PROBLEM-BASED LEARNING VIA CHATBOT FOR ADVANCE TOPIC IN COMPUTER COURSE

Main Article Content

Sarawut Markchit

Abstract

This research developed the problem-based learning management via chatbot. The sample employed in this research was a group of 20 third-year undergraduate students majoring in Computer Education selected by purposive sampling method, who enrolled in Advance Topic in computer course of education faculty, Suratthani Rajabhat University. We also assessed the learning achievement, evaluated the effectiveness of the lesson, and assessed the satisfaction towards the use of chatbot in teaching and learning process. This study is a research and development (R&D). The topic of Fundamental of Python Programming was chosen to develop as the lesson via chatbot on Dialogflow platform through LINE application. The developed chatbot is a keyword-based conversation. The Natural Language Processing (NLP) was used to translate the keyword question from a student and respond the answer according to the pattern of conversation that chatbot had set. The research instruments were 1) Lessons using chatbots, 2) problems or questions during learning, 3) pre-test and post-test, and 4) satisfaction assessment towards the use of chatbot. The statistics used to analyze the data were percentage, standard deviation (S.D.) and t-test. The results showed that 1) the learning efficiency (E1/E2) was higher than the standard criterion at 83.91/81.13, 2) the post-learning achievement of students treated by problem-based learning management via chatbot was higher than the pre-learning counterpart learning at the .05 level of statistical significance, and 3) the satisfaction towards the use of chatbot in teaching and learning process was at a high level (gif.latex?\bar{x}= 3.74, SD = 0.77).

Article Details

How to Cite
Markchit, S. (2022). PROBLEM-BASED LEARNING VIA CHATBOT FOR ADVANCE TOPIC IN COMPUTER COURSE. Journal of Industrial Education, 21(1), 44–55. Retrieved from https://ph01.tci-thaijo.org/index.php/JIE/article/view/246928
Section
Research Articles

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