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 Next-Term Academic Success Prediction Using Deep Learning
Tác giả hoặc Nhóm tác giả: Vu-Thu-NguyetPham, Quang-ChungNguyen, Van-To-Thanh Nguyen, Thanh-Phong Ho, Quang-Vu Nguyen
Nơi đăng: The 11th Conference on Information Technology and its Applications - CITA 2022; Số: ISBN: 978-604-84-6711-1;Từ->đến trang: 51-60;Năm: 2022
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
As students proceed through their university degrees, they are confronted with a plethora of course options. It is critical that they get assistance based not only on their interests, but also on the "predicted" course achievement, in order to improve their learning experience and academic success. In this study, we suggest the next-term grade prediction task as a suitable course selection guide. We offer a machine learning framework for predicting course success in a certain term based on prior student-course data. In this framework, we create a prediction model utilizing Long Short Term Memory (LSTM) that takes into account both student and course qualities as well as previous student-course grade data.
ABSTRACT
As students proceed through their university degrees, they are confronted with a plethora of course options. It is critical that they get assistance based not only on their interests, but also on the "predicted" course achievement, in order to improve their learning experience and academic success. In this study, we suggest the next-term grade prediction task as a suitable course selection guide. We offer a machine learning framework for predicting course success in a certain term based on prior student-course data. In this framework, we create a prediction model utilizing Long Short Term Memory (LSTM) that takes into account both student and course qualities as well as previous student-course grade data.
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