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30-07-2024 | Empirical Research

Intelligence Mindsets Trajectory Profiles among Chinese Elementary School Students: Associations with Changes in Academic Achievement from Grades 4 to 6

Auteurs: Cai Qi, Xiaolin Guo, Chunhui Liu, Yue Li, Baoxu Zhao, Tiantian Bi, Liang Luo

Gepubliceerd in: Journal of Youth and Adolescence | Uitgave 1/2025

Abstract

Early adolescence is a critical period for the development of children’s intelligence mindsets, which play a significant role in academic achievement. However, existing research predominantly employs variable-centered approaches, which fail to capture individual differences in mindset–achievement relations. This research addresses this gap by adopting a longitudinal person-centered approach to explore the joint developmental trajectories of growth and fixed mindsets among early adolescents. It further explores how these trajectories relate to changes in academic achievement (i.e., the mean of standardized mathematics and Chinese achievement test scores) over 2 years, accounting for intelligence and sociodemographic factors such as age, sex, and family SES. In two five-wave longitudinal studies with 748 (M_age = 10.23 years, SD = 0.30; 49% girls at T1) and 3258 (M_age = 10.34 years, SD = 0.37; 49% girls at T1) Chinese elementary school students from grades 4 to 6, four distinct mindset trajectory profiles were identified: Growth (initially high growth but low fixed mindsets), Fixed (initially high fixed but low growth mindsets), Moderate (initially moderate levels in both mindsets), and Both-High (initially high levels in both mindsets). Analysis across both studies revealed that students in the Growth trajectory profile exhibited the most significant improvements in academic achievement 2 years later. Conversely, students in the Both-High trajectory profile experienced the least favorable academic outcomes. These findings highlight the importance of recognizing individual differences in mindset trajectories and their potential impact on academic outcomes. The current research underscores the need for educational interventions that are tailored to different mindset profiles to optimize student development and achievement.

vorige artikel Early Mother-Child Interaction Flexibility Predicts Adolescent Psychological Adjustment

volgende artikel Longitudinal Relations among Self-Compassion, Self-Esteem, and Depressive Symptoms in College Students: Disentangling the Within-Person Process from Stable Between-Person Differences

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The Beijing and Hebei samples were analyzed separately for the following reasons. First, the two samples come from different regions: one is from an urban area in Beijing (Study 1), the capital of China, while the other is from a rural area in northern China (Study 2). The overall economic and educational development levels of these two regions differ significantly (detailed SES data for both studies are available in the online supplements of Table S3, Figs. S2, and S3). Second, the family SES backgrounds of the two samples differed. For example, in Study 1, the majority of participants’ parents had college or university degrees (approximately 62%), whereas in Study 2, the majority of participants’ parents had educational levels up to middle school (approximately 82%). The average annual family income in 2016 of two samples also varied markedly: in Study 1, it ranged from $15,060 to $30,120, which was substantially higher than the $4,518 to $7,530 range reported in Study 2. Third, the measurement tools varied: the Beijing sample included measures of students’ IQ, which were controlled as a covariate in Study 1, whereas the Hebei sample did not incorporate these measures.

Robustness analyses were conducted using various subsamples (involving students who participated in at least three or four waves) to address potential biases due to missing data. The results (i.e., the trajectory profiles and their links with changes in academic achievement) remained consistent across samples (see Tables S8 to S11 of the online supplements for details).

Robustness analyses were conducted to assess the impact of different data processing methods on the BCH results. Specifically, two approaches were compared: direct data importing into Mplus without EM imputation for BCH analysis and incorporating EM imputation in SPSS before data importation into Mplus for BCH analysis. The results obtained from these two methods were consistent, with no significant differences observed.

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Titel: Intelligence Mindsets Trajectory Profiles among Chinese Elementary School Students: Associations with Changes in Academic Achievement from Grades 4 to 6
Auteurs: Cai Qi
Xiaolin Guo
Chunhui Liu
Yue Li
Baoxu Zhao
Tiantian Bi
Liang Luo
Publicatiedatum: 30-07-2024
Uitgeverij: Springer US
Gepubliceerd in: Journal of Youth and Adolescence / Uitgave 1/2025
Print ISSN: 0047-2891
Elektronisch ISSN: 1573-6601
DOI: https://doi.org/10.1007/s10964-024-02061-z

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