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 Transforming enrollment advising in education with deep learning models and chatbots
Tác giả hoặc Nhóm tác giả: Nguyen Nang Hung Van, Ho Le Minh Nhat, Vo Duc Hoang, Do Phuc Hao
Nơi đăng: Hội nghị quốc gia - FAIR 2023; Số: 987-604-357-119-6;Từ->đến trang: 188 - 195;Năm: 2023
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
Enrollment advising plays a pivotal role in guiding aspiring students throughout the university admissions process. The emergence of deep learning models and chatbot technologies has opened up new avenues for redefining the approach to enrollment advising. This article centers its attention on the creation of customized datasets catered specifically to admissions consultants, elucidating the data preprocessing procedures employed to enhance the quality of information utilized in advising. Furthermore, the article demonstrates the effective application of deep learning models in analyzing and interpreting the acquired data, facilitating precise predictions and individualized recommendations for prospective students. Lastly, the exploration extends to
the implementation of these deep learning models within chatbot interfaces, offering an engaging and accessible platform for students to interact with enrollment advisors. The fusion of deep learning models and chatbots harbors the potential to revolutionize the landscape of enrollment advising, leading to enhanced efficiency, scalability, and an enriched student experience.
ABSTRACT
Enrollment advising plays a pivotal role in guiding aspiring students throughout the university admissions process. The emergence of deep learning models and chatbot technologies has opened up new avenues for redefining the approach to enrollment advising. This article centers its attention on the creation of customized datasets catered specifically to admissions consultants, elucidating the data preprocessing procedures employed to enhance the quality of information utilized in advising. Furthermore, the article demonstrates the effective application of deep learning models in analyzing and interpreting the acquired data, facilitating precise predictions and individualized recommendations for prospective students. Lastly, the exploration extends to
the implementation of these deep learning models within chatbot interfaces, offering an engaging and accessible platform for students to interact with enrollment advisors. The fusion of deep learning models and chatbots harbors the potential to revolutionize the landscape of enrollment advising, leading to enhanced efficiency, scalability, and an enriched student experience.
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