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All Journal Journal of Applied Data Sciences International Journal for Applied Information Management
Pasawano, Tiamyod
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Research on the Influencing Factors of College Students' Deep Meaningful Learning in Blended Learning Mode Li, Shu; Pasawano, Tiamyod; Sangsawang, Thosporn
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.254

Abstract

This study examines the factors that impact deep and meaningful learning in blended learning environments and their connections. The sample included 397 college students from a university in Sichuan Province, selected through random sampling. Data was collected using a questionnaire based on Bandura's ternary interaction theory, encompassing learners, helpers, environment, and interaction dimensions. The following text should be remembered: "Hypotheses were developed based on existing literature, and a survey with established scales was created. Quantitative analysis was conducted using SPSS and AMOS software. The mean, standard deviation, Variance, skewness, and kurtosis values were within reasonable ranges. The model's latent variables showed strong convergent validity, with standardized factor loadings (SFL) ranging from 0.807 to 0.965, average Variance extracted (AVE) from 0.697 to 0.946, and composite reliability (C.R.) from 0.919 to 0.946. Model fit indices indicated acceptable fit (CMIN/DF: 2.303, NFI: 0.966, CFI: 0.980, RMSEA: 0.058, RMR: 0.008, PNFI: 0.789). The study optimized the model through path analysis, culminating in the final structural equation model (SEM)." Findings indicate (1) Learner, environmental, and interaction factors positively influence deep meaningful learning, while helper factors show a negative correlation; (2) learner, interaction, and helper factors mediate the environment's impact on deep, meaningful learning; and (3) environmental factors hold the most significant sway over helper factors, followed by interaction and learner factors. Helpers wield significant influence over learners, enhancing deep understanding. These insights guide effective, deep, meaningful learning strategies in blended learning
Data-Driven Analysis of Teaching Quality Impact on Graduate Employment in Higher Vocational Colleges of Hefei Wang, Ning; Pasawano, Tiamyod; Sangsawang, Thosporn; 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.v5i1.169

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.
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.
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.
Predicting AI Service Focus in Companies Using Machine Learning: A Data Mining Approach with Random Forest and Support Vector Machine Sangsawang, Thosporn; Tang, Lin; Pasawano, Tiamyod
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.83

Abstract

This study investigates the prediction of AI service focus in companies using machine learning models. The primary objective is to predict the percentage of AI service focus based on company characteristics such as project size, hourly rate, number of employees, and geographical location. Two machine learning models, Random Forest Regressor and Support Vector Regressor (SVR), were trained and evaluated to determine their effectiveness in predicting AI adoption. The dataset consists of 3099 companies, with key features cleaned and preprocessed, including the transformation of categorical variables into numerical ones using one-hot encoding and imputation techniques applied to handle missing values. The Random Forest model demonstrated better performance, with an R² value of 0.12, indicating a modest ability to explain the variance in AI service focus. In contrast, the SVR model had a negative R² value of -0.03, suggesting that it struggled to capture the underlying relationships in the data. The analysis identified project size and hourly rate as the most significant predictors of AI service focus, with larger projects and higher hourly rates correlating with a greater emphasis on AI services. Despite the relatively low performance of both models, this research provides valuable insights into the factors that influence AI adoption. The findings emphasize the importance of project-related characteristics in determining a company's AI service focus. However, the study is limited by missing data and the absence of additional features that could further improve prediction accuracy. Future research could benefit from incorporating more business-specific features and advanced modeling techniques to enhance the predictive power and generalizability of the model.
Co-Authors Li, Shu Pigultong, Matee Sangsawang, Thosporn Tang, Lin Wang, Ning