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    Enhancing Student’s Learning by Integrating the Concept of Project-Based and Challenge-Based Learning
    (2025-01-01)
    Kularbphettong, Kunyanuth
    ;
    Raksuntorn, Nareenart
    ;
    Roonrakwit, Pattarapan
    ;
    Boonseng, Chongrag
    This study aims to enhance student learning in the introductory data science course by combining Project-Based and Challenge-Based Learning methodologies. The goal is to improve student performance, engagement, and comprehend of data science subjects through research and development. By integrating challenge-based learning (CBL) and project-based learning (PBL) into a cohesive teaching approach, the benefits of both approaches are employed to improve the learning experience for students. This methodology facilitates in-depth comprehension, critical reasoning, and practical application of data science skills, preparing students for academic success and real-world obstacles. The research sample comprised 56 third-year undergraduates from the Computer Science department, selected using a purposive sampling method. Data analysis involves the utilization of several statistical techniques such as the t-test, analysis of covariance (ANCOVA), mean, standard deviation, and other relevant statistics. The findings indicated that students who utilized a blend of Project-Based and Challenge-Based Learning methods had a notable increase in academic performance post-learning, as compared to their pre-learning performance, with statistical significance. Firstly, students at the 05 level are more likely to express their satisfaction with the course of study. This is primarily because the course incorporates problem-based learning (PBL) and case-based learning (CBL), which are both engaging and practical teaching methods. Furthermore, this implies that improving student achievement in the data science course is an effective use of the integrated teaching methodology.
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    Sentiment analysis of the awareness of environmental sustainability
    (2024-01-01)
    Kularbphettong, Kunyanuth
    ;
    Roonrakwit, Pattarapan
    ;
    Boonseng, Chongrag
    This study examines the sentiment analysis of awareness of environmental sustainability. Environmental sustainability is the responsible management and utilization of Earth's natural resources to meet the needs of the present generation and ensure that future generations will access those resources. The awareness of environmental sustainability has been growing globally as people, businesses, and governments recognize the importance of preserving the planet for current and future generations. Sentiment analysis of environmental sustainability involves evaluating opinions, attitudes, and emotions expressed in texts related to environmental sustainability, and analyzing sentiment can provide insights into public perception, awareness, and engagement with environmental issues. This exploratory study's primary goal is to conduct social media opinion mining in the context of Thai people's environmental sustainability. The paper presented how to build a model of sentiment analysis with linguistic analysis, including data preprocessing steps, feature extraction, and model constructions. The techniques used in this research include Logistic Regression, Random Forests, Support Vector Machine, Word Segmentation and Bag of Words. The result shows that the model is able to categorize sentiment analysis opinions in the sustainability context primarily in positive terms. The positive sentiments suggest a sustained, long-term shift in awareness, or they might be influenced by specific events or trends. However, positive sentiment analysis results are expressed towards environmental sustainability initiatives, such as renewable energy projects, waste reduction efforts, or conservation programs. Moreover, public awareness plays a crucial role in influencing individual behavior, corporate practices, and government policies towards a more sustainable and environmentally conscious future.