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Thosporn Sangsawang
Educational Technology and Communications Division, Faculty of Technical Education, Rajamangala University of Technology, Thanyaburi, Pathum Thani, 12110, Thailand
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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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; Thosporn Sangsawang; Kitipoom Vipahasna; Matee Pigultong
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.
Data Envelopment Analysis of Scientific Research Performance for Higher Vocational Colleges Lin Zhou; Sutthiporn Boonsong; Issara Siramaneerat; Thosporn Sangsawang; Pakornkiat Sawetmethikul
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 Lihua Huang; Sutthiporn Boonsong; Issara Siramaneerat; Thosporn Sangsawang; Pakornkiat Sawetmethikul; Rui Chen
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.
Statistical Analysis the Influence of Internal and External Factors on Entrepreneurial Intentions Tingbin Wen; Sutthiporn Boonsong; Issara Siramaneerat; Thosporn Sangsawang; Pakornkiat Sawetmethikul
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.v5i1.167

Abstract

This study aimed to explore and analyze the internal and external factors influencing statistical analysis the influence of internal and external factors on entrepreneurial intentions. The specific focus was on conducting an in-depth analysis of how these factors manifest within the data science demographic. The study involved a sample group of 432 university students, employing an anonymous questionnaire to gather reliable feedback and achieving a commendable response rate of 93%. Through an established random sampling scheme, 402 valid responses were obtained for data analysis. The data processing and analysis were conducted utilizing SPSS software, incorporating descriptive statistics, hypothesis testing, and multiple regression analysis to uncover insights within the data science context. The study yielded significant results: 1) Gender emerged as a robust variable with a significant t-value=3.28 and a low p-value = .001, indicating a notable gender-based disparity in entrepreneurial intention among students in the data science domain. Work experience also exhibited noteworthy t and p-values (t = -2.45, p = .015), emphasizing the influential role of prior work experience on students' entrepreneurial inclination within the data science field; 2) A comprehensive examination of data related to determinants of university students' entrepreneurial intention revealed distinct differences in the realm of individual traits (personality: ????̅ = 3.94, SD. = .74; values: ????̅ = 4.01, SD. = .70; motivation: mean = 3.87, SD. = .74), social-cultural influences (????̅ = 3.89, SD. = .70), family (????̅ = 3.78, SD. = .86), peers (????̅ = 3.77, SD. = .72), mentors (????̅ = 3.72, SD. = .89), dimensions related to data science entrepreneurship education (innovation education: ????̅ = 3.80, SD. = .87; training: ????̅ = 3.76, SD. = 0.94; courses: ????̅ = 3.71, SD. = .93), and economic environmental factors (financial pressures: ????̅ = 3.93, SD. = .77; financing: ????̅ = 3.89, SD. = .72; market opportunities: mean = 3.83, SD. = .80) exhibited pronounced trends towards convergence within the data science sector. These findings highlight the necessity of comprehensively considering multiple interconnected factors specific to data science in fostering entrepreneurial spirit among university students; 3) All secondary indicators of the four hypothesized factors - individual traits, social support, data science entrepreneurship education, and economic environment - were significant at the .01 level (p .01), affirming positive correlations between all hypothesized factors and the dependent variable of entrepreneurial intention within the data science context.
Statistical Approach to Evaluating the Efficacy of Career Guidance Programs on University Graduate Employability in China Li Guo; Thosporn Sangsawang; Piyanan Pannim Vipahasna; Matee Pigultong; Sulaganya Punyayodhin; Kanokwan Darboth
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.v5i1.172

Abstract

This study aimed to develop a career guidance model for improving employment ability among Chinese undergraduate students and assess the impact of this model on students’ employment ability. The research involved 17 Chinese experts and 100 instructors from 10 universities in Sichuan, China. The Delphi technique was employed to gather expert perspectives, while data on employment ability were collected using the College Student Employment Ability Questionnaire. The Cronbach's coefficient of the questionnaire is .869, and Cronbach's α .80 indicates excellent internal consistency, affirming the authenticity and credibility of the data in this study. Based on the statistical criteria defined from the results of the fourth-round inquiries, each Course needs to meet any two of the following conditions: arithmetic x ̅ 3.5964, Full Score Rate .1020, and Cronbach's α .3883 to be preliminarily retained. The results of the third-round expert inquiries show that the course offerings meet the Arithmetic x ̅ 3.3548 criteria, Full Score Rate .1987, and Cronbach's α .5590. The study found a significant improvement in students’ employment ability after participating in the model, with the average score increasing from 16.11 to 20.33. These results underscore the effectiveness of targeted career guidance in enhancing undergraduate students’ employment prospects. Most experts have passed all courses and course content by this round, with viable ideas identified. Career Education and Orientation received the highest response percentage (90.67%), followed by self-assessment (89.50%), industry-oriented skill development (87.50%), mentor support and networking (85.50%), industry insights and trend analysis (89.50%), job search and application assistance (90.80%), continuous review and improvement (87.50%), and follow-up counseling and support (89.50%).
Unveiling Entrepreneurial Development in Data Science Using CCIP-PF Model and Statistical Analysis Junhua Zhong; Sutthiporn Boonsong; Issara Siramaneerat; Thosporn Sangsawang; Pakornkiat Sawetmethikul
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.v5i1.168

Abstract

This study aims to explore the intricacies of entrepreneurial development within the realm of data science, shedding light on both internal and external factors that play pivotal roles in shaping the entrepreneurial landscape. Through the lens of the CCIP-PF model and employing rigorous statistical analysis, this research endeavors to provide insights crucial for fostering entrepreneurial growth in this dynamic field. The objectives of this study are: 1)To develop the CCIP-PF model and establish an assessment index system for mental health literacy training in junior high schools; and 2)To apply the evaluation index system to junior high school mental health literacy training, thereby promoting the enhancement of educational quality. The sample group consisted of 17 experts who participated in discussions and generated 162 viewpoints on the constituent elements of evaluation for junior high school mental health literacy training. The methodology employed the Delphi method; the instrument utilized a qualitative assessment approach, employing questionnaires to ensure anonymity and provide reliable feedback. A questionnaire survey was conducted among 422 participants in Sichuan Province's relevant educational administrative authorities, middle school mental health education teachers, university lecturers and professors in mental health education, and psychological counselors. The response rate reached 96.2%. The study analyzed the data using mathematical statistics and SPSSAU22.0, focusing on the reliability of the entire questionnaire and its dimensions. The findings of this study are as follows:1)primary indicators at mean 4.794, SD = 0.473, IQR = 0.125; secondary indicators at mean 4.823, SD = 0.379, IQR = 0.25; tertiary indicators at mean 4.790, SD = 0.424, IQR = 0.302. A factor contribution rate of 74.175% demonstrates efficacy. 2)Empirical research was conducted in various districts of Zigong City, yielding outcomes that align with reality and meet the anticipated objectives.
Quantitative Analysis of Educational Techniques for Psychological Development in Vocational Students in China Shuang Li; Thosporn Sangsawang; Narumom Thepnuan; Matee Pigultong; Sulaganya Punyayodhin; Kanokwan Darboth
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.v5i1.173

Abstract

The research of objective were to: 1) examines environment, educational system, teacher-student relationship, self-awareness, and other aspects affecting Chinese vocational school students' psychological quality., and 2) development Psychological Quality for Vocational School Students in China Model for address unique psychological challenges and foster personal development in vocational education. Populations and sampling group were stents tests 7,000 Zigong, Rong County, and Dujiangyan vocational and technical students. The questionnaires used a percentage- based scoring standard, with a score below 50 indicating “strongly disagree,” 51 to 70 indicating “neutral,” 71 to 90 indicating “moderately agree,” and 91 to 100 indicating “strongly agree.” Data processing affects Zigong, Rong County, and Dujiangyan Chinese vocational school students' mental health. Statistical percentage of students picking each option. Guttman half coefficient was .802 after Split-Half Method testing of the data, indicating good split-half reliability and internal consistency. The questionnaire reveals how survey questions, sample size, and data processing affect Chinese vocational school students' mental health. The questions asked Zigong, Rong County, and Dujiangyan vocational and technical school students about mental health. 4,768 people completed 6,458 surveys. After deleting 97 low-reliability questionnaires with similar answers to seven consecutive items, 4,671 were valid. The Countermeasure Developing Model in China enhances the psychological quality of vocational school students by implementing multi-level therapy, methodical mental health education, and a supportive learning environment.
Data-Driven Analysis of Teaching Quality Impact on Graduate Employment in Higher Vocational Colleges of Hefei Ning Wang; Tiamyod Pasawano; Thosporn Sangsawang; Matee Pigultong
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.v5i1.169

Abstract

The objectives were to identify the influence of teaching quality in higher vocational colleges on the employment quality of graduates, and to develop instructional design through both theoretical and empirical analysis, to synthesize the relationships among teaching quality, human capital, and employment quality. In collaboration with 17 experts, they were selected through purposive sampling and involving 100 instructors within higher vocational colleges in China. The instruments using the Delphi Technique through a round questionnaire of vocational colleges' teaching quality positively influenced both graduates' human capital and employment quality. The findings revealed that vocational colleges' teaching quality positively influenced both graduates' human capital and employment quality. Vocational education has a favorable effect on employment quality, with human capital playing a crucial role in enhancing teaching quality. This paper distributed 600 questionnaires in total and collected 527 valid questionnaires, with an effective recovery rate of 87.83%. Data processing and analysis were carried out on the valid questionnaires. However, the relationship between teaching quality and employment quality is mediated by professional cognition and growth ability. These results offer important insights for vocational colleges, pointing to the crucial significance of human capital and educational quality in improving employment quality. In higher vocational colleges, the study investigates the connection between human capital, employment quality, and instructional quality. The teaching quality positively affects graduates' human capital and employment quality, according to data from Hefei grads. The link between teaching and learning is moderated by human capital. The research uses AMOS software to analyze vocational teaching variables, revealing a direct effect of higher colleges' teaching quality on graduates' employment quality and human capital. The significance level of these effects is .001, indicating a strong capacity for explanatory reasoning.
Co-Authors Issara Siramaneerat Junhua Zhong Kanokwan Darboth Kitipoom Vipahasna Li Guo Lihua Huang Lin Zhou Matee Pigultong Narumom Thepnuan Ning Wang Pakornkiat Sawetmethikul Piyanan Pannim Vipahasna Rui Chen Shuang Li Sulaganya Punyayodhin Sutthiporn Boonsong Sutthiporn Boonsong Tiamyod Pasawano Tingbin Wen Zhang Zhang