Developing Smart Community Using Geographic Information Systems-Based Applications: An Educational Approach to Sustainable Resource Management


  •  Wittaya Taosa    
  •  Pongsaton Palee    
  •  Warut Kitcharoen    

Abstract

This study developed and evaluated an educational model that integrates Geographic Information Systems (GIS) applications to strengthen community capacity and improve competencies in sustainable resource management. Positioned within the context of higher education community engagement, the research adopted a Research and Development (R&D) framework comprising four phases: educational needs analysis and instructional framework design, pedagogical application development, system testing, and educational implementation and evaluation. The learning instruments included a GIS-SRM instructional manual, a Smart Community Competency Assessment Form, and a learner satisfaction questionnaire. Data were analyzed using descriptive statistics and inferential statistics (paired-sample and independent-sample t-tests). The results demonstrated that the developed educational system provided a comprehensive digital geographic learning environment, enabling learners to produce thematic maps for real-world applications such as infrastructure management and disaster risk assessment. A quasi-experimental design was conducted involving 150 adult learners—comprising local administrative staff and community stakeholders participating in a university extension program. Participants were divided into an experimental group (n = 75) and a control group (n = 75). The findings revealed that adult learners engaged with the GIS-SRM instructional model demonstrated significantly higher learning achievements in knowledge acquisition, practical skills, and spatial decision-making competencies regarding sustainable resource management compared to those receiving traditional instruction (p < .05). Furthermore, participants reported high satisfaction with the system’s usability and its pedagogical effectiveness in facilitating real-time data collection, spatial analysis, and community-based learning.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1925-4725
  • ISSN(Online): 1925-4733
  • Started: 2011
  • Frequency: semiannual

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