The learning of English courses is not only a process for students to master language knowledge and skills, but also a process for improving students' comprehensive humanistic quality. The integration of moral education in English teaching can not only cultivate students' good study habits, improve the efficiency of English learning, but also help to cultivate students' excellent moral quality. Through the research on the organic integration strategy of primary school English teaching and moral education, this paper aims to provide an effective method for the effective integration of primary school English teaching and moral education, so as to promote the improvement of the level of primary school moral education and achieve the goal of building morality and cultivating people.

An extensive assessment index system was developed to evaluate the integration of industry and education in higher vocational education. The system was designed using panel data collected from 31 provinces in China between 2016 and 2022. The study utilized the entropy approach and coupled coordination degree model to examine the temporal and spatial changes in the level of growth of the integration of industry and education in higher vocational education, as well as the factors that impact it. In order to examine how the integration of industry and education in higher vocational education develops over time and space, as well as the factors that affect it, we utilized spatial phasic analysis, Tobit regression model, and Dagum’s Gini coefficient. The study’s findings suggest that between 2016 and 2022, the integration of industry and education in higher vocational education showed a consistent improvement in overall development. Nevertheless, there are still significant regional differences, with certain areas showing limited levels of integration, while the bulk of regions are either in a state of low integration with high clustering or low integration with low clustering. Most locations showed either a “low-high” or “low-low” level of agglomeration, indicating a significant degree of spatial concentration, with a clear trend of higher concentration in the east and lower concentration in the west. The progress of industrial structure and the degree of regional economic development have a substantial impact on the amount of integration of industry and education in higher vocational education. There is a notable increase in the amount of integration between industry and education in higher vocational education, which has a favorable effect. Conversely, the local employment rate has a substantial negative effect on this integration. Moreover, the direct influence of industrial structure optimization is restricted. The Gini coefficient of the development level of integration of industry and education in higher vocational education exhibits a slight rising trend. Simultaneously, there is a varying increase in the Gini coefficient inside the group and a decrease in the Gini coefficient between the groups. The disparities in the level of integration between Industry and Education in the provincial area primarily stem from inter-group variations across the locations. To promote the integration of industry and education in higher vocational education, it is recommended to strengthen policy support and resource allocation, address regional disparities, improve professional configuration, and increase investment in scientific and technological innovation and talent development.

This study examines the relationship between macroeconomic determinants and education levels in eight selected African oil-exporting countries (AOECs) over the period 2000–2022. Drawing on human capital theory, the paper scrutinizes the impact of factors such as income inequality, health outcome, economic growth, human development, unemployment, education expenditure, institutional quality, and energy consumption on education levels. Employing robust estimation techniques such as fixed effects (FE), random effects (RE), pooled mean group (PMG) and cross-section autoregressive distributed lag model (CS-ARDL), the study unveils vital static and dynamic interactions among these determinants and education levels. Findings reveal notable positive and significant connections between education levels and some of the variables—human capital development, institutional quality, government expenditure on education, and energy consumption, while income inequality demonstrates a consistent negative relationship. Unexpectedly, health outcomes exhibit a negative impact on education levels, warranting further investigation. Furthermore, the analysis deepens understanding of long-run and short-run relationships, highlighting, for example, the contradictory impact of gross domestic product (GDP) and unemployment on education levels in AOECs. Finally, the study recommends targeted human development programs, enhanced public investment in education, institutional reforms for good governance, and sustainable energy infrastructure development.

In the human and economic development context, this study examines the relationship between human capital, life expectancy, labor force participation rate, and education level in Indonesia, Malaysia, and Thailand. The World Bank’s 2001–2021 data are examined using a panel vector autoregressive model. The findings demonstrate the substantial influence of health expenditure from the prior period on present health expenditure. Though not significantly different, life expectancy and education levels from earlier periods also impact present health spending. A slight positive correlation exists between prior labor force involvement and present healthcare costs. An increase in current health expenditure supports an increase in life expectancy. Health expenditure in the previous period had a significant positive effect on education, although insignificant. Life expectancy in the previous period harms current education but is also insignificant. Education in the previous period significantly positively affects current education, indicating a sustained impact of education investment. Labor force participation in the previous period also positively affected education, although not significantly. The prior period’s health spending, life expectancy, and educational attainment impact the current labor force participation rate. The length of life has a significant favorable impact on entering the labor sector. Currently being in the job field has a good correlation with prior education as well. These findings support that higher education levels lead to higher labor force participation rates. Life expectancy, health care costs, education level, and prior work experience all influence current life expectancy. While prior life expectancy significantly influences current life expectancy, health expenditures have a negligible negative impact. Prior education positively impacts life expectancy but negatively impacts prior labor force engagement. These results reject the hypothesis that increasing life expectancy causes current health expenditure to increase.