The Development of Chatbot System for Cooperative and Work-Integrated Education Using Machine Learning Method
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Abstract
The objectives of this research ware 1) to create a model and determine the efficiency of question classification models, 2) to develop a chatbot system for cooperative and work-integrated education, and 3) to investigate user satisfaction with the chatbot system for cooperative and work- integrated education. The target group consisted of 68 fourth-year students majoring in Information Technology at the Faculty of Information Technology, Rajabhat Maha Sarakham University, who were undertaking cooperative education in the academic year 2022. The research tools used included: 1) a dataset of questions related to cooperative and work-integrated education, 2) a chatbot system for cooperative and work-integrated education, 3) a user satisfaction survey questionnaire for the chatbot system. The research findings suggest that among the models developed and tested for classifying question types, the Decision Tree and Multilayer Perceptron models achieved the highest accuracy at 93.7%, while the K-Nearest Neighbors method reached 78.4%. Additionally, the developed chatbot system for cooperative and work-integrated education, integrated into the Line application, demonstrated accurate and efficient question-answering capabilities when paired with the model. Moreover, the overall satisfaction evaluation of the chatbot system revealed a high level of satisfaction, with an average score of 4.63 and a standard deviation of 0.65. Overall, the research indicates that the developed chatbot system operates efficiently and meets users' needs.
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