MFDS-STGCN: Predicting the Behaviors of College Students With Fine-Grained Spatial-Temporal Activities Data-Reference-Cited by-同舟云学术

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MFDS-STGCN: Predicting the Behaviors of College Students With Fine-Grained Spatial-Temporal Activities Data

Published:2024-01 Issue:1 Volume:12 Page:254-265
ISSN:2168-6750
Container-title:IEEE Transactions on Emerging Topics in Computing
language:
Short-container-title:IEEE Trans. Emerg. Topics Comput.

Author:

Zhou Dongbo¹^ORCID,Yu Hongwei²^ORCID,Yu Jie³^ORCID,Zhao Shuai¹^ORCID,Xu Wenhui¹^ORCID,Li Qianqian¹^ORCID,Cai Fengyin¹^ORCID

Affiliation:

1. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China

2. College of Software, Beihang University, Beijing, China

3. State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6245516/10474150/10379042.pdf?arnumber=10379042

Reference42 articles.

1. Predicting Academic Performance for College Students

2. Observations of Student Behavior in Nonclassroom Settings

3. Annotated Examples and Parameterized Exercises: Analyzing Students’ Behavior Patterns

4. Educational Big Data: Predictions, Applications and Challenges

5. StudentLife

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1. APTrans: Transformer-Based Multilayer Semantic and Locational Feature Integration for Efficient Text Classification;Applied Sciences;2024-06-04

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