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General Information

Prof. Pascal Lorenz
University of Haute Alsace, France
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.

IJFCC 2017 Vol.6(3): 102-105 ISSN: 2010-3751
doi: 10.18178/ijfcc.2017.6.3.498

An Achievement Prediction Method on the Video Learning Time Based on EKF

Jun Xiao and Hongliang Gu
Abstract—In China, the prediction of the examination achievement is important in the field of online learning, for both the learners and the service providers. On the other hand, the learners' online learning time, e.g. the time of watching the teaching video, can be acquired and recorded easily on most learning platforms. In this paper, we try to establish an EKF model on the exam/test achievement and the learning time, and use the model to predict the achievement in the next exam. The experiment on a two-semester network education course proves, the method can achieve the prediction precision, which is not inferior to the classic methods, and meanwhile own some advantages such as, the capability of noise resistance, uniform convergence, and controllable complexity etc.

Index Terms—Achievement prediction, extended Kalman filter, online learning.

Jun Xiao is with Shanghai Open University, Shanghai, China (e-mail: xiaoj@shtvu.edu.cn).
Hongliang Gu was with Shanghai Jiao Tong University, Shanghai, China (e-mail: hlgu@sjtu.edu.cn).


Cite: Jun Xiao and Hongliang Gu, "An Achievement Prediction Method on the Video Learning Time Based on EKF," International Journal of Future Computer and Communication vol. 6, no. 3, pp. 102-105, 2017.

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