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Intelligent and Interactive Chatbot Based on the Recommendation Mechanism to Reach Personalized Learning

Intelligent and Interactive Chatbot Based on the Recommendation Mechanism to Reach Personalized Learning

Ching-Bang Yao, Yu-Ling Wu
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 23
ISSN: 1550-1876|EISSN: 1550-1337|EISBN13: 9781799893561|DOI: 10.4018/IJICTE.315596
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MLA

Yao, Ching-Bang, and Yu-Ling Wu. "Intelligent and Interactive Chatbot Based on the Recommendation Mechanism to Reach Personalized Learning." IJICTE vol.18, no.1 2022: pp.1-23. http://doi.org/10.4018/IJICTE.315596

APA

Yao, C. & Wu, Y. (2022). Intelligent and Interactive Chatbot Based on the Recommendation Mechanism to Reach Personalized Learning. International Journal of Information and Communication Technology Education (IJICTE), 18(1), 1-23. http://doi.org/10.4018/IJICTE.315596

Chicago

Yao, Ching-Bang, and Yu-Ling Wu. "Intelligent and Interactive Chatbot Based on the Recommendation Mechanism to Reach Personalized Learning," International Journal of Information and Communication Technology Education (IJICTE) 18, no.1: 1-23. http://doi.org/10.4018/IJICTE.315596

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Abstract

With the impacts of Covid-19 epidemic, e-learning has become a popular research issue. Therefore, how to upgrade the interactivity of e-learning, and allow learners to quickly access personalized and popular learning information from huge digital materials, is very important. However, chatbots are mostly used in automation, as well as simple occasions of general standard question and answer. But to solve the different problems of e-learners in the learning process, chatbots are used to filter the blind spots of learners and to provide further relevant information, so that e-learning can improve in efficiency and interactivity. This study utilizes AI, two-stage Bayesian algorithm, and crawler technology to provide customized learning materials according to learner's current learning situation. The experimental results show that this research system can indeed correctly understand and judge the blind spots of digital learners, and effectively find the relevant e-learning and video information. The accuracy rate reaches nearly 90%.