关键词: 学习投入, 变迁, 多层线性回归, 线性元回归模型, 横断历史元分析

Abstract:

Grounded in a sociocultural perspective on learning engagement, combining human capital theory, this study investigated the longitudinal development of student learning engagement in China’s mainland. By incorporating two complementary sub-studies, the research systematically analyzed how three broad categories of societal factors—economic (GDP, Gini coefficient, urban unemployment rate), educational (government spending on education), and internet (internet penetration rate)—influence student engagement levels over time.

Sub-study 1 employed a cross-temporal meta-analysis of 406 empirical studies, encompassing a cumulative sample of 393,117 participants. The results revealed a significant and sustained upward trend in Chinese students’ learning engagement over the past 19 years (β=0.46, 95% CI [0.21, 0.71], p＜0.001). Although engagement levels temporarily dipped in 2020—likely due to the unprecedented disruptions caused by the COVID-19 pandemic—this decline was short-lived, with engagement rebounding and continuing its upward trajectory. Effect size analysis supported this trend, showing medium to large increases in overall engagement (Cohen’s d=0.45), and its core dimensions: vigor (d=0.62), dedication (d=0.57), and absorption (d=0.55). Regression analyses indicated that GDP growth, increasing education funding, and greater internet access were significant positive predictors of learning engagement. Conversely, income inequality (as measured by the Gini coefficient) and urban unemployment rates were not statistically significant predictors in this context.

Sub-study 2 drawed on longitudinal data from the China Family Panel Studies (CFPS), comprising 14,623 participants. Using multilevel linear regression models, the sub-study 2 validated the meta-analytic findings, confirming a steady increase in student learning engagement over time (β=0.023, 95% CI [0.022, 0.024], p＜0.001), with a noticeable inflection point around 2012. While the influence of urban unemployment appeared inconsistent, the remaining societal variables—GDP, education investment, internet penetration rate, and income inequality—showed stable, statistically significant associations with engagement.

Together, these two sub-studies offered robust, triangulated evidence for a long-term increase in student learning engagement across China’s mainland. By employing different methodologies and data sources to enhance both the internal and external validity of the findings, they jointly highlighted the pivotal roles of economic development, educational investment, and internet connectivity in shaping students’ academic motivation and behavior.

The study also introduced a novel theoretical contribution: the proposal of “belief-benefit resonance” mechanism. This concept suggested that during periods of rapid economic growth, prevailing cultural values—such as the belief that “knowledge changes destiny”—reinforce the material benefits of education, thereby motivating and sustaining higher levels of student engagement. However, in times of intensified social tensions or inequality, this synergy may break down, potentially leading to disengagement or motivational decline among learners.

To further achieve a comprehensive understanding of the mechanisms driving shifts in learning engagement, future research could prioritize the following directions. First, this study only selected one kind of measurement tool from diverse tools of engagement for meta-analysis, which might have missed some researches. Future research might incorporate data from other measurement tools to validate the stability and generalizability of the findings. Second, it is imperative to systematically integrate sociocultural and psychological constructs, such as shifting societal values, perceived educational equity, and collective emotional dynamics, alongside conventional macro-level indicators, including GDP, educational expenditure, and internet penetration. This broader analytical lens is essential for capturing the complex, multilevel interactions between contextual forces and student engagement. Finally, researchers are encouraged to adopt advanced methodological strategies, such as Multiverse Analysis (MA) and Specification Curve Analysis (SCA), to rigorously identify the key determinants of academic engagement and to evaluate the stability and robustness of their predictive pathways across alternative model specifications.

In sum, this study provided a nuanced and comprehensive account of how macro-level societal transformations—including economic growth, educational reforms, and technological diffusion—shape the psychological processes underlying student learning engagement in contemporary China. The findings not only advanced educational psychology theory but also offered timely, evidence-based guidance for educators and policymakers seeking to enhance learning outcomes in rapidly changing social contexts.

Key words: learning engagement, longitudinal changes, multilevel linear regression, linear meta-regression model, cross-sectional historical meta-analysis