This study examines the contentment and commitment of rural residents from three different perspectives. The first is environmental management, followed by municipal services and finally territorial planning. The study’s objective is to analyze the causal relationships between the expected quality and perceived quality concerning perceived value, satisfaction and citizen loyalty to provide tools for decision-making to public managers. This research proposes a structural equation model to evaluate and validate five hypotheses. For this study, household-level surveys were implemented to a population sample of 450 families in the rural area of Tenguel in Ecuador. The results suggest that the public policies exercised by territorial managers significantly influence citizens’ perceived value, satisfaction, and loyalty, which impacts social welfare. This research shows that there are deficient areas that negatively impact perceived locality, which decreases the perceived value. Such as firefighting service, municipal police, veterinary services, preservation of historical and cultural assets and activities, and facilities for community use.

The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.

As China’s urbanization process accelerates, it has become common for rural men to go out to work and women to stay at home. The implementation of China’s rural revitalization strategy is in dire need of a large amount of high-quality human capital, and education and training are an important way to improve human capital and empower left-behind women. Starting from the background of China’s rural revitalization, this study focuses on the education and training of rural left-behind women, a topic that has received less attention. Through in-depth interviews and participatory observation, we analyzed the factors affecting rural left-behind women’s participation in education and training, as well as the problems that exist in China’s rural education and training process, and proposed strategies to solve them. The study found that education level, traditional attitudes, economic income, knowledge of education and training, and mental health are important factors affecting the participation of left-behind women in education and training in rural China. At the same time, there are some problems in the process of education and training, such as a single main body of supply and training methods, a lack of teachers, and a lack of management, etc., which affect the development of education and training, and thus also the promotion of rural revitalization.

Access to clean water and improved sanitation are basic elements of any meaningful discourse in rural development. They are critical challenges for achieving sustainable development over the next decade. This paper seeks to examine the strategies for improving access to clean water and sanitation in Nigerian rural communities. Hypothetically, the paper states that there is no significant relationship between access to clean water and sanitation and the attainment of Sustainable Development Goal 6 in Nigeria. The paper leverages Resilience Theory. The survey research design was adopted, and primary data was obtained from a sample size of 250 respondents, proportionally drawn from the 10 wards in Obanliku local government area of Cross River State. The chi-square statistical technique was to test the hypothesis. The result shows that the calculated value of Chi-square (X²) is 24.4. Since the P-value of 21.03 is less than the level of significance (0.05), the null hypothesis was rejected and the alternate accepted. The study concludes that there is a significant relationship between access to clean water and sanitation and the attainment of Sustainable Development Goal 6 in Cross River State, Nigeria. it recommends the need for more commitment on the part of government and international donor agencies in expanding access to clean water and improved sanitation in Nigeria.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.