Research on the prediction and relationship between academic attention and network attention in chemistry teaching

Abstract

AbstractIn order to adapt to the development of modern education and provide education practitioners with decision‐making suggestions, it is necessary to understand the relationship between the public's attention to basic chemistry education (BCE) and academic attention. However, many existing research is based on a single platform to study social hot information, such as Google Index, Baidu Index, Web of Science, and so on. But they ignore the relationship that exists between the Baidu Index and related academic platforms, and ignore the common information reflected between them. This paper takes advantage of the big data method, through the big data of Baidu Index and the big data obtained from China National Knowledge Internet (CNKI) database, to study the network attention and academic attention of BCE, and propose a CVS‐LSP‐GP framework. It first selects keywords through correlation analysis, secondly uses the data obtained from the first step to construct a nonlinear regression model, and finally combines the results of gray prediction to predict the academic attention of CNKI related to BCE. The research results show that the BCE is mainly affected by the micro‐lecture teaching mode, and relevant education practitioners should integrate the micro‐lecture mode into teaching for more further research and practice.