The purpose of this study is to explore the relationship among higher vocational college (HVC) students’ social support (SS), learning burnout (LB), and learning motivation (LM), and to further explore the influence regulation mechanism. By analyzing the questionnaire survey data of 500 HVC students, this study found some important conclusions. First, a positive correlation is found between SS and LM, whereas LB exhibits a negative correlation with LM. Second, regression analysis results indicate significant influences of SS and LB on LM, with the latter serving as a partial intermediary between SS and LM. Lastly, analysis of group disparities reveals noteworthy distinctions in SS, LB, and LM across students of varying grades. These discoveries underscore the pivotal roles of SS and LB in molding the LM of HVC students, offering valuable insights for educational practices and policy recommendations. This study benefits the understanding of the key factors in the learning process of HVC students and provides a new direction for further research.

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.

Quality human resources will be formed if education focuses on improving students’ skills. Of course, the foundation of education must be quality. Qualified human resources will later be responsible for making Indonesia a good country in all fields. This study aims to examine the effect of applying the REACT learning model (Relating, Experiencing, Applying, Cooperating, Transferring) on learning outcomes and critical thinking skills of students of SMAN 9 KENDARI. Quantitative research method with experimental research type. The research design used was post experimental control design. The research location was at SMAN 9 KENDARI. The instruments used include learning outcomes test and critical thinking skills test. The data obtained were explained using statistical tests to see the differences between the experimental group and the control group in chemistry subjects. The results showed that the application of REACT model significantly improved students’ learning outcomes and critical thinking skills compared to conventional learning methods in chemistry subjects. The findings indicated that the REACT model was effective in improving the quality of learning and developing critical thinking skills of students of SMAN 9 KENDARI, especially in chemistry learning.

Comparative studies of national values are becoming increasingly important in the context of contemporary globalization processes. An essential condition for the shaping of national values in learners is the enrichment of pedagogical technology with components of digital technology. Both qualitative and quantitative approaches were used in the current study. The purpose of this research is to examine the efficacy of mobile learning in shaping the national values of prospective teachers. The experiment included 180 participants. Diagnostics of the levels of national values formation in the initial stage confirmed the assumption about the low formation of national values among teacher candidates and, consequently, the need for targeted work on their formation. This study demonstrates that significant advances in students’ national values have occurred following the introduction and testing of mobile learning with experimental group (EG) participants to shape national values. The data from this study can serve as the basis for creating strategies for shaping the national values of learners in universities and as a methodological basis for adapting mobile learning for the shaping of national values.

In this paper, a study developed at the University of Seniors in Aragón is presented. The Sono-libro, used as an innovative resource, is assessed in the proposal with an educational and pedagogical purpose. The aim is to understand the motivational and learning perception variation after the incorporation of the Sono-libro in the sample. In this quantitative longitudinal design study, the listening habits of the participants are comparatively analyzed at two moments: The first data collection took place before the implementation of the proposal, and the second collection occurred after the proposal. The sample consists of 116 subjects, with 64.16% being women and an average age of 66 years of age. Data was obtained through a validated ad hoc questionnaire judged by experts. The results of the data collections showed an increase in both motivation and perception of the learning obtained, indicating the benefits of incorporating digital resources into contexts of adult students.