This study uses personality and psychology health characteristics of high school students as intermediary variables to study how cognitive ability affects academic performance, and analyzes memory, information processing, presentation, logical reasoning, and thinking transformation ability in high school students. In this study, the structural equation model was used to analyze the mediating effect, and the bootstrap method was used to test the significance of the mediating effect. The participants were 572 high school students from Beijing, China. They completed a survey (...) that included questions on cognitive ability, personality characteristics, and psychology health. This study uses structural equation modeling for mediation analysis. Through the analysis of four models of comprehensive academic performance, Chinese academic performance, mathematics academic performance, and English academic performance, the results of the study showed that cognitive ability has a significant effect on academic performance, and personality characteristics and psychology health play a partially mediating role between cognitive ability and English academic performance. The mediation effect is about 40%. (shrink)

In this study, cognitive ability was classified into memory ability, representational ability, information processing ability, logical reasoning ability, and thinking conversion ability, and analyzed the effects of these five ability values on academic achievement. Structural equation modeling was used to analyze the moderating effect of Self-monitoring between cognitive ability and Academic Achievement, using students’ Self-monitoring as moderating variables. The results of the study showed that cognitive ability can significantly and positively affect academic achievement, while Self-monitoring can significantly moderate the effect (...) of cognitive ability on academic performance, with a significant moderating effect on math subjects and English subjects among achievement subjects, and the higher the value of cognitive ability, the stronger the moderating effect. (shrink)

Background: Previous studies assessing students’ learning outcomes and identifying contributing factors have often dwelt on the cognitive domain. Furthermore, school evaluation decisions are often made using scores from cognitive-based tests to rank students. This practice often skews evaluation results, given that education aims to improve the three learning domains. This study addresses this gap by assessing the contributions of four students’ input to their cognitive, affective and psychomotor skills (CAPs). Methods: A cross-section of senior secondary class II students (n = (...) 870), sampled through the multistage procedure, participated in a physical survey. Students’ Inputs Questionnaire (STIQ) and Learning Outcomes Questionnaire (LOQ) were used for data collection. Based on data obtained from a pilot sample (n = 412), principal axis factoring (PAF) was performed to assess the internal structure of the instruments following an oblique rotation. The KMO value of sampling adequacy were 0.88 and 0.94, while the Bartlett’s test of sphericity were significant χ2 (253) = 5,010; p < 0.001 and χ2 (105) = 3693.38, p < 0.001 for the STIQ and LOQ, respectively. Confirmatory factor analysis was used to assess the models’ acceptability based on the maximum likelihood estimation technique. The main study used hierarchical linear regression for data analysis. Results: Findings indicated that innate ability, health, motivation and social capital relatively and cumulatively predicted students’ overall, cognitive, affective and psychomotor learning outcomes. The proportion of variance explained by the predictors increased at different levels of the models with the addition of new variables. Students’ social capital reduced the effect of students’ innate ability regardless of their motivation and health status. Conclusion/implication: This study has provided evidence that the four students’ inputs are crucial predictors of their learning outcomes in the three domains. This result is helpful for school management to provide services aimed at improving the school climate for students’ motivation and social capital. The result can provide policymakers with a proper understanding of the constituents of learning outcomes and how policies can be aligned to secure quality student inputs for maximum productivity in education. (shrink)