Abstract
Unlike the off-line teaching, the feedback from the student is hard to capture, so that the teaching effect of the course is hard to be improved. In order to solve this problem and achieve a better teaching effect as the same as the offline one, we propose a method for the feedback of MOOC course by combing PCA (principle component analysis) and SVM (support vector machine). The dataset for analysis are collected from the cameras in the courses, KECA (kernel entropy component analysis) is used here to extract the feature vectors for the dataset. The features are feed into the SVM to achieve the recognition of the students studying feedback. The recognition is finally transferred to a classification process. To verify the priority of our method, our method is compared with the other two methods, the result shows that our method not only has the optimal dimensionality for samples, but also has the higher classification rate.
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Acknowledgments
This work was financially Project supported by the Education Department of Hainan Province, project number:hnjg2017ZD-17. The Hainan Provincial Department of Science and Technology under Grant No. ZDKJ201602 and the Natural Science Foundation of Hainan Province under Grant No. 20156222.
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Chen, Zc. (2019). Automatic Self-feedback for the Studying Effect of MOOC Based on Support Vector Machine. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_27
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DOI: https://doi.org/10.1007/978-3-030-24265-7_27
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