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p-Index From 2021 - 2026
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All Journal Journal of Applied Data Sciences International Journal for Applied Information Management
Tang, Lin
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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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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 Pasawano, Tiamyod Sangsawang, Thosporn