Self-adaptive Education Resource Allocation Using BERT Model
Yi Wen · 2024
The balanced allocation of educational resources plays an important role in improving the quality of education and promoting educational equity. The current configuration models are difficult to understand resource information features and have limitations in automatic learning of features and resource classification. To improve the effectiveness of resource allocation and enhance the level of educational development, this article combines the BERT (Bidirectional Encoder Representations from Transformers) model to study self-adaptive allocation of educational resources. By using a brand new pre-trained language representation, the features of educational resources are extracted and interpreted, and the allocation scheme is optimized using a fitness function defined by heuristic algorithms. In the experimental analysis, this article uses the SVM (Support Vector Machine) model and CNN (Convolutional Neural Network) model as references to verify the application effect of the BERT model. The results showed that in the analysis of configuration effects, after training and optimizing the BERT model, the student-teacher ratio of various universities achieved significant improvement, with specific proportion values below 10.00. It can be concluded that self-adaptive allocation of educational resources based on the BERT model can help to achieve balanced development of educational resources and promote high-quality development of education.