HHA: An Attentive Prediction Model for Academic Abnormality
Warning students with poor performance in advance based on historical academic data, namely, the academic abnormality prediction is important task in education. The majority of existing methods focus on digging out abnormal complex clues from historical data, while ignoring two basic considerations:...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9134742/ |
id |
doaj-d578f789ad9e45e786b5cf9ca29f3e9e |
---|---|
record_format |
Article |
spelling |
doaj-d578f789ad9e45e786b5cf9ca29f3e9e2021-03-30T02:18:15ZengIEEEIEEE Access2169-35362020-01-01812475512476610.1109/ACCESS.2020.30077509134742HHA: An Attentive Prediction Model for Academic AbnormalityYawen Zeng0https://orcid.org/0000-0001-9983-4598Yong Ouyang1https://orcid.org/0000-0003-4292-8539Rong Gao2https://orcid.org/0000-0001-7935-7173Ye Qiu3https://orcid.org/0000-0003-2940-436XYonghong Yu4https://orcid.org/0000-0003-2587-8090Chunzhi Wang5https://orcid.org/0000-0002-6742-3644School of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaCollege of Tongda, Nanjing University of Posts and Telecommunications, Nanjing, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaWarning students with poor performance in advance based on historical academic data, namely, the academic abnormality prediction is important task in education. The majority of existing methods focus on digging out abnormal complex clues from historical data, while ignoring two basic considerations:(1)these works fail to handle unrecorded/missing data when this part is sparse; (2)these works ignore the complex relationship between courses. The different courses are used as the attention weight vector for abnormality prediction, but they do not notice the mutual influence between courses. To this end, we contribute a Hybrid Neural Network Model based on High-Order Attention Mechanism, called HHA, to address the academic abnormality prediction problem. Specifically, we first exploit Generative Adversarial Network(GAN) to mine hidden factors in the unrecorded/missing data reasonably by simulating student behavior. Thereafter, a high-order attention mechanism is proposed to measure the importance of course and course combination. Lastly, a multi-layer projection abstracts feature and classifies whether the student is abnormal. By experimenting on real-world dataset, we demonstrate the effectiveness and rationality of our proposed model.https://ieeexplore.ieee.org/document/9134742/Academic abnormality predictionhigh-order attention mechanismhybrid neural networkgenerative adversarial network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yawen Zeng Yong Ouyang Rong Gao Ye Qiu Yonghong Yu Chunzhi Wang |
spellingShingle |
Yawen Zeng Yong Ouyang Rong Gao Ye Qiu Yonghong Yu Chunzhi Wang HHA: An Attentive Prediction Model for Academic Abnormality IEEE Access Academic abnormality prediction high-order attention mechanism hybrid neural network generative adversarial network |
author_facet |
Yawen Zeng Yong Ouyang Rong Gao Ye Qiu Yonghong Yu Chunzhi Wang |
author_sort |
Yawen Zeng |
title |
HHA: An Attentive Prediction Model for Academic Abnormality |
title_short |
HHA: An Attentive Prediction Model for Academic Abnormality |
title_full |
HHA: An Attentive Prediction Model for Academic Abnormality |
title_fullStr |
HHA: An Attentive Prediction Model for Academic Abnormality |
title_full_unstemmed |
HHA: An Attentive Prediction Model for Academic Abnormality |
title_sort |
hha: an attentive prediction model for academic abnormality |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Warning students with poor performance in advance based on historical academic data, namely, the academic abnormality prediction is important task in education. The majority of existing methods focus on digging out abnormal complex clues from historical data, while ignoring two basic considerations:(1)these works fail to handle unrecorded/missing data when this part is sparse; (2)these works ignore the complex relationship between courses. The different courses are used as the attention weight vector for abnormality prediction, but they do not notice the mutual influence between courses. To this end, we contribute a Hybrid Neural Network Model based on High-Order Attention Mechanism, called HHA, to address the academic abnormality prediction problem. Specifically, we first exploit Generative Adversarial Network(GAN) to mine hidden factors in the unrecorded/missing data reasonably by simulating student behavior. Thereafter, a high-order attention mechanism is proposed to measure the importance of course and course combination. Lastly, a multi-layer projection abstracts feature and classifies whether the student is abnormal. By experimenting on real-world dataset, we demonstrate the effectiveness and rationality of our proposed model. |
topic |
Academic abnormality prediction high-order attention mechanism hybrid neural network generative adversarial network |
url |
https://ieeexplore.ieee.org/document/9134742/ |
work_keys_str_mv |
AT yawenzeng hhaanattentivepredictionmodelforacademicabnormality AT yongouyang hhaanattentivepredictionmodelforacademicabnormality AT ronggao hhaanattentivepredictionmodelforacademicabnormality AT yeqiu hhaanattentivepredictionmodelforacademicabnormality AT yonghongyu hhaanattentivepredictionmodelforacademicabnormality AT chunzhiwang hhaanattentivepredictionmodelforacademicabnormality |
_version_ |
1724185461700689920 |