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All Journal Indonesian Journal of Science and Technology Jurnal CoreIT Buletin Ilmiah Sarjana Teknik Elektro IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences International Journal of Robotics and Control Systems Munaddhomah: Jurnal Manajemen Pendidikan Islam International Journal for Applied Information Management Indonesian Journal of Teaching in Science Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Sangsawang, Thosporn
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A Mixed-Methods Data Approach Integrating Importance-Performance Analysis (IPA) and Kaiser-Meyer-Olkin (KMO) in Applied Talent Cultivation Zhang, Zhang; Sangsawang, Thosporn; Vipahasna, Kitipoom; Pigultong, Matee
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.170

Abstract

This study endeavors to establish an assessment framework for cultivating undergraduate applied talent, specifically emphasizing data science competencies, in alignment with the development of China's regional economy. A mixed-methods approach, integrating focus group interviews and questionnaire surveys conducted over three rounds of data collection, was employed. The collected data underwent rigorous reliability and validity analyses utilizing SPSS software. An Importance-Performance Analysis (IPA) was executed to construct a performance chart, evaluating the effectiveness of a 24-item framework designed to encompass key aspects of data science education. The initial internal consistency α coefficients for Questionnaire 2 and Questionnaire 3 were found to be .892 and .913, respectively, surpassing the 0.7 threshold, indicating a high level of reliability for all items related to data science competencies. The Kaiser-Meyer-Olkin (KMO) measurements approaching approximately 0.9 affirmed the efficiency of the questionnaire, specifically designed to gauge the relevance and effectiveness of data science-related indicators in the context of applied talent cultivation and regional economic development. Furthermore, the study underscores the significance of indicators such as teamwork, regional market research, and business opportunity identification within the domain of data science. It identifies gaps between key indicators and lower-performing indicators, proposing strategic improvement measures to enhance the alignment of applied talent cultivation objectives with the evolving needs of regional economic development, particularly in the data science landscape. The research findings not only contribute to a foundational understanding of data science competencies in applied talent cultivation but also lay the groundwork for innovative reforms in future talent cultivation models. By clarifying objectives and better aligning them with the dynamic demands of regional economic development, this study sets the stage for transformative advancements in the field of applied talent cultivation, particularly within the realm of data science.
Incorporating Augmented Reality to Enhance Learning for Students with Learning Disabilities: A Focus on Spatial Orientation in Physical Intarapreecha, Navinee; Sangsawang, Thosporn
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.126

Abstract

This research endeavors to integrate Augmented Reality (AR) technology into the realm of physical education, with a specific emphasis on improving spatial orientation skills among students with learning disabilities. The study pursues three core objectives: (1) To assess the efficacy of utilizing AR-based instructional tools to enhance spatial orientation abilities; (2) To scrutinize the academic advancements of students with learning disabilities post-AR intervention; (3) To gauge the satisfaction levels of these students with the AR-enhanced learning experience. The study cohort comprises nine students with learning disabilities, drawn from an educational institution situated in Pathum Thani Province, Wat Pathum Nayok school, using a targeted sampling methodology. Data is gathered through immersive AR experiences within the context of physical education, with a focus on spatial awareness. The analytical approach encompasses a diverse array of statistical techniques, including percentages, means, and standard deviations. Furthermore, the t-test is deployed to statistically compare pre and post-learning outcomes, maintaining a significance level of α = 0.05. The research outcomes substantiate that AR-driven educational activities in physical education effectively enhance spatial orientation skills among students (E1/E2: 82.40/81.33). Preceding the intervention, students recorded an average score of 8.80 with a standard deviation of 2.33, which significantly escalated to 16.27 with a standard deviation of 1.48 following AR-assisted learning. The t-test underscores the statistically significant disparity (p 0.05) in scores prior and subsequent to the AR intervention. Furthermore, students with learning disabilities express considerable satisfaction with the application of AR in physical education, with an average satisfaction rating of 4.51. This research carries substantial implications, particularly within the realm of data science, as it pertains to the collection and analysis of data relating to students' educational achievements and satisfaction levels.
Data Envelopment Analysis of Scientific Research Performance for Higher Vocational Colleges Zhou, Lin; Boonsong, Sutthiporn; Siramaneerat, Issara; Sangsawang, Thosporn; Sawetmethikul, Pakornkiat
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.166

Abstract

This research aims to evaluate the scientific research performance of higher vocational colleges in Sichuan within the evolving landscape of data science. The study pursues two primary objectives: firstly, to assess the scientific research performance of these institutions using advanced methodologies such as Data Envelopment Analysis (DEA) and the Malmquist index models; secondly, to explore the intricate relationship between scientific research inputs and efficiency through the lens of Rough Set theory. The dataset comprises scientific research inputs and outputs from 30 higher vocational colleges, spanning the years 2019 to 2021. The findings underscore an overall positive trend in scientific research performance across the higher vocational colleges under examination. However, a nuanced analysis using DEA and Malmquist index models identified that only five institutions demonstrated robust performance during the specified period. Furthermore, the study delves into the influential factors affecting scientific research efficiency, revealing that internal expenditure on scientific research funds and the presence of senior and above professional teachers play pivotal roles. These insights are gleaned through the application of Rough Set theory, providing a unique perspective within the realm of data science. In conclusion, the research recommends strategic interventions to improve research management and resource allocation, emphasizing their role in enhancing efficiency and mitigating disparities among higher vocational colleges in Sichuan, particularly in the context of data science. The study adopts a holistic approach, employing an integrated model that combines DEA, Malmquist, and Rough Set theory for a comprehensive evaluation of research performance within the evolving landscape of data science.
A Comprehensive Data-Driven Analysis of Talent Supply using Delphi Method in Higher Vocational Education and Ethnic Minority Regions Huang, Lihua; Boonsong, Sutthiporn; Siramaneerat, Issara; Sangsawang, Thosporn; Sawetmethikul, Pakornkiat; Chen, Rui
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.171

Abstract

This study delves into the principles of structural reforms on the supply side of talent in higher vocational education, specifically focusing on the context of Guangxi, China, and extending its applicability to diverse ethnic regions. Embracing a data science approach, the research aims to develop a model grounded in theoretical foundations and policy considerations, offering insights to enhance the higher vocational education system and facilitate a high-quality talent supply. The research sample comprises 28 experts who contributed 182 perspectives on the constituent elements of higher vocational education reform in ethnic minority areas. Leveraging the Delphi method, the study employs qualitative evaluation methods through anonymous questionnaire surveys to ensure reliable feedback. A comprehensive survey includes 391 participants representing various stakeholders, such as the education department, teachers, industry experts, and students. Utilizing mathematical statistics and SPSS AU22.0 for data analysis, the study confirms that adaptation indicators meet established standards, aligning the theoretical model with measured data. Descriptive analysis and correlation testing of model variables reveal moderate to high average values, indicating a significant positive correlation between the scales. The study explores the layout of universities, major settings, curriculum systems, and talent cultivation as independent variables, with a focus on their influence on vocational talent cultivation. Additionally, it covers the demand side of talents, incorporating perspectives from the government, society, students, and parents. The analysis assesses the satisfaction of the supply side of higher vocational education, exploring specific manifestations of the contradiction between talent supply and demand. Through attribution analysis, the study concludes by proposing considerations for the supply-side structural reform of higher vocational education talents in Guangxi and similar ethnic regions. This research, rooted in data science methodologies, provides valuable insights for educational policymakers and practitioners. It sets the stage for further exploration into the dynamic interplay between data-driven decision-making and structural reforms in the higher vocational education landscape.
Utilizing the Delphi Technique to Develop a Self-Regulated Learning Model Li, Yongmei; Sangsawang, Thosporn; Vipahasna, Kitipoom
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.124

Abstract

This study combines learning process theories within the context of data science education in Sichuan Province, China, and develops a customized instructional model for the self-regulated International Higher Education (IHE) Model. In collaboration with 17 experts, selected through purposive sampling, and involving 100 instructors within Sichuan, China, this research explores an instructional model designed to foster self-regulated learning in the field of data science. The Delphi data collection method is employed to investigate the relevance of various learning theories within international higher education in Sichuan Province, China, with a specific emphasis on the data science discipline. The Self-Regulated Learning in International Higher Education (SLR-IHE) model, informed by survey questionnaires, addresses pertinent challenges encountered in data science education, including issues related to English language proficiency, faculty training, curriculum development, faculty mobility, cross-border regulations, and funding constraints. The findings of this study lead to the development of an International Higher Education (IHE) Model for Sichuan Province, China, using the Delphi Technique, which consists of four distinct instructional modules. Through a linear regression analysis of the SLR-IHE model, it becomes evident that the self-regulated learning process in data science education comprises four essential stages, each contributing to the acquisition of distinct goals. These stages include: (1) activating prior knowledge; (2) fostering idea exchange and iterative improvement; (3) building organizational knowledge through understanding, memorization, analysis, and transfer; and (4) generating innovative ideas through reflexive thinking and initiating creative thought processes. These stages collectively support the achievement of specific goals associated with Self-Managed Learning (SML), Self-Regulated Learning (SRL), Self-Paced Learning (SPL), and Self-Directed Learning (SDL) in the context of data science education. This comprehensive instructional model for data science education within the framework of international higher education development in Sichuan Province, China, emphasizes globalization, collaborative efforts, and economic growth as key drivers for enhancing the quality of education in the field of data science.
The Efficacy of Online Gamification in Improving Basic English Skills for Fourth-Grade Students Pasawano, Tiamyod; Sangsawang, Thosporn
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.410

Abstract

The study aimed to achieve three main objectives: 1) to develop an online gamification system using digital learning platforms for teaching English to Grade 4 students, following the E1/E2 = 80/80 efficiency criterion, 2) to compare students' achievement in Basic English through online gamification, and 3) to assess students' satisfaction with the use of online gamification in learning Basic English. The sample comprised 30 Grade 4 students from Settabutr Upathum School in the academic year 2022, selected through purposive random sampling. Research instruments included online Zoom classes, lesson plans, and interactive learning platforms. The study employed mean, standard deviation, and t-tests for dependent samples for data analysis. The results revealed an efficiency value of E1/E2 as 70.00/69.00, falling short of the 80/80 criteria. Several factors, such as the comprehensive nature of testing macro skills using digital media beyond cognitive abilities, may have contributed to not meeting the set criterion. Furthermore, a significant improvement in learning achievements in Basic English was observed among Grade 4 students who used online gamification compared to traditional methods, with higher scores in achievement tests at a significance level of 0.05. Finally, students expressed a good level of satisfaction with the online gamification approach in learning Basic English.
Applying Factor Analysis to Assess Employment Competitiveness Strategies: A Data Science Perspective Wang, Yang; Sangsawang, Thosporn; Vipahasna, Piyanan Pannim; Vipahasna, Kitipoom; 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.v6i1.650

Abstract

This study aims to identify and analyze the factors influencing the employment competitiveness of graduates from higher vocational colleges in China and evaluate the impact of targeted programs designed to enhance these factors on graduates' employability. The research involved 17 experts and 100 instructors from Sichuan University of Science and Engineering, utilizing purposive sampling to explore effective career guidance models for improving employment ability. The Delphi technique was applied to synthesize expert opinions on key factors affecting graduate employment competitiveness. Additionally, a sample of undergraduate students participated in the study, with data collected through questionnaires. The findings demonstrate the transformative potential of focused career guidance programs, showing a significant improvement in students' employability post-intervention. These results emphasize the importance of targeted initiatives that equip students with the necessary skills, resources, and career insights to succeed in the job market. By bridging the gap between academia and industry expectations, such programs play a crucial role in preparing students for a smooth transition from university to the professional world, helping them secure meaningful employment opportunities.
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.
Instructional Strategy Competence Model for Pre-Service Teachers Using Data-Driven Approaches Tang, Lin; Pasawano, Tiamyod; Sangsawang, Thosporn
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.732

Abstract

The objectives of this study were to: (1) identify and analyze the factors influencing the instructional strategy competence of pre-service primary and secondary school teachers, (2) examine how these factors impact their competence, and (3) develop a comprehensive competence model incorporating personal, school, and social factors using data-driven approaches. The sample consisted of 17 Chinese experts and 320 pre-service teachers in Sichuan Province, selected through purposive random sampling. Data collection involved the Delphi method with experts to gather insights on influential factors and a structured questionnaire for pre-service teachers. Statistical analyses included Cronbach’s alpha for reliability, descriptive statistics (mean, standard deviation, interquartile range), exploratory factor analysis for structural validity, and structural equation modeling (SEM) using AMOS to assess factor influences. The results demonstrated strong internal consistency with a Cronbach’s alpha of 0.90. Expert responses showed a high level of consensus (mean = 4.86, standard deviation = 0.40, IQR = 1). The developed instructional strategy competence model was validated by experts and found to be highly appropriate for pre-service teachers.
Co-Authors Aang Subiyakto Abdul Karim Bakti Gama, Cipta Boonsong, Sutthiporn Chankham, Nawaphas Chen, Mei Chen, Rui Cheng, Fan Darboth, Kanokwan Dulklang, Thanyathorn Evi Triandini Evy Nurmiati Fetrina, Elvy Forca, Adrian Jaleco Fu, Ying Huang, Lihua Huda, Muhammad Qomarul Intarapreecha, Navinee Isman, Aytekin Junru, Chen Kim, Sangbum Kulnattarawong, Thidarat Li Guo Li, Shu Liu Yang Luaensutthi, Aammuay Luhuringbudi, Teguh Meinarini Catur Utami Mesraini Mesraini Nashrul Hakiem Pasawano, Tiamyod Pigultong, Matee Pigultong, Metee Prammanee, Noppadol Punyayodhin, Sulaganya Rahman, Hadi Rangkuti, Rahmah Yasinta Sawetmethikul, Pakornkiat Shuang Li Siramaneerat, Issara Tang, Lin Thepnuan, Naruemon Thepnuan, Narumom Vipahasna, Kitipoom Vipahasna, Piyanan Pannim Wang, Ning Watkraw, Wasan Wen, Tingbin Yang Wang Yongmei Li Yudhanta, Satya Yuni Sugiarti, Yuni Yuri Pamungkas Zhang, Yongfeng Zhang, Zhang Zhong, Junhua Zhou, Dan Zhou, Lin