TY - GEN
T1 - Key course selection for academic early warning based on Gaussian processes
AU - Yin, Min
AU - Zhao, Jing
AU - Sun, Shiliang
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Academic early warning (AEW) is very popular in many colleges and universities, which is to warn students who have very poor grades. The warning strategies are often made according to some simple statistical methods. The existing AEW system can only warn students, and it does not make any other analysis for academic data, such as the importance of courses. It is significant to discover useful information implicit in data by some machine learning methods, since the hidden information is probably ignored by the simple statistical methods. In this paper, we use the Gaussian process regression (GPR) model to select key courses which should be paid more attention to. Specifically, an automatic relevance determination (ARD) kernel is employed in the GPR model. The length-scales in the ARD kernel as hyperparameters can be learned through the model selection procedure. The importance of different courses can be measured by these corresponding length-scales. We conduct experiments on real-world data. The experimental results show that our approaches can make reasonable analysis for academic data.
AB - Academic early warning (AEW) is very popular in many colleges and universities, which is to warn students who have very poor grades. The warning strategies are often made according to some simple statistical methods. The existing AEW system can only warn students, and it does not make any other analysis for academic data, such as the importance of courses. It is significant to discover useful information implicit in data by some machine learning methods, since the hidden information is probably ignored by the simple statistical methods. In this paper, we use the Gaussian process regression (GPR) model to select key courses which should be paid more attention to. Specifically, an automatic relevance determination (ARD) kernel is employed in the GPR model. The length-scales in the ARD kernel as hyperparameters can be learned through the model selection procedure. The importance of different courses can be measured by these corresponding length-scales. We conduct experiments on real-world data. The experimental results show that our approaches can make reasonable analysis for academic data.
KW - Academic early warning
KW - Automatic relevance determination kernel
KW - Gaussian process regression
KW - Key course selection
UR - https://www.scopus.com/pages/publications/84989950541
U2 - 10.1007/978-3-319-46257-8_26
DO - 10.1007/978-3-319-46257-8_26
M3 - 会议稿件
AN - SCOPUS:84989950541
SN - 9783319462561
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 240
EP - 247
BT - Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings
A2 - Zhang, Daoqiang
A2 - Gao, Yang
A2 - Yin, Hujun
A2 - Li, Bin
A2 - Li, Yun
A2 - Yang, Ming
A2 - Klawonn, Frank
A2 - Tallón-Ballesteros, Antonio J.
PB - Springer Verlag
T2 - 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016
Y2 - 12 October 2016 through 14 October 2016
ER -