Beginner Pre-Service Special Education Teachers’ Learning Experience During Practicum
Examination of the Modification indices MI did not give any suggestions to modify the measurement model. As the adequacy of the measurement model was supported by parameters estimates, the directions of the estimates were theoretically justifiable. In other words, the three latent variables in the measurement model, namely, g, analytical, and environment are theoretically represented by their constructs. Many scholars such as Cattell and Cattell (1960) identify the components of the fluid intelligence as the ability of classification, deductive reasoning, inductive reasoning, and manipulate conditions (topology). Further, Carroll’s (1993) conception of high order intellectual abilities (stratum II) are consisted of the sequential reasoning (premises or conditions to conduct one or more steps of reasoning to draw a conclusion), induction (to find out the rules that direct the similarities or contrasts), quantitative reasoning (using concepts including mathematical relations to reach a correct conclusion), Piagetian reasoning (abstraction), visualisation (to manipulate visual patterns), and originality/creativity (original verbal/ ideational responses). And for the environment latent variable, numerous scholars (e.g. Bloom, 1985; Csikzentmihalyi et al., 1993; Winner, 1996; Feldman, 1986) present gifted’ environment in terms of family, peers, teachers, school, society, and resources which were confirmed in the measurement model as one latent variable named environment.
Assessment of Model Adequacy for the Competing Model
The competing model has been analyzed using Amos V.16 with Maximum Likelihood Estimation (MLE) as shown in Figure 2. Table 4 shows the results for Goodness-of-Fit Indices (GFI) for the competing model.
Table 4: Results of Goodness-of-fit Indices of the Competing Model
Goodness of fit indexes
The model adequacy has indicated that a statistically fit structured model with root mean square error of approximation (RMSEA) = .050 (<.08), comparative fit index (CFI) = .936 (> .90), Tucker-Lewis index (TLI) = .923 (> .90) and the overall good fit index (GFI) = .906. Moreover, the chi-square statistics of ( = 118.819, df = 87, P = .063) and relative chi-square (CMIN/df = 1.366) which fell below the threshold point of 3.000 as suggested by Kline (2005).
Table 5 shows the elaborated evaluation of the competing model parameters. All factor loadings were significant with CR > ± 1.96 and all the error variance were < 1.0 indicating that there was no violation of estimates revealed. The direct effect of environment on g was 0.530, environment on analytical abilities was 0.488, and g on analytical abilities was 0.285. All direct effect were significant paths (CR > ± 1.96).
As the paths coefficients > 0.20, the effects of the environment are considered important to the analytical abilities. Further, the direct relationship between the environment and the analytical abilities was significant (CR > ± 1.96, p < 0.05) with path coefficient of 0.448. As the path coefficient > 0.20, the effect of the environment is considered important to the analytical abilities. On the other hand, the indirect effect of environment on the analytical abilities through the g was 0.151. The total standardized effects for environment on the analytical abilities was 0.639 and on g was 0.530, the total standardized effects for g on the analytical abilities was 0.285. These results indicated that the external nurturing factors embodied in Environment had strong effects on the analytical elements of the intellectual giftedness.
Figure 2: The Competing Model with the Factor Loadings
Table 5: Summary of the Direct and Indirect Effects, Total Effects, Standardized Error, and
Critical Ratio of the Competing Model
Standardized Direct Effects
Standardized Indirect Effects
Standardized Total Effects (Direct Effects + Indirect Effects)
The percentage of variance (SMC) explained range from 0.129 or 12.9 % (Artificial language) to 0.593 or 59.3 % (Pattern Recognition). The amount of variance associated with g accounted for 0.281 or 28.1 % by its predictors, namely, CCFT series, CCFT matrices, and CCFT topology. The amount of variance associated with the analytical abilities accounted for 0.467 or 46.7 % by its predictors, namely, problem solving, pattern recognition, audio-logic, artificial language, and audio-matrices as shown in Table 6. SMC results indicated a strong relationship between the variables’ constructs and their factors and demonstrate the greater explanatory power of these factors in predicting the intellectual giftedness. Examination of the modification indices (MI) did not give any suggestions to modify the competing model. As the adequacy of the competing model was supported by parameters estimates, the directions of the estimates were theoretically justifiable.
By examining paths coefficients among the latent variables in the competing model, one latent variable revealed a strong bond among them, namely, environment, g, and analytical abilities. This bond was supported by calculating the direct and indirect effects among these variables. The direct effects were: environment to g = 0.53, environment to analytical abilities = 0.49 and g to analytical abilities = 0.29. The indirect effects were: environment to analytical abilities = 0.488 as shown in Table 3. This bond was named analytical giftedness; the existence of this bond was due to the crucial roles played by environment to crystallize these compounds (g and analytical abilities). This role is mediating by g platform which supports the analytical abilities to be maximized.
Discussion and Conclusion
The findings of this study are consistent with Gagne’s (1985) DMGT model, Tanenbuam’s (1991) Star model, and Sternberg’s (1985) Triarchic model. The beauty of the current findings stand on counting the contribution of environment factors (family, peers, teachers, school, society, and resources) to the development of the intellectual giftedness. No study utilized multivariate analysis using SEM to investigate the interrelationships of the environment factors, the fluid intelligence, and intellectual giftedness. Further, the concept of giftedness and talent is now varied based on the incubating environments of the intellectual giftedness. However, the previous models of giftedness and talent (e.g. Gagne’s (1985) model) describe giftedness in terms of high ability and talent as high performance, while
Table 6: Maximum Likelihood Parameter Estimates of the Standardized Factor Loadings, Standard Error, Critical Ratio, and Squared Multiple Correlation for Measurement Model
it can be redefined by prescribing these concepts in depth showing how a specific series of aptitudes combined in a certain way to establish g , analytical abilities, or any other areas of giftedness and talent. Further, the foundation of talent can be redefined by having these compounds connected to internal and external factors, namely motivation and environment to establish the bonds as talent foundation. This conceptualization is consistent with Cattell-Horn-Carroll (CHC) model (McGrew, 1997) as crystallized intelligence extends the fluid intelligence capabilities by having a context, which encompasses motivation and environment factors.
Environment as a nurturing tool plays a crucial role in the development of giftedness to become a distinguished talent. The evaluation of the competing model parameters in terms of the direct, indirect, and total standardized effects gave a significant direct effect of environment on g and analytical abilities. These results are consistent with Bloom (1985) and Carlson (1993), in which talent development is supported by several factors such as good teachers, potential support, sport clubs, socialization, playful activities with guidance, support from parents, and stimulation of interest. Environment gives the gifted opportunities to manifest gifted potentials and to supports individuals’ aptitudes to be nurtured through various interfaces. Additionally, giftedness requires social context that allows individuals’ abilities to be flourished. The analytical abilities are affected by the environment factors, which is consistent with Tannenbaum (1991) that child’s environments such as family, peers, school, and community, beside the social, economical, and political institutions can help to determine the field of talent.
The family factor loading in the structural model was significant (CR > ± 1.96, p < 0.05), and this highlights the important role of parents as emphasized by Bloom (1985) in his studies as the role of families is vital in nurturing individuals’ talents and parents offer efforts to support their talent development. The Environment’ items developed in this study followed the theoretical foundations of the role of parents proposed in the literature. For example, Winner (1996) suggests that parents tend to set high standards for their talented children rather than their emotional and financial support, while Bloom (1985) emphasized that parents support their children to challenge, to strive for increasingly higher levels of achievement and to evaluate the success of their performances. Zimmerman and Ringle (1981) demonstrate that talented children's levels of achievement and personal ambitions are affected by the goals parents set for them. According to Csikzentmihalyi et al. (1993), the best environment for cultivating talent challenging is provided by supportive families. Bloom (1985) and Winner (1996) reported that the behaviours parents’ model influences children's talent development which is consistent with results of this study.
The peer factor loadings in the structural model were significant (CR > ± 1.96, p < 0.05) and this indicates that the important role of the peers in giftedness development. This result is consistent with Ryan (2001) in which the competitive and supportive peer groups can serve to promote the intrinsic value of school and the educational process in its members. Also it is consistent with Bloom (1985) and Guimond (1999) as children's peers support the development of talent and have an influential effect on attitudes and concepts. Sichivitsa (2004) found peers to play a significant role in shaping students’ values and attitudes toward music. The teachers factor loading in the structural model was significant (CR > ± 1.96, p < 0.05), and this indicates the important role of teachers play in the development of giftedness, which in turn consistent with Bloom (1985), Csikzentmihalyi et al. (1993), and Sichivitsa (2004) who found that teachers play a crucial role in improving both academic and social integration of their students, parental support of music and the amount of previous musical experience had a significant positive impact on college students’ self-concepts in music.
A multivariate analysis employing the structural equation modelling (SEM) to explore the simultaneous interconnections and relationships between fluid intelligence, analytical abilities, and environmental factors was used in this study. The main focus of this study was to investigate how these factors interacted so that the administration of the gifted and talented education and the role of parents and other environmental factors can be enhanced. The findings of this study indicated that the availability of supportive environments promoted higher analytical abilities and suggested that environmental factors were integral and significant variables in the further development of gifts and talent. Thus, these findings provide support for the belief that with proper environmental scaffolding everyone can be talented and these findings can be helpful for planning and conducting the identification and nurturing processes of gifted and talented individuals. However, more studies that explore the characteristics of the environmental factors in promoting other intellectual, emotional, and psychomotor intelligences are recommended.
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