This study investigates the dynamic landscape of agritourism in Thailand, emphasizing innovations, challenges, and policy implications in the post-COVID-19 era. Employing a qualitative approach, including a comprehensive literature review and semi-structured interviews with stakeholders, the research identifies key agritourism models, such as immersive learning experiences, technology-driven agritourism, and unconventional practices like salt and coconut plantations. Findings reveal that agritourism has adapted to shifting market demands through diversification, technological integration, and a heightened focus on sustainability. Notably, technology adoption in precision farming and hydroponics enhances resource efficiency and visitor engagement, while initiatives like rice paddy field tourism and highland agritourism showcase the cultural and ecological richness of rural landscapes. The study underscores the critical role of policy frameworks, infrastructure development, and community empowerment in fostering sustainable agritourism practices. Key policy recommendations include targeted subsidies, capacity-building programs, and harmonized regulatory frameworks to address challenges such as financial constraints, regulatory ambiguities, and inadequate infrastructure. This research contributes to the broader discourse on sustainable tourism and rural development, aligning agritourism with the United Nations Sustainable Development Goals (SDGs). By synthesizing insights on innovation, resilience, and sustainability, this study offers a comprehensive roadmap for policymakers, practitioners, and academics to leverage agritourism as a vehicle for rural revitalization and global sustainability. Future research directions are proposed to explore the long-term impacts of technological integration, community empowerment, and resilience strategies in agritourism.

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.

The aim of this study was to assess the challenges of rural landholding rights of women in Boloso sore Woreda. The population that used as source of data were sample womenfrom four kebeles,Kebele land administration committee members,Woreda women,youth and children office head,Woreda women’s association president,Woreda agriculture office head and Woreda agriculture office rural land administration desk experts.four kebeles from 28 rural kebeles selected by using systematic random sampling. Data gathered using questionnaire were analyzed using SPSS where descriptive and inferential were used for the purpose. Secondary data were collected from different relevant literatures such as reports, research results documents and publications. As to the findings,women landholding trend in the study area was highly contrasts legally ensured equal holding and using rights of women with men.The community including women themselves perceive women independent landholding as taboo and prohibits it.Even if they hold by different means,the plot of land they got or held was small in size and not conducive for agriculture and house construction. The awareness of women on rural land registration and certification benefit was also poor. Thus,rural women should be initiated to organize and struggle for their equal landholding and administering rights.

With the advancement of modernization, commoditization and grassroots governance have become important terms. Community governance not only promotes modern democracy but plays a key role in improving community governance capabilities and modernizing the governance system, which is receiving much attention. Despite the expanding number of articles on community governance, few evaluations investigate its evolution, tactics, and future goals. As a result, the particular goal of this study is to provide the findings of a thematic analysis of community governance research. Investigating the skills and procedures needed for practice-based community government. Data for this study were gathered through a thematic assessment of 66 papers published between 2018 and 2023. The pattern required by the researchers was provided by the ATLS.ti23 code used to record the review outcomes. This study proposes six central themes: 1) rural advancement, 2) community (social) capital, 3) public health and order governance, 4) governance technology, 5) sustainable development, and 6) governance model. The research results show that the research trend of community governance should focus on rural advancement, taking rural community governance as the starting point, the dilemma and adjustment of the governance model, community public health and order governance, and digital governance. It will yield new insights into new community governance standards and research trends.

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.