Evaluation of Physical Education Multimedia Teaching for Data Assimilation
The evaluation of physical education (PE) multimedia teaching refers to the prediction of physical education multimedia teaching quality in the absence of initial multimedia teaching information. Therefore, the evaluation method of PE multimedia teaching based on unsupervised feature learning has achieved good performance. But, its quality prediction accuracy decreases significantly with the reduction of the feature dimension. In order to overcome this defect, the author combines the active learning strategy with the unsupervised feature learning and proposes a kind of data assimilation framework to improve the discriminability of the representation of teaching features. The results show that the proposed method can enhance the accuracy of teaching quality prediction by 8%. Experiments show that, when feature dimension is relatively low, the proposed method can improve the teaching quality prediction accuracy by 8% compared with the method based on unsupervised feature learning. At the same time, the performance of the proposed method is superior to that of the other physical education multimedia teaching evaluation methods at present.
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