Teacher Professional Learning Community and Interdisciplinary Collaborative Teaching Path Under the Informationization Basic Education Model

Abstract

The construction of a learning community cannot be separated from the participation of information technology. The current teacher learning community has problems of low interaction efficiency and insufficient enthusiasm for group cooperative teaching. This study adopts the Latent Dirichlet allocation method to process text data generated by teacher interaction from the evolution of knowledge topics in the learning community network space. At the same time, the interaction data of the network community learning space is used to extract the interaction characteristics between teachers, and a collaborative teaching group is formed using the K-means clustering algorithm. This study verifies the management effect of Latent Dirichlet allocation and Kmeans algorithm in learning community space through experiments. The experiment showed that the Latent Dirichlet allocation algorithm had the highest F1 value at a K value of 12, which is 0.88. It collaborated with the filtering algorithm on the overall F1 value. At the same time, there were a total of 4 samples with incorrect judgments in Latent Dirichlet allocation, with an accuracy of 86.7%, which is higher than other algorithm models. The results indicate that the proposed Latent Dirichlet allocation combined with K-means algorithm has superior performance in the management of teacher professional learning communities, and can effectively improve the service level of teacher work.