Knowledge Dissemination Model Based on Graph Neural Network
- 10.2991/978-2-494069-31-2_370How to use a DOI?
- Basic Knowledge propagation model; Semi supervised learning; GCN model; Graph neural network
Explore the application of GCN model to the prediction of knowledge transmission model. Teaching abstract into neural network, neural network model parameters education meaning, the model in the frequency domain space, design different convolution kernels, finally by GCN model of common teaching model, discusses several typical teaching model, the results of quantitative and qualitative analysis and comparison of practical teaching results, the rationality of the application of evaluation and scientific. The essence of different teaching organization modes of teachers is to adjust the graph neural network to predict the teaching effect through the model. Combined with the complexity of education and teaching process, the results of artificial intelligence algorithm are in line with the reality of education within the scope of assumptions, which is conducive to knowledge dissemination. Innovation will be applied to the education of the general consumption model. Without a once and for all teaching method, teachers should fully establish various teaching information platforms, cooperate with teaching and reasonable interaction, scientifically organize classroom teaching, establish learning groups composed of students who have mastered some knowledge, and speed up the efficiency of knowledge dissemination.
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Cite this article
TY - CONF AU - Wei-ming Liu PY - 2022 DA - 2022/12/29 TI - Knowledge Dissemination Model Based on Graph Neural Network BT - Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022) PB - Atlantis Press SP - 3157 EP - 3163 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-31-2_370 DO - 10.2991/978-2-494069-31-2_370 ID - Liu2022 ER -