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Pigultong, Metee
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Journal : Journal of Applied Data Sciences

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Utilizing Systematic Digital Platforms and Instructional Design in Health Communication: A Data-Driven Approach in China's Curriculum Fu, Ying; Sangsawang, Thosporn; Pigultong, Metee; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.651

Abstract

This study explores the integration of systematic instructional design and digital platforms in health communication courses in China, with a focus on evaluating the effectiveness of these approaches in enhancing medical interns' knowledge and satisfaction. The research involved 17 experienced physicians and 30 medical interns, utilizing the Delphi Method for expert input and various data collection methods, including in-person surveys, telephone interviews, and email-based questionnaires. The study aimed to assess the impact of digital platforms and instructional design on knowledge acquisition and overall satisfaction. The findings suggest that the integration of systematic instructional design with digital platforms significantly improved medical interns' knowledge and engagement with the health communication curriculum. Additionally, expert consensus supported the effectiveness of this approach in addressing critical gaps in digital literacy and practical health communication skills. The study introduces the Chinese IDSDPS Health Communication Model, a dynamic, culturally relevant framework designed to bridge gaps in digital literacy, communication tactics, data analysis, and interdisciplinary learning. By incorporating locally relevant health content and ensuring alignment with China's public health needs, the model presents a scalable approach to improving health communication education. This research emphasizes the transformative potential of combining instructional design and digital technologies to enhance educational outcomes in health communication, offering valuable insights for addressing broader public health challenges both in China and globally.
Data-Driven Development of an Elderly Training Package Using the GCC Model Cheng, Fan; Sangsawang, Thosporn; Pigultong, Metee; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.662

Abstract

This study aimed to design and assess the effectiveness of an elderly training package for first-year students at Yibin University, China, based on the GCC Model for geriatric rehabilitation. The goal was to integrate theoretical knowledge with practical skills in geriatric care, using data-driven approaches to evaluate its impact on student learning outcomes. A purposive sample of 17 experts and 30 first-year students enrolled in geriatric rehabilitation courses participated in the study. Data were collected through a combination of in-person surveys, telephone interviews, and email interviews using the Delphi Method. The training package focused on critical aspects of geriatric care, including aging-related health issues, physical rehabilitation, psychological support, and social integration. Additionally, it incorporated technology, practical simulations, case studies, and feedback mechanisms to enhance healthcare professionals’ skills. Data analysis demonstrated a significant improvement in students' knowledge and practical abilities post-intervention, with moderate satisfaction expressed by both experts and students regarding the effectiveness of the package. The study underscores the importance of blending theoretical learning with hands-on experience, utilizing data-driven evaluation methods to assess the impact on educational outcomes. These findings provide valuable insights for the development of effective geriatric care training models that combine data science and educational practices to optimize learning in healthcare education.
Factor Analysis on Teaching Quality Management for Art Design Students Using Data Driven Approach Junru, Chen; Sangsawang, Thosporn; Pigultong, Metee; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.746

Abstract

This study aimed to improve teaching quality management for Art Design students using a data-driven approach through three objectives: (1) synthesizing key factors influencing instructional quality, (2) analyzing those factors using expert consensus, and (3) evaluating student satisfaction after applying the data-driven methodology. The Delphi Method was used to gather insights from 17 education experts, while 30 purposively selected Art Design students participated in satisfaction assessments. Data collection involved questionnaires and interviews, with analysis techniques including mean, standard deviation, Coefficient of Variation (CV), and t-tests. Cronbach’s α was 0.98, indicating high internal reliability. Results showed expert consensus on relevant teaching quality factors (M = 3.92, SD = 0.33, CV = 19.96, p = .002). Key aspects identified included instructional design, digital integration, feedback mechanisms, and curriculum alignment. Post-intervention analysis revealed significant student improvement, with average skill levels increasing from 16.12 (SD = 0.89) to 20.34 (SD = 0.566, p = .002). Student satisfaction reached 78.59%, with a mean of 3.90 (SD = 0.72, CV = 18.78). All statistical terms were properly defined and contextualized. The findings underscore the role of structured data analysis and expert-informed models in enhancing instructional strategies, aligning teaching with professional expectations, and promoting continuous improvement in Art and Design education.
Co-Authors Cheng, Fan Fu, Ying Junru, Chen Sangsawang, Thosporn Watkraw, Wasan