Student well-being is essential for academic achievement and personal growth. Fostering happiness among university students is crucial for individual development, strong family bonds, a harmonious society, and national progress. This study aimed to identify key determinants of student happiness in higher education. Eight factors, including GPA, workload, family support, university environment, attitude, motivation, time management, and social relationships, were examined among 388 Thai students using an online survey. Students were categorized into distinct groups based on these factors using k-means clustering. ANOVA was employed to assess whether these factors significantly differentiated the groups, and significant factors were further analyzed using regression analysis to confirm their impact on student happiness. A neural network analysis was also utilized to evaluate the relative importance of each factor. The results revealed that attitude, GPA, and time management significantly affected student happiness. A positive attitude fosters a sense of opportunity and achievement, a high GPA reflects academic success and enhances self-confidence, and effective time management reduces stress while allowing more time for enjoyable activities.

Digital literacy is important capacity for students’ learning in the world of rapid changes in technology. This article aimed to study digital literacy among non-STEM undergraduate students. The study covered the component of 4 skills, i.e., operation skills, thinking skills, collaborative skills, and awareness skills. The hypothesis set in the study was academic major in non-STEM has different digital literacy. When each component was separately studied, digital literacy among non-STEM students should be different as well. The participants in the study were 378 non-STEM undergraduate students of Prince of Songkla University, Surat Thani Campus. The research instrument was digital literacy measurement. Inferential statistics were used in the data analysis to compare two independent groups. The results support hypotheses that the sample in non-STEM undergraduate students with different academic majors had different levels of digital literacy. Students major in a language had higher mean scores than students major in management. When each component was separately studied, students with different academic majors had different levels of digital literacy in each component. Students major in language had higher mean scores than students major in management in all components. This article mentioned an important point about digital literacy of non-STEM undergraduate students so as to be a guideline for stakeholders to determine strategies, set policies and make plans to develop digital skills among non-STEM students, which is highly important to students in the era of rapid changing technology.

Digital marketing significantly influences travellers’ decision-making processes. This research aims to analyze the specific digital marketing strategies affecting Gen-Z travellers’ decisions. Machine learning techniques were employed to uncover key insights. Data were collected through a survey using a convenience random sampling of 346 university students in Thailand. Correlation analysis and machine learning approaches, such as logistic regression and neural network analysis, were used to explore the relationships between various digital marketing strategies and their impact on Gen-Z travellers’ destination choices. The findings indicate that Online Travel Agency (OTA) websites exert the most significant influence on Gen-Z travellers’ decision-making processes, with an odds ratio of 1.6879. This is followed by the use of social media marketing strategies and businesses having their own websites. To validate these results, an Artificial Neural Network (ANN) technique was also employed, confirming the importance of rankings derived from the logistic regression analysis. This research provides valuable insights into which digital marketing strategies most influence Gen-Z travellers’ decision-making processes. These insights can help tourism businesses make informed investments in digital marketing strategies, prioritizing those that align with the preferences of Gen-Z travellers.

This study proposes and evaluates a tri-dimensional model for online learning in response to disruptions caused by a global health crisis. It examines learners’ attitudes, student and community stakeholder satisfaction, and differences in academic performance between STEM and non-STEM students. A total of 288 undergraduate students enrolled in business and technology programs participated in the study. Utilizing a mixed-methods approach, the study explores three core dimensions: (1) learners’ attitudes toward online learning, assessed using the uses and gratifications theory (UGT), focusing on cognitive, affective, and social needs, and analyzed using structural equation modeling (SEM); (2) student satisfaction and community feedback on online marketing deliverables, measured using descriptive statistics; and (3) comparative academic performance across disciplines, evaluated using inferential statistical tests. The findings reveal that all UGT variables significantly influence learners’ attitudes. Students expressed high satisfaction with the model’s real-world relevance, and community stakeholders reported similar satisfaction with the student-generated outputs. Furthermore, STEM students achieved significantly higher academic performance than their non-STEM peers.

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making.