Please use this identifier to cite or link to this item:
https://elib.vku.udn.vn/handle/123456789/2312
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pham, Vu Thu Nguyet | - |
dc.contributor.author | Nguyen, Quang Chung | - |
dc.contributor.author | Nguyen, Van To Thanh | - |
dc.contributor.author | Ho, Thanh Phong | - |
dc.contributor.author | Nguyen, Quang Vu | - |
dc.date.accessioned | 2022-08-17T01:51:22Z | - |
dc.date.available | 2022-08-17T01:51:22Z | - |
dc.date.issued | 2022-07 | - |
dc.identifier.issn | 978-604-84-6711-1 | - |
dc.identifier.uri | http://elib.vku.udn.vn/handle/123456789/2312 | - |
dc.description | The 11th Conference on Information Technology and its Applications; Topic: Data Science and AI; pp.51-60. | vi_VN |
dc.description.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. | vi_VN |
dc.language.iso | en | vi_VN |
dc.publisher | Da Nang Publishing House | vi_VN |
dc.subject | Success Prediction | vi_VN |
dc.subject | RNN | vi_VN |
dc.subject | LSTM | vi_VN |
dc.subject | DeepLearning | vi_VN |
dc.title | Next-Term Academic Success Prediction Using Deep Learning | vi_VN |
dc.type | Working Paper | vi_VN |
Appears in Collections: | CITA 2022 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.