TY - GEN
T1 - Intelligent educational data analysis with gaussian processes
AU - Wang, Jiachun
AU - Zhao, Jing
AU - Sun, Shiliang
AU - Shi, Dongyu
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - As machine learning evolves, it is significant to apply machine learning techniques to the intelligent analysis on educational data and the establishment of more intelligent academic early warning system. In this paper, we use Gaussian process (GP)-based models to discover valuable inherent information in the educational data and make intelligent predictions. Specifically, the mixtures of GP regression model is adopted to select personalized key courses and the GP regression model is applied to predict the course scores. We conduct experiments on real-world data which are collected from two grades in a certain university. The experimental results show that our approaches can make reasonable analysis on educational data and provide prediction information about the unknown scores, thus helping to make more precise academic early warning.
AB - As machine learning evolves, it is significant to apply machine learning techniques to the intelligent analysis on educational data and the establishment of more intelligent academic early warning system. In this paper, we use Gaussian process (GP)-based models to discover valuable inherent information in the educational data and make intelligent predictions. Specifically, the mixtures of GP regression model is adopted to select personalized key courses and the GP regression model is applied to predict the course scores. We conduct experiments on real-world data which are collected from two grades in a certain university. The experimental results show that our approaches can make reasonable analysis on educational data and provide prediction information about the unknown scores, thus helping to make more precise academic early warning.
KW - Academic early warning
KW - Course score prediction
KW - Gaussian process regression
KW - Key course selection
KW - Mixtures of Gaussian processes
UR - https://www.scopus.com/pages/publications/85059032584
U2 - 10.1007/978-3-030-04224-0_30
DO - 10.1007/978-3-030-04224-0_30
M3 - 会议稿件
AN - SCOPUS:85059032584
SN - 9783030042233
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 353
EP - 362
BT - Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
A2 - Cheng, Long
A2 - Leung, Andrew Chi Sing
A2 - Ozawa, Seiichi
PB - Springer Verlag
T2 - 25th International Conference on Neural Information Processing, ICONIP 2018
Y2 - 13 December 2018 through 16 December 2018
ER -