Learning Behavior and Intelligence as Explanations for Children's Scholastic Achievement

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Abstract

This study assesses the unique and complementary ability of childhood intelligence and learning-related behavior to explain variation in achievement outcomes. Teacher-observed classroom learning behaviors and individually administered intelligence and achievement test performances were collected for a representative national sample (N = 1,100) of students ages 6–17 years. The sample was blocked for age, grade level, and gender, and stratified according to the U.S. Census by race/ethnicity, parent education level, national region, community size, family structure, and educational placement. Teacher-assigned grades were collected for a secondary national sample (N = 420). Hierarchical regression models revealed substantial proportions of assigned grade variance explained primarily by learning behavior and achievement test score variance explained by intelligence. Explanatory patterns remained consistent after control for demographics and alternative intellectual or behavioral variation, and the variance explained jointly by learning behavior, intelligence, and their interactions exceeded appreciably the contributions of any one source. Implications are discussed for educational assessment and intervention.

Section snippets

National cross-sample

A representative cross-sample (N = 1,100) of schoolchildren ages 6–17 was drawn from the national standardization samples of the LBS (McDermott et al., 1999) and the Differential Ability Scales (DAS)(Elliot, 1990). The cross-sample was matrix-blocked for gender, age, and grade level and stratified proportionately via the U.S. Census (U.S. Department of Commerce, 1992) according to race/ethnicity, national region, parent education level, family structure (two parents, single mother, single

Results

As preliminary analyses, zero-order correlations were computed between GCA and the LBS dimensions. The maximum overlap between intelligence and learning behaviors was 15.2%, indicating that approximately 85% of their variance was unique and supporting the underlying premise that intelligence and learning behavior are essentially nonredundant constructs. Table 2 presents results of the various setwise regression analyses, in each instance displaying the percentage of explained student

Discussion

Recent amendments to the IDEA require the use of technically sound instruments that assess the relative contribution of cognitive and behavioral factors (Brief Synopsis, 1997). This study addresses the relative import for scholastic performance of student learning behavior and intelligence. Apart from performance variability that otherwise might be linked to unalterable genetic or relatively unalterable environmental factors, learning behavior alone is able to account for appreciable variation

Conclusion

One important goal of psychological assessment is estimation of student progress as it is likely to transpire without the benefit of intervention. Intelligence tests certainly play a major part in this effort. Still another important goal is the discovery of relevant phenomena that not only afford reasonable estimation of academic performance in lieu of intervention, but also further identify potentially alterable behaviors that will inform the interventions themselves. The evidence suggests

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