Analyzing and Tracking Student Educational Program Interests on Social Media with Chatbots Platform and Text Analytics

Analyzing and Tracking Student Educational Program Interests on Social Media with Chatbots Platform and Text Analytics

Applied Informatics

Text Analytics

Educational Data Mining

Eruptive Technology

Technology-Enhanced Learning

This research presents a chatbot application to provide educational information for university students. There are three objectives: 1) to study the problem of providing information to university students with chatbots, 2) to develop a model and construct a chatbot to predict the interest of university students, and 3) to assess the satisfaction of the information provided by the chatbot application. The research datasets were the conversations from the Messenger Facebook Page of the Faculty of Information Technology, Rajabhat Maha Sarakham University, during the academic year 2020-2021. In total, there were 1,094 transactions used in this research work. Furthermore, data mining and machine learning techniques, including CRISP-DM, Naïve Bayes, K-Nearest Neighbors, and Neural Network, were used as the research tools. The cross-validation and confusion matrix techniques were used to test the model performance. Moreover, a questionnaire was the application satisfaction assessment tool for 30 respondents. As a result, it showed that the developed model provided high-level results, which are 88.73% accuracy and an average of 3.97 for application satisfaction. In the future, the researchers plan to apply the results for the next academic year and expand into other academic programs.

Nasa-Ngium, Patchara

Nuankaew, Wongpanya Sararat

Nuankaew, Pratya

International Journal of Interactive Mobile Technologies (iJIM); Vol. 17 No. 05 (2023); pp. 4-21

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2023-03-07

Copyright (c) 2023 Patchara Nasa-Ngium, Wongpanya Sararat Nuankaew, Pratya Nuankaew

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article