. They may be able to cover more subject matter in highly than in poorly motivated classes, because less time is wasted on maintaining discipline and on keeping the students' attention focused. Thus, students may learn more in highly than in poorly motivated classes and schools because the time spent on teaching is higher.

Achievement motivation, gender, and academic achievement
Achievement motivation is considered as one of the crucial determinants of student achievement and academic success (Anderman & Anderman, in press). Over the past decades, the concept of motivation has been studied extensively, leading to various terminologies and diverse perspectives that emphasise different aspects of motivation, such as, the expectancy-value theory (Atkinson, 1964;Eccles, Adler, Futterman, Goff, Kaczala, Meece, & Midgley, 1983), self-efficacy theory (Bandura, 1997), self-determination theory (Deci & Ryan, 1985), attribution theory (Weiner, 1985) and goal theory (Maehr & Anderman, 1993). For an overview of the different perspectives, we refer to Pianta (2006). The present study builds on Atkinson's theory of achievement motivation (1964) that was one of the first comprehensive theories on achievement motivation that combined the constructs of needs, expectancy and value (Pintrich & Schunk, 2002). According to Atkinson's theory (1964), achievement behaviour is defined as the resultant of the emotional conflict between two tendencies, namely, achievement motivation or the tendency to approach success and fear of failure or the tendency to avoid failure. He proposed an orthogonal, two dimensional model in which individuals can be placed on high versus low on both tendencies. These tendencies are determined by a need for achievement/need to avoid failure, which are considered as relatively stable dispositions that vary between individuals, by the subjective probability of success/failure, and by the incentive value of success/failure. This means that in achievement situations, both needs are energised and together with the subjective probability of success/failure and the incentive value of success/failure they determine the (level of the) tendency to approach success and the (level of the) tendency to avoid failure. The present study focuses on the tendency to approach success or on achievement motivation. Although more recently theorists introduced an achievement goal approach to achievement motivation (e.g., Maehr & Anderman, 1993), Atkinson's theory nowadays remains very important not in the least because of its applicability within the educational setting and particularly within student coaching, such as, prevention and remediation of fear of failure.
With this study, we would like to contribute to the understanding of the role that students' achievement motivation and that of fellow students play in the explanation of academic achievement during the early years of adolescence. We will focus on language as well as math achievement and we will study the effect of achievement motivation not only on current achievement, but also on the progress students make across Grades 7 and 8. This approach is in keeping with the insight that progress (or growth) is a more important indicator of educational success and effectiveness than students' achievement at some single point in time (Teddlie & Reynolds, 2000). In an era of increased accountability of schools and teachers, it is important to realise that, for example, secondary education teachers and schools should not be held responsible for students' status at entry of secondary education but rather for the progress students make during their watch. We will investigate the effect of achievement motivation on status and growth in language and math achievement on top of the effect of students' intelligence and background characteristics, such as gender, age, socio-economic status, and language spoken at home.
We also focus on the role achievement motivation may play in the explanation of the language and math achievement gap between boys and girls. It has been well documented that girls, on the whole, have higher achievement motivation and that they -in general -show more positive attitudes towards schooling than boys. For instance, girls spend more time on their homework, show less disruptive behaviour in class, and are more concentrated in the class (Clark & Trafford, 1995;Davies & Brember, 2001;Lamb, 1997;Walsh, Hickey, & Duffy, 1999;Warrington, Younger, & Williams, 2000). Boys' less positive motivational orientations are considered as one of the crucial determinants of the explanation of boys' underachievement (Meece, Glienke, & Burg, 2006). Over the past two decades, there has been a growing concern among educational researchers and policy makers about boys' (under)achievement and attainment. Since the 1970s, girls' and women's level of educational participation and occupational status has improved considerably. Nowadays, more girls than boys enter university and colleges. Girls receive more academic honours and earn higher grades at school (Gorard, Rees, & Salisbury, 2001;Kleinfeld, 1999;Yates, 1997). Interestingly, over the past 30 years, girls continue to excel boys in reading and writing tests but the large gender gap in favour of boys in math has decreased or even closed (Cole, 1997). Research has shown that in early adolescence (i.e., middle school level), either no gender differences in math achievement or gender differences in favour of girls are found, whereas boys catch up with and overtake girls at high school and college levels (Johnson, 1996). One of the possible explanations of this age trend is the change in curriculum or math content area across secondary education. During the early years, the emphasis is on math skills, such as calculation and algebra, in which girls excel whereas during the final years of secondary education, the emphasis is more on geometry and mathematical reasoning and problem solving tasks, where boys stand out (De Corte, Janssen, Verschaffel, Knoors, & Colémont, 1999;Penner, 2003;Willingham, Cole, Lewis, & Leung, 1997). Based on these findings, we expect that girls will achieve higher in language as well in math during Grades 7 and 8. If achievement motivation plays an important role in the explanation of the gender gap in language and math achievement, we expect that by controlling for achievement motivation the gender gap in language and math achievement will be reduced or even become non-significant.

Research hypotheses
The main goal of the present study is to gain more insight into the effect of students' achievement motivation and that of their schoolmates on the status and growth in language and math achievement during the first two Grades of secondary education (i.e., Grades 7 and 8). A second goal is to examine the role of achievement motivation in the explanation of the gender gap in language and math achievement. The main research hypotheses are: 1) Achievement motivation predicts status and growth in language and math achievement across Grades 7 and 8 in addition to individual students' background characteristics and ability. 2) The achievement motivation of fellow students predicts status and growth in language and math achievement on top of individual students' achievement motivation. 3) When controlling for achievement motivation, the gender gap in language and math achievement (with regard to status and growth) is reduced or even becomes non-significant.

Data
The data used in this study originate from the LOSO-project (longitudinal research in secondary education project) that started in 1990 and followed a cohort of more than 6000 students during secondary school and afterwards (age 12-21) in Flanders, the Dutch speaking part of Belgium (Van Damme & Onghena, 2002). The sample of schools that participated in the project was selected to be representative of Flanders with regard to characteristics such as school size, school type, the curriculum offered, and the participation of both Catholic and Public schools. The students which were enrolled in these schools in the first grade of secondary education (Grade 7) for the first time in 1990 constituted the LOSO-cohort. The present study focuses on the first two Grades of secondary education, namely, Grade 7 (age 13) and Grade 8 (age 14). The data set consisted of 4340 students (2297 girls and 2043 boys) in 57 schools. Only students who stayed within the same school during Grade 7 and Grade 8, who did not repeat a Grade in the first two years of secondary education, and who were enrolled in the A-curriculum 1 were considered. We applied these data restrictions in order to control for possible confounding factors associated with students who change schools, who are retained, or who follow different curricula. Because missing data on the dependent variable can be accommodated within multilevel growth curve models, students were not excluded on the basis of missing data on language and math achievement.
-----1 In Grade 7 and 8, students can follow two different curricula, the A-and the B-curriculum. The A-curriculum is academically orientated, while the B-curriculum is vocationally orientated. The B-curriculum is the only option for students who have not been able to obtain the primary education certificate.

Language and math achievement
Language and math achievement were measured at three different points in time, namely: at the beginning of Grade 7, at the end of Grade 7, and at the end of Grade 8. Because there are no national exams in Flanders, the tests were constructed specifically for the LOSO-project. The language achievement tests were composed of curriculum-relevant multiple-choice items covering spelling, reading comprehension, vocabulary, and metaphors. All three tests showed high internal consistencies (Cronbach's α ranged from .90 to .93). The math achievement tests were composed of multiple-choice items that tapped number theory, geometry, and set theory, and showed acceptable internal consistencies (Cronbach's α ranged from .70 to .90). In order to compare the achievement scores over time, test scores were calibrated by IRT-analyses using BIMAIN (Zimowski, Muraki, Mislevy, & Bock, 1994). This resulted in IRT-scores for language and math achievement that refer to the same scale on each of the three measurement occasions.

Student-level variables
Intelligence. At the start of secondary education, the Getlov-intelligence test, which has a high internal consistency (Cronbach's α = .82) and a strong validity (see Lancksweerdt, 1990), was administered to all students. We used the global intelligence score as a covariate in the analyses.
Socio-economic status. A factor analysis was carried out on six variables that were derived from the parents' questionnaire administered at the beginning of secondary education. These variables include the educational and the occupational level of mother and father, the monthly income, and the cultural capital of the family as indicated by the number of cultural activities, such as, reading and going to the theatre. On the basis of the Scree-test of Cattell and the criterion 'eigenvalue is greater than one', one factor was selected that was interpreted as the 'socio-economic status' (SES). This factor explained 54% of the total variance and all six variables showed a high loading on the factor component. The factor scores were used as the values for the variable 'socio-economic status'.
Language spoken at home. A distinction was made between families who only speak Dutch at home (code = 1) and families who speak another language at home (code = 0). In the data set, the majority (93%) of the students only speak Dutch at home.
Gender. Gender was dummy coded with '0' for boys and '1' for girls. The data set consisted of 53% girls and 47% boys.
Age. Age was coded as '1' for students who entered secondary education at the normal age of 12. It was coded '0' for the students who had fallen behind by one or more years before their start in secondary education. Only a small percentage of the students (6%) in the data set were already behind at entry of secondary education.
Achievement motivation. Achievement motivation was measured by the Achievement Motivation test for Children, which is a Flemish adaptation of the Dutch version of Hermans (1983). Hermans (1983) defines achievement motivation as a relatively stable trait leading to the willingness to excel in achievement tasks. Although besides achievement motivation, also fear of failure and the tendency to give socially approved answers were measured by means of the same questionnaire, the present study only uses the subtest 'Achievement Motivation'. This subtest has a high internal consistency (Cronbach's α = .82) and a high validity, as indicated by the high correlation with enjoyment of studying and learning. As individual students' achievement motivation is central to the present study, we have listed in Appendix A (a translation of) the 29 items on which the achievement motivation scale is based.

School-level variables
At the school level, aggregated measures of the six student-level explanatory variables were constructed. For each school, we calculated the mean score of the six student-level variables of all the students in that school. By this means, we created a measure of the school public's intelligence, the school public's socio-economic status, the proportion of girls, of normally progressing students, and of Dutch-speaking students in each school, and finally, the school public's academic motivation. To make a distinction between the student-level variables and the school-level aggregated variables, the prefix "SCH-" is used to denote the aggregated variables.

Analytical approach
Multilevel (linear) growth curve models were estimated for math and language achievement separately using the MLwiN software (Rasbash, Browne, Goldstein, Yang, Plewis, Healy, Woodhouse, Draper, Langford, & Lewis, 2000). Three-level models were fitted, with level 1 referring to the measurement level, level 2 to the student level, and level 3 to the school level. They represent the development over time in math and language achievement. An intercept and a linear slope are allowed to vary between students and between schools. This means that for each student and for each school a linear growth curve was estimated that can be characterised by, respectively, an individual specific and a school specific intercept (i.e., status) and slope (i.e., growth or rate of change). The beginning of Grade 7 was chosen as the reference point, meaning that the intercept or status refers to the math and language achieve-ment at the start of secondary education. The linear slope represents the growth or the progress in math and language achievement of students and schools during Grades 7 and 8. Predictors at level 2 included students' background characteristics -namely: the socio-economic status, gender, language spoken at home, and age -, students' ability (i.e., intelligence), and students' achievement motivation. The aggregated school-level measures served as predictors at level 3. In order to investigate whether these predictors were associated with growth or progress in language and math achievement, the interactions between each of the predictors and the time variable were included. All non-dichotomous predictors were grand mean centred to facilitate the interpretation of the intercept (Singer & Willett, 2003). All models assumed that the measurement errors were homoskedastic and uncorrelated across time. The percentage of variance explained at the student or at the school level by including a student-level or school-level variable was used as an indicator of effect size. Table 1 and 2 present the unconditional linear growth curve model (Model 1) and the linear growth model including the effects of students' achievement motivation (Model 2), intelligence (Model 3), and background characteristics (Model 4) for language and math achievement, respectively. The unconditional growth curve model estimates the linear growth curves (i.e., initial status and rate of change or slope) for language and math achievement, the variances, and covariances at the student and school level. According to Model 1, the students made -on average -no significant progress in language achievement whereas they did make significant progress in math achievement across Grades 7 and 8 (see: the rate of change in Model 1, Table 1 and 2). Both for language and math achievement, there were significant differences in initial status and in rate of change between students within schools as well as between schools (Model 1, see Table 1 and 2). These differences were reduced considerably when students' achievement motivation, intelligence, and background characteristics were taken into account.

Effect of achievement motivation, intelligence, and background characteristics on status and growth in language and math achievement (Hypothesis 1)
In a first step, we added achievement motivation to the unconditional linear growth curve model (Model 2 in Table 1 and 2). Students' achievement motivation was significantly positively related to the initial status and the rate of change in language achievement. The same conclusion holds in the case of math achievement. But in terms of the reduction of variance components -which can be seen as an effect size measure -the effect of the latter relationship proves to be very small 2 . For language achievement, there was a small (4%) reduction of the variance in the rate of change, both at the student level and at the school level.
In a next step, students' intelligence was taken into account (Model 3 in Tables 1 and 2). It reduced the variance in initial status between students (within schools) considerably, by about three fifths (60%) for language and more than three quarters (76%) for math achievement. Moreover, the variance in initial status between schools was reduced by more than three quarters for both language (76%) and math achievement (78%). This means that students' intelligence is a strong predictor of the initial status of the language and math achievement at the start of secondary education and that schools differ in the type of students (highly intelligent or less intelligent) they attract at the beginning of secondary education. In contrast, the students' intelligence is not significantly related to their growth in language achievement. But for math achievement, there is a significant relationship between intelligence and growth: Students with lower intelligence scores made significantly more progress; by adding the intelligence score as a predictor of the rate of change in math achievement, its student-level variance was reduced by a quarter (25%), leaving the remainder not significantly different from zero (Model 3 in Table 2).
In a final step, students' background characteristics (see Model 4) were added. This reduced the student-level variance in initial status further for both language (4%) and math achievement (1%). In addition, the schoollevel variance in initial status was reduced further with 8% and 3% for language and math achievement, respectively. All the student-level background -----2 We use the amount of reduction of variance (components) to measure the size of the effect of (groups of) predictors. When we quantify the reduction of a variance component -to supplement qualitative descriptions such as 'small', 'considerable', 'important' -the size of the corresponding component in Model 1 (see Tables 1 and 2) is used as the basis for comparison. Consecutive estimates of the between-school variance of the initial status of language achievement (Table 1) A disadvantage of a description such as 'a 76% reduction of the variance' is that it may invite an overly optimistic impression of high accuracy. Bearing in mind the standard errors of the variance component estimates involved, the latter description should probably rather be interpreted as 'a reduction of the variance by three quarters'. Finally, note that other quantifications of the reduction of variance components are sometimes used, for example with as a basis of comparison the total variance or the variance components of the previous model in the sequence.  characteristics except age were significantly positively related to language achievement at the beginning of Grade 7; for math achievement at the beginning of Grade 7 only the effect of gender was not statistically significant. The students' background characteristics reduced the variance in rate of change at the student level with 17% and 13%, and at the school level with 16% and 12% for language and math achievement, respectively. This further reduction in the rate of change in language achievement can be chiefly attributed to the effect of gender and the effect of the language spoken at home whereas for math achievement, also the socio-economic status was important. As expect-   ed, girls made more progress in language and math achievement. We can conclude that achievement motivation has an independent effect on the status and rate of change in language and math achievement because it remained significant when controlling for students' intelligence and background characteristics (Tables 1 and 2). Although its effect was smaller than the effects of intelligence and background characteristics, consistent positive effects have been demonstrated.

Compositional effects of achievement motivation on status and growth in language and math achievement (Hypothesis 2)
In a first step, we tested whether the achievement motivation of schoolmates influenced status and growth in language and math achievement over and above the effect of students' achievement motivation, ability and background characteristics. The results are shown in Table 3 and 4 for language and math achievement, respectively (see Model 5). For language but not for math achievement schoolmates' achievement motivation was a significant predictor; more specifically it predicts the rate of change in language achievement. This means that students with similar ability, background characteristics, and achievement motivation make more progress in language achievement in schools composed of students with high achievement motivation than in schools with a lower average achievement motivation. The variance of the rate of change in language achievement at the school level was reduced by 14%. No significant association was found between schoolmates' achievement motivation and language achievement at the start of secondary education (i.e., status).
In a next step, we first added the school public's ability (Model 6) and then the school public's background characteristics (Model 7) to Model 5, to investigate whether the compositional effect of achievement motivation remains significant when taking into account other compositional effects. The final model (Model 7) in Table 3 shows that it does. Apart from the average achievement motivation in the school, the percentage of schoolmates having entered secondary education at the normal age was a significant predictor -with a positive coefficient -of individual growth in language achievement. Moreover, the school average intelligence proved to be positively related to language achievement at the start of secondary education. Its introduction (Model 6) reduced the school-level variance in initial status further by 9%, whereas the addition of the schoolmates' age (Model 7) reduced the school-level variance in rate of change by 4%.
With regard to math achievement, there is no evidence of a significant association between average achievement motivation in the school and the initial status or the growth of achievement in the Models 5 and 6 ( Table 4). In contrast, Model 7 -which includes the average background characteristics -suggests a negative relationship between schoolmates' achievement motivation and the rate of change in math achievement. The latter effect, however, we interpret as a statistical artefact. This point of view is supported by Model 8 (Table 4), which is different from Model 7 in that it only includes the school-level variables that were significantly related to math achievement in Models 6 or 7. In Model 8, which does not fit the data worse than Model 7 according to a deviance test (Snijders & Bosker, 1999, p. 88), the effect of    the average achievement motivation in the school is no longer statistically different form zero. We suspect that the data are not sufficiently powerful to yield reliable estimates of individual coefficients from this block of mutually correlated school-level variables -see Table 5 -, when they are introduced in an already complex model.

Achievement motivation and gender differences in trajectories for math and language (Hypothesis 3)
Finally, we investigated how achievement motivation may play a role in the explanation of the gender gap in language and math achievement. In a first step, we compared the effect of gender in two models: Model A that included all the student-level variables (i.e., Models 4 in Table 1 and 2) and Model B omitting the effect of students' achievement motivation. Because these two models only differ in the inclusion of the variable achievement motivation, a comparison of the results reveals to what extent the effect of gender on the status and rate of change in language and math achievement changes by taking into account achievement motivation. For language achievement we found that -by controlling for achievement motivation -the effect of gender on the status decreased from 0.135 to 0.126, and its effect on the rate of change from 0.158 to 0.152. Although there was a nominal increase in the effect of gender on the status in math achievement from -0.004 to -0.011, the effect of gender remained non-significant. The effect of gender on the rate of change in math achievement showed a small decrease (from 0.070 to 0.064). We can conclude that there were only very small decreases in the effect of gender on language and math achievement by controlling for achievement motivation.
In a second step, we investigated the effect of the interaction between gender and achievement motivation on the status and growth in language and math 1 2 3 4 5 6 1 SCH-intelligence 1.00 2 SCH-SES 0.88 *** 1.00 3 SCH-gender -0.06 -0.01 1.00 4 SCH-language spoken at home 0.73 *** 0.58 *** -0.08 1.00 5 SCH-age 0.93 *** 0.84 *** 0.01 0.73 *** 1.00 6 SCH-achievement motivation 0.34 * 0.39 ** 0.35 ** 0.01 0.33 * 1.00 * p < .05, ** p < .01, *** p < .001 N = 57 achievement. The effect of this interaction on the rate of change in language achievement turned out to be only marginally significant (see Figure 1), χ 2 (1, N = 4340) = 3.75, p = .053 3 , whereas no significant effects of interactions were found for the initial status of language achievement or for math achievement. Figure 1 shows that girls not only achieve more than boys with regard to language at the start of Grade 7, but that they also make more progress during Grades 7 and 8, resulting in an increase in the gender gap in language achievement across Grades 7 and 8. Moreover, the gender gap in language achievement increases more among the less than among the moderately and highly motivated students. Figure 1 clearly shows that especially boys with low achievement motivation are at risk of falling behind in language. On the other hand, weakly motivated girls do not appear to progress less in language achievement than the averagely and highly achievement motivated girls.

Discussion
The main goal of the present study was to examine the importance of the role of individual students' achievement motivation and of the average achievement motivation of their (school) peers in the explanation of status and growth in language and math achievement. Overall, some evidence was found in support of our hypotheses although the effects were more pronounced in the case of language achievement than with regard to mathematics.
The fact that evidence was found for the effect of achievement motivation of school peers on language but not -or only to a small extent -on math achievement raises questions concerning the generalization of these effects. A possible explanation of this difference is that school-level achievement motivation may only influence math achievement indirectly, through individual students' achievement motivation, whereas -in addition to a possible indirect effect -a direct effect on language achievement was found. Thus, the absence of a clear relationship between achievement motivation and math achievement in the present study does not necessarily imply that school peers' achievement motivation does not affect math achievement, but rather that there is no direct effect. As was mentioned previously (see Introduction), the compositional effect of achievement motivation on academic achievement may be mediated by three different mechanisms: (1) through its impact on individual students' achievement motivation (i.e., peer context), (2) through teachers' expectations and the use of differentiated teaching styles and practices, and (3) through the more effective time spent on teaching. Based on the findings of the present study, we can conclude that the impact of the achievement motivation of school peers on students' language achievement was not only mediated by individual students' achievement motivation because the effect of the achievement motivation of school peers remained significant when students' achievement motivation was taken into account. In other words, other factors, such as, teachers' expectations and time spent on teaching may play a role in the explanation of the compositional effect. Although it did not lie within the scope of the present article, a promising future line of research would be to investigate the mediating effects of each of these proposed mechanisms using structural equation modelling techniques in order to gain more insight into the relationship between the achievement motivation of school peers and individual students' academic achievement.
Another possible explanation of the difference between language and math in the present study may be that school peers represent an overly broad context, preventing the influence of peers' achievement motivation on math achievement to come out clearly. The achievement motivation of classmates or close friends may be stronger predictors of math achievement. Previous research has shown that factors that are proximal to the students, such as the class and the teacher, are likely to be more important than more distant factors, such as school characteristics (Stringfield, 1994). Unfortunately, no information on close friendships was available in our data. The complex task of including a class-level in the multilevel growth curve models -taking into account that the Grades 7 and 8 refer to different classes, which implies a two-dimensional (cross-classified) structure at the class level (Raudenbush & Bryk, 2002) -has not been attempted within the framework of the present study, but may represent an interesting line of future research. We decided not to include the class-level in our analyses because we argue that in the present study the contribution of each class to the progress made could not have been modelled adequately. More data points need to be gathered in order to investigate the contribution of each class (and possible class characteristics) on the current achievement and progress made. According to Singer and Willet (2003), three measurement points are needed to adequately estimate linear growth curves. In the present study, the language and math achievement were measured three times across Grades 7 and 8. This means that a linear growth curve can be estimated over these two Grades within the same schools, but that we cannot model the linear growth rate in each Grade or class separately.
Despite these limitations, the present study extends the understanding of the role achievement motivation plays in explaining achievement. In particular, it was demonstrated that individual achievement motivation has an, albeit small, effect on progress in language achievement, and that this effect persists even when intelligence and background characteristics are taken into account (Hypothesis 1). Moreover, it turned out that individual intelligence did not have any additional explanatory power on top of the effect of achievement motivation with regard to growth in language achievement. Evidence was also found for the positive compositional effect of achievement motivation on progress in language achievement (Hypothesis 2). In other words, the achievement motivation of school peers had an effect on progress in language achievement, over and above the effect of individuals' achievement motivation, intelligence and background characteristics. The effect remained intact even when the school average intelligence and the schoolmates' background characteristics were added to the model. Among these five additional characteristics of the school composition -the school public's average intelligence and socio-economic status, the proportion of girls, of normally progressing students, and of Dutch-speaking students -only the proportion of normally progressing students contributed to the prediction of growth in language achievement after the inclusion of the school average of achievement motivation. Our final hypothesis (Hypothesis 3) was not confirmed. When controlling for achievement motivation the gender gap in language and math achievement remained significant and was reduced only to a very small extent. This means that, although achievement motivation may play a role in the explanation of gender differences, it cannot fully account for the gender differences in language and math achievement. In the case of language achievement, for example, other factors such as gender differences in interest, expectations, self-concept with regard to language, perceived difficulty, and perceived usefulness of the language subject also play a role in the explanation of the gender difference in language achievement (Eccles et al., 1983).
In a further examination of potential effects of the interaction between gender and achievement motivation, a marginally significant interaction effect on growth in language achievement implies that achievement motivation is a more important predictor for the progress of boys than for girls. It also implies that the gender gap is larger among students with poorer achievement motivation and that especially boys with low achievement motivation are at risk of falling behind with regard to language achievement. Thus, the data seem to suggest that boys' achievement motivation at the beginning of secondary education may be used as an indicator of boys' future underachievement in language.
In addition, it would be interesting to investigate in a next study the effect of not only achievement motivation but also fear of failure on current achievement and progress made in order to gain more insight into the moderating effect of fear of failure on the relationship between achievement motivation and academic achievement. According to Atkinson (1964), students with high levels of both achievement motivation (AM) and fear of failure (FF) differ motivationally from students with high levels of achievement motivation and low levels of fear of failure. In fact, based on the position in the two-dimensional model (see Introduction), four different motivational patterns have been distinguished: the 'success-orientated' individuals (high AM, low FF), the 'overstrivers' (high AM, high FF), the 'failure avoiders' (low AM, high FF), and the 'failure acceptance' (low AM, low FF). Covington and Omelich (1991) found support for Atkinson's theory and showed that the achievement behaviour can be differentiated by means of these four motivational patterns. Because girls -in general -show higher levels of achievement motivation ánd of fear of failure, more research is needed in order to broaden our understanding of the effect of both tendencies (and their interaction) for girls and for boys on math and language achievement. Furthermore, the type of the tasks (i.e., math versus language) may also influence the levels of achievement motivation and fear of failure of boys and girls because of the possible gender differences in incentive values and subjective probabilities of success in language versus math achievement tasks. Also, an investigation of the effects of different goal orientations, such as, task orientated versus performance orientated on current achievement and progress made, presents an interesting future line of research as they might be differentially related to the two outcome measures (i.e., current achievement and progress made). A possible hypothesis might be that task orientated goals are more strongly correlated with progress made than with current achievement whereas performance orientated goals are more strongly associated with current achievement. Unfortunately, such a hypothesis could not be tested in the present study because goal orientations were not measured during the LOSO-project.
In summary, we have found some evidence to argue that in explaining individual progress in achievement, one should not neglect the effect of achievement motivation, both of individuals and in groups, even when ability and background characteristics such as the socio-economic status, age, sex and home language are being considered as potential factors.


extfieldsofstudy:
Sociology
Computer Science

text:
Would-Be Teachers' Competence in Applying ICT: Exposition and Preconditions for Development

Changes in educational technologies modify the role of the teacher and require new competencies. Scientific literature describes competence in using ICT as the wholeness of two structural components - basic and educational ICT competence. The conception has been considered conducting research on would-be teachers' competence in applying ICT. The article deals with the exposition of would-be teachers' competence in applying ICT in the process of teaching practice and focuses on the impact of studying informatics in forms 11 and 12 on the development of the latter competence. Research disclosed that would-be teachers had fully mastered ordinary abilities demonstrating basic and educational competencies in ICT. The development of would-be teachers' competence has been accepted as a permanent process starting yet before the studies at university within the process of learning informatics in forms 11 and 12.

Introduction
Changes in educational technologies are rotational phenomena. Variation in technological resources leads to discovering new teaching aids. In the 17th century, verbally and exemplary based educational technologies were supplemented with teaching technologies supported by the course book. In the second half of the 20th century, educational practice started using information and communication technologies. In the 21st century, the education systems are increasingly obliged to use new information and communication technologies (ICT) providing knowledge for the learners/students and exercising abilities and skills.
Teaching based on the grounds of information and communication technologies has a positive evaluation. It has been established that new ICT effectively helps with achieving educational goals and develops learners' information and communication abilities necessary for professional activity in the future (Papert, 1997;Cuban, 2001;Fuchs and Woessmann, 2004). Applying ICT allows using techniques of programmed teaching (Ozmon and Carver, 1996) and develops preconditions for producing new learning environments suitable for constructive studying (Jonassen, 1996;Coughlin, 1999;Schacter, 1999;Dexter et al., 2000;Knierzinger et al., 2002). Some researchers and practitioners are having a discussion whether a computer is efficient enough to completely change the teaching process (Becker and Anderson, 1998;Becker and Ravitz, 2001;Fulton, 1997). Obviously, ICT have the advantage in the learning process only when present and would-be teachers manage to effectively use these technologies in educational practice. The teachers successfully applying ICT these days will achieve good results working with future technologies (Hattler, 1999;Wong and Li, 2006).
Changes in educational technologies make the teacher's role different and require new competencies. The teacher's competence in information communication technologies (ICT competence) consists of knowledge, abilities, approaches, values and other personal qualities determining successful ICT use in educational practice (Ashworth and Saxton, 1991;Cuban, 2001). Competence in ICT is formed within studies at higher school, independent learning, collaboration and other activities. The preconditions for developing competence in ICT can be subdivided into two categories -formal (studying at higher/comprehensive school) and informal (studying through collaboration, independent studies, the learner's motivation, relationship between a learner and a user). The majority of the universities train would-be teachers to use ICT in educational practice. However, these educational establishments poorly concentrate on how the teachers apply ICT in educational practice and what competence in ICT they have. Therefore, feedback ensured by the students' educational practice is unfavourably received.
Teaching practice is the period that discloses competence in ICT and its quality. Following the Lithuanian model of teacher training the students are given some time for competence reflexion and correction when practice is over. The above mentioned preconditions settled the problem of research -What is the students-would-be teachers' competence in ICT and how its exposition depends on formal studies?
The goal of the article is to evaluate the exposition of the students-would-be teachers' competence in ICT in teaching practice concerning preconditions developed in formal studies.
The following tasks make the goal of research more specific: 1. What is the level of competence in ICT of the students carrying on teaching practice? 2. How does basic ICT competence of would-be teachers relate to learning informatics in comprehensive school? 3. How does educational ICT competence of would-be teachers relate to learning informatics in comprehensive school?

Background
In order to evaluate the exposition of the students-would-be teachers' competence in ICT and preconditions for its development, the structure and content of competence in ICT must be clearly defined. A part of researchers agree that successful use of ICT in educational practice depends on didactical competence, ICT literacy and ICT pedagogical competence (Andersen and Brink, 2002), the others point to some more components in-cluding pedagogy, social and ethical issues, knowledge of technology, professional improvement and organization of teaching (Coughlin, 1999;Knierzinger et al., 2002;Resta et al., 2002). The researchers from Finland emphasize that would-be teachers must study technical and pedagogical subjects that create preconditions for developing the most important components of competence in ICT including literacy and didactical preparation (Rautopuro et al., 2006). Therefore, competence in ICT embraces two structural components -ICT literacy and ICT competence in didactics. Some authors describe ICT literacy as ordinary skills in ICT being developed and improved for life (Witfelt, 2000;Li et al., 2006). Hence, ICT literacy can be treated as a dynamic rather than static component that requires renovation and expansion in order to reflect changes in the context. The didactic component of competence in ICT mainly implies the ability to adapt ICT in educational practice. The integrity of ICT literacy and the didactic component are ensured by the needs of a teacher. The flow of ICT technologies to educational practice shows how the teacher's needs may vary from training in using ICT to the need for how to learn ICT integration into educational practice (Troter and Ellison, 2001). In a number of countries, technical and didactical sides of competence in ICT are considered in teacher training. However, more frequently the use of technologies rather than integration of ICT into educational practice are emphasized in the teaching process (Rautopuro et al., 2006).
A few researchers (Shapiro and Hughes, 1996;Markauskaitė, 1999) accentuate two levels of competence in ICT -basic and extended abilities. The basic abilities are the core of ICT use consisting of computer literacy, knowledge, information and skills to operate the acquired data, knowledge of law and appropriate moral attitudes. The extended abilities fulfil the basic ones and cover knowledge and skills necessary to rationally apply information technology to different fields of science, culture and social life. A comparison of the latter researchers' approach to competence in ICT with the above introduced standpoints obviously reveals some commonalities. The component of ICT literacy conforms to the level of the basic abilities while the didactical component agrees with the extended abilities.
Competence in ICT is dynamic and dependable on the alterations in educational technologies and the factors of the teacher's work environment (Anderson et al., 2002;Li et al., 2006)). Four stages of integrating ICT technologies can be marked: introduction, application, inclusion and transformation (Anderson, 2002, Jucevičienė andBrazdeikis, 2003;Dagienė, 2004). Concerning the four stages of ICT integration, the competence structure of the teachers' ability to use ICT falls into four levels: behaviouristic, enclosed, integrated and holistic. Apart from the spread of competence in ICT into four levels, the components including basic and integral educational ICT competence remain stable (Coughlin, 1999;Andresen and Brink, 2002;Jucevičienė and Brazdeikis, 2003).
The structure of competence in using the two components (basic ICT literacy and integral educational ICT) is disclosed in the standards of using ICT by the teachers: ISTE (International Society for Technology in Education, 2000) and Teacher Computer Literacy Standard (2001).
The conclusion of foreign researchers' approaches (Ashworth, and Saxton, 1991;Shapiro and Hughes, 1996;Anderson et al., 2002;Andresen, 2002) towards ICT content and structure drawn by Lithuanian investigators (Urbonaitė, 1999;Jucevičienė and Brazdeikis, 2003) suggest defining competence in ICT as the wholeness of basic and integral educational ICT competence. Basic ICT competence comprises the ability to use and manipulate ICT, be informationally literate and knowledgeable about how to apply social, ethical and legal issues concerning ICT. Integral educational ICT (educational ICT) competence covers the ability to apply ICT in educational practice and to develop learners' computer literacy including the ability to plan, operate and analyse the processes of applying ICT. The research discussed in the article is based on the conception of the content and structure of competence in ICT.
The foreign researchers have investigated would-be teachers' competence in ICT within teaching practice from different angles: collaboration, learning experience, trust etc. When examining experience of the would-be teachers-mathematicians Robert Powers et al. (2005) points to high level of the students' knowledge and ability to use technologies as a teaching tool. Successful collaboration allows the teachers-mentors to broaden experience working with the students would-be teachers within teaching practice. Eunjoo Oh and Russell French (2002) states that successful ICT application depends on preparation of both the present and would-be teachers. Would-be teachers must possess practical skills at using computer technologies in the processes of teaching and learning. When dealing with information technologies, knowledge as well as teachers' reliance on possibility of effective use of the obtained information must be taken into consideration. An examination of the PISA (Programme for International Student Assessment) research in Finland performed by Rautopuro et al. (2006) shows that positive results can be directly related to applying ICT in educational practice. The researchers presume that in order to make would-be teachers engaged and ready for using ICT at work more attention should be devoted to their learning experience. The results presented by the Finish researchers indicate that such qualities as 'motivation and precision', 'relationship between the learner and the user' and 'supervision for the learner and independent learning' should encourage would-be teachers to apply ICT at work.
The review of literature sources shows that competence in ICT and development preconditions have been examined from different angles. However, we focused on another precondition for developing competence in ICT which is learning informatics in comprehensive school.

Research Methodology
Research methodology is based on the concept of meaningful learning. Jonassen (1999) maintains that meaningful learning can be related to constructivism, reflectivity, collaboration, activity, intentionality and contextuality. The methodological approach of research described in this article refers to constructivism and reflectivity. Considering this position, the learner should extend a new knowledge on the basis of already available information. In this case, reflexion is extremely important. To conform to the new situations of studying, a learning environment should help the students with self-evaluation of studies and previously acquired knowledge (Jonassen, 1999;Jonassen et al., 1999).

Sample and Procedure
The article looks at students' competence in ICT on the basis of students' reflexion. The article discusses the survey data on would-be teachers' competencies in ICT. The above introduced extensive research was conducted in Vilnius Pedagogical University and was targeted at evaluation of pedagogical competencies gained by the students within teaching practice. The students' competencies revealed along practice were evaluated by the learners taught, the students themselves and the teachers-mentors who were monitoring the process of teaching practice.
The model of the survey was chosen and the sample was provided applying the methods used in IEA international research (International Association of the Educational Achievement). To carry on research, a random students' sample was used. The principle of sampling is a random cluster sample based on randomly chosen students' groups when a sample comprises all the students of a particular group rather than single respondents. The students from different departments participated in the survey, i.e., the respondents studying exact (mathematics, informatics), natural (physics, chemistry and biology), social, technological sciences and the humanities were surveyed one month after teaching practice had been over. 900 bachelor's degree students in total were questioned. Hence, the research sample is reliable and presentable.
To evaluate competence in ICT of the students who were carrying on teaching practice quantitative research was applied. The questionnaire was accepted as the instrument of the survey. In order to establish competence in ICT, two sections of integrated questions were introduced. Section 1 included questions about basic ICT competence (computer literacy). The answers had to be provided along with self-evaluation of basic ICT competences displayed within teaching practice. These competencies embrace a wide spectrum of technical ICT activity: from worksheets in the classroom to educational software, personal websites on the Internet and organization of remote learning. Obviously, these activities require different level of basic ICT competences. The questions included in the questionnaire considered these issues, and therefore the students had to evaluate different abilities making basic ICT competence (Table 1).
Section 2 contained questions discussing educational ICT competence based on educational experience at different level and covered the ability to apply visual static educational material in the classroom, to employ active methods of teaching, to create problematic situations in the classroom using ICT etc. (Table 4).
To evaluate competence in ICT, the rank scale of measurement and three evaluation ranks 'succeeded', 'partly succeeded' and 'failed' were introduced. Technical preconditions were considered to be required elements of showing competence in ICT. Therefore, along the three evaluation ranks describing competence, an extra answer 'missed' was added. The latter version was chosen by the respondents who had no technical possibilities of applying ICT in teaching practice. Moreover, this answer was popular among those who though having technical preconditions did not employ them.
Research revealed two formal preconditions for developing competence in ICT: learning informatics in secondary school and studies of developing competence in ICT in higher school. The learners of Lithuanian upper secondary school (Forms 11 and 12) can choose between an advanced (higher level) and general (lower level) level of the course on informatics. The older part-time students who graduated from secondary school some time ago might not be introduced a course on informatics. 281 students learnt informatics at advanced and 455 -at general level whereas 164 students did not study informatics in secondary school. Under the available regulations of teacher training, teaching practice is submitted in the fourth (last) year of bachelor studies. Thus, a three year period goes past since the last studies of informatics in comprehensive school.
The second precondition for developing competence in ICT covers studies in higher school. The certain modules of developing competence in ICT are included in the curricula of studies at all departments of Vilnius Pedagogical University. The modules are under the obligation law About Teacher Computer Literacy Standard passed by the Minister of Education and Science of the Republic of Lithuania and must take from 2 to 4 credits. A particular amount of hours is devoted to perform individual tasks.

Research Framework and Data Analysis
To process research data, descriptive statistics was applied. In order to evaluate statistical significance of different positions, the percentage of the answers was calculated. The χ 2 (chi square) test was used to examine homogeneity of population. The chosen level of significance (α) was 0.05. If the calculated p-value 0.05, the difference is statistically significant. Otherwise, it is statistically insignificant.
To statistically process data of research, the statistical packet SPSS 13 was applied.

Basic ICT Competence of Would-Be Teachers and Preconditions for Development
The fundamentals of basic ICT competence include the abilities of using ICT and information abilities. They are supplemented with knowledge of social and ethical laws, the rules of law and the ability to follow them in ICT educational practice. To prepare the questionnaire, the abilities to use ICT were specified considering their level of difficulty. The respondents had to show more or less complex abilities to use ICT in practice: working with text for schooling purposes using ICT, producing visual (static, dynamic, audio equipped etc.) educational material, designing educational software etc. (Table 1).
Research data (Table 1) indicates that within teaching practice would-be teachers succeeded in finding relevant texts, partly succeeded -in producing static visual material, using electronic databases for educational purposes and using the Internet for effective communication, i.e., they managed to implement the activities requiring ordinary abilities which are the fundamentals of basic ICT competence.
A number of the respondents did not produce visual, dynamic or audio-equipped educational material (63.1%), educational software (95.8%), use the Intranet (90.2%), deal with remote learning (99.3%) or create personal websites (97.0%). The evaluation of the latter result points to two crucial factors -lack of competence in ICT and poor technical basis. Stimulating the process of remote teaching and using the Intranet are clearly limited due to technical preconditions. However, the possibilities of technical preconditions that could help with producing visual educational material, developing educational material, creating personal websites on the Internet were offered all respondents having an opportunity to use computers. Therefore, supposedly the implementation of these activities was determined by insufficient basic ICT competence. Relation between basic ICT competence and the format of learning informatics in forms 11 and 12 was examined. 4 of 11 cases indicate that statistically significant deviation (p < 0.05) between basic ICT competence of the respondents who learnt informatics in school at advanced level and those who studied the subject at general level or did not study at all was established (Table 2). In all other cases of the survey, concerning the format of learning informatics in forms 11 and 12, statistical deviation between basic ICT competence was found insignificant (p > 0.05). The previous data should be considered along the interpretation of the results obtained (Table 1). No statistically significant deviation was noticed because nearly all surveyed respondents (90.2%-99.3%) did not implement such activities as designing educational software, creation of personal websites on the Internet, the use of the Intranet and possibility of remote teaching. Some abilities to operate basic ICT competence (working with text for schooling purposes using ICT, the use of the Internet for the purposes of efficient correspondence, the use of electronic databases) do not relate to the format of learning informatics in forms 11 and 12 (p > 0.05). Thus, the respondents who studied informatics in forms 11 and 12 Table 2 Basic ICT competence of would-be teachers and the format of learning informatics: statistical significance of deviation at advanced level and those who did not or learnt the subject at general level equally succeeded in working with text for schooling purposes using ICT and using the Internet for the purposes of effective correspondence and creation of electronic databases. The above introduced abilities demonstrating basic ICT competence are not complex, easy mastered and can be developed through professional activities as well as through communication and collaboration in daily life. The evaluation of the following abilities, the constituents of basic ICT competence including produced visual static and dynamic (animation and audio-equipped) educational material using ICT, and the use of the Internet to find required information disclosed statistically significant deviation (p < 0.05). To interpret the motives of this statistically confirmed deviation, the data of research was grouped considering the format of learning informatics in forms 11 and 12 (Table 3). According to the figures, the respondents who studied informatics in forms 11 and 12 at advanced level better applied ICT to produce visual static, dynamic and dynamic audio-equipped teaching material.
Statistically significant deviation was established only when examining more complex abilities demonstrating basic ICT competence, i.e., the abilities requiring a broader knowledge of informatics (Table 2). Thus, it is supposed that learning informatics in forms 11 and 12 have an impact on developing the abilities that need a wider knowledge of informatics.

Educational ICT Competence of Would-be Teachers
Educational ICT competence can be defined as the ability to use ICT in educational practice and to develop learners' computer literacy and capability of planning, manipulating and analyzing the processes of ICT application. This research examines the abilities of using ICT in educational practice (Table 4). The abilities to plan, manipulate and analyze were not investigated. The abilities demonstrating educational ICT competence were studied from three angles including application of teaching aids, teaching techniques and evaluation of learners' achievements. The abilities to apply teaching aids were specified considering the format of a teaching aid: presentation of visual educational material using ICT, the use of educational software, the Internet and enriched learning environments. The application of teaching methods was also clearly defined and embraced traditional (teacher-centred) and active (learner-centred) methods of teaching.
Research revealed that within teaching practice, the students managed to employ ICT in the classroom and made visual presentations using computer assistance, applied educational software, the Internet, used enriched learning environments. Moreover, when evaluating knowledge of the learners would-be teachers used ICT applying traditional and active methods of teaching. However, less than one third of the respondents succeeded in applying ICT in the classroom. More than a half of the surveyed participants missed an opportunity of using educational software (54.8%), the Internet (63.1%) and enriched learning environments (71.1%) in the classroom. It should be stressed that a similar percentage (56.7) of the respondents did not use ICT when applying traditional (teacher-centred) and active (learner-centred) methods of teaching. Educational ICT competence of the students is improved in pedagogical university going into the modules developing ICT competencies and being involved in teaching practice. Moreover, educational and basic ICT competences are coherent (Anderson et al., 2002). The format of learning informatics in forms 11 and 12 has the impact on developing extraordinary abilities demonstrating basic ICT competence. Considering the situation, an investigation into relation between educational ICT competence and the format of learning informatics in forms 11 and 12 was carried out ( Table 5).
4 of 8 cases show that educational ICT competence has relation with the format of learning informatics in forms 11 and 12. Statistically significant deviation between positions of the respondents who learnt informatics in school at advanced level and those who did not learn the subject or studied it at general level was established. Statistically significant deviation was discovered evaluating the following abilities demonstrating educational ICT competence: presentation of visual educational material produced by the students using computer assistance, using enriched ICT learning environments in the classroom (multimedia, hypertext), applying ICT in the classroom using traditional and active methods of teaching. It should be underlined that statistically significant deviation was noticed evaluating abilities that demonstrate educational ICT competence and require extraordinary abilities demonstrating basic ICT competence. Table 5 Educational ICT competence of would-be teachers and the format of learning informatics: statistical significance of deviation

Conclusions
The assessment of literature sources has disclosed a complex structure of competence in ICT the major constituents of which are basic and educational ICT competencies. These components are integrated by the teachers' needs that may vary from knowledge of how to operate ICT to the abilities to apply them in educational practice. The would-be teachers are good at ordinary abilities demonstrating basic ICT competence. They succeed in working with text, producing visual educational material, using electronic databases. However, they lack the extraordinary abilities demonstrating basic ICT competence, i.e., knowledge of how to design educational software and personal websites on the Internet need to be improved. Furthermore, within teaching practice, the students should more efficiently use the Internet for the purposes of searching for educational material and effective collaboration.
Teaching practice revealed educational ICT competence of would-be teachers including the ability to produce visual material using computer assistance and to design educational software. Nevertheless, teacher training must increase their abilities to work with enriched learning environments in the classroom and apply ICT employing active and traditional methods of teaching.
Research has confirmed that development of would-be teachers' competence in ICT is a permanent process that starts yet until the studies at university. The existing practice of teacher training nationwide shows that a three year period passes from the start of learning informatics in forms 11 and 12 at secondary school to the first professional activity -teaching practice. This period of time is important due to different factors that determine development of competence in ICT: formal and informal studies, learning motivation and experience, previously acquired skills, relationship with studying, self-help and other factors. The development of the respondents' competence in ICT was not separated from these factors, i.e., we researched a real phenomenon of ICT competence development rather than a simplified model isolated from the environment factors. Our study showed the impact of learning informatics in forms 11 and 12 on the development of basic ICT competence. It has been established that learning informatics in secondary school is important to the development of extraordinary abilities demonstrating basic ICT competence (visual static educational material, visual dynamic (animated) educational material, producing visual dynamic audio-equipped educational material using ICT, using the Internet for the purpose of searching for educational material). This conclusion reflects the objectives of teaching informatics in national secondary school (forms 11 and 12). The teaching goals focus on educating a person able to improve skills at information technological activity and on developing general abilities to work.
The relationship between the development of educational ICT competence and the format of learning informatics in forms 11 and 12 cannot be unambiguously reasoned. When teaching informatics in secondary school, the educational aspects of applying ICT are not considered. Therefore, a direct relationship is impossible. However, in some cases, it has been found out that successful ICT application in educational practice is determined by basic ICT competencies. Research proved that the students who learnt informatics in forms 11 and 12 at advanced level more successfully applied visual educational material produced using computer assistance, enriched ICT learning environments, traditional and active methods of teaching. Research data confirms the integrity of the structural components (basic and educational ICT competencies) of competence in ICT. P. Pečiuliauskienė is an associate professor in the Department of Education Science at Vilnius Pedagogical University. She lectures on teaching methodology and multimedia instructional development and implementation. Her research interests include improvement of natural science didactics, application of informational technologies in educational practice of natural science subjects as well as distance teaching of physics.
M. Barkauskaitė is the head of the Department of Education Science at Vilnius Pedagogical University, a member of Vilnius Pedagogical University Senate, the Chair of the Council of the Faculty of Pedagogy and Psychology as well as the editor-in-chief of the scientific journal "Pedagogika". Her scientific interests include important issues related to teacher training and application of information technologies in educational practice.