Wanida Saetang

The preservation of tacit knowledge embedded in Southern Thai textiles remains a significant challenge because much of this knowledge is dispersed across private households and is inadequately supported by conventional object-centered documentation systems. This study developed a crowdsourcing-based digital storytelling platform that enabled communities to document, organize, and disseminate knowledge related to Southern Thai textile heritage. The platform integrated community participation, structured narrative authoring, and knowledge organization within a socio-technical system designed for cultural heritage documentation. To guide its development, the study proposed the Crowdsourced-Storytelling Intangible Cultural Heritage Framework (CS-ICH Framework) and operationalizes it through requirements analysis, iterative prototyping, and empirical user evaluation. The evaluation results indicated high levels of user satisfaction and positive user perceptions regarding knowledge accessibility, content organization, and the platform’s support for heritage preservation. These findings suggested that participatory digital platforms can effectively facilitate the documentation and dissemination of locally embedded cultural knowledge that is difficult to capture through conventional institutional systems. This study contributed to digital heritage research by providing a potentially transferable framework and design principles for integrating crowdsourcing and digital storytelling within platforms designed to preserve and transmit intangible and community-held cultural heritage.

This study introduces a comprehensive phygital framework tailored for small local museums, addressing the unique challenges of textile exhibitions. By seamlessly integrating physical artifacts with advanced digital tools through a user-centered design–thinking approach, the platform transforms traditional museum visits into hybrid experiences. The research addresses challenges faced by small museums, such as limited interactivity, static information presentation, and resource constraints. The findings demonstrate that the phygital platform significantly enhances visitor satisfaction, usability, and engagement. Features like mobile applications, chatbots, and gamification foster dynamic interactions, increasing interest in historical textile collections. The evaluation highlights positive impacts on visitor learning and accessibility, with high usability scores and favorable feedback confirming the platform’s success. By bridging physical and digital realms, the platform empowers small local museums to modernize their exhibition experience offerings while preserving their authenticity and cultural significance. This study contributes to the growing literature on phygital strategies in museum contexts, offering practical recommendations for implementing such platforms in resource-constrained settings. The findings underscore the potential of phygital approaches to foster deeper connections with cultural heritage, ensure broader accessibility, and support sustainable visitor engagement.

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.

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.