Abstract
In the context of the popularization and diversified application of information technology in higher education, efficient information dissemination has a significant impact on the learning effect of the learning community. Improving the efficiency of information dissemination and driving the force of learning to enhance the learning effect are the hot issues in the field of higher education data analysis. This paper proposes a new method of feature fusion using information entropy and ReliefF algorithm, applies the improved PageRank algorithm and K-means algorithm to optimize the information transfer mode, and finally develops a new and efficient network information model. The comparative test results show that the new model can complete the dissemination of the same amount of information with a smaller delivery ratio. The research results can play an advantageous role in information interaction feedback, curriculum quality analysis, and teaching information transmission.
Highlights
Learning activities will be attributed to learning output
High-quality learning output is affected by many factors, such as students’ learning drive, learning methods, and learning environment
New information technology breaks through the time and space limitations of traditional teaching methods, and at the same time, a new kind of educational productivity is born
Summary
Learning activities will be attributed to learning output. High-quality learning output is affected by many factors, such as students’ learning drive, learning methods, and learning environment. New information technology breaks through the time and space limitations of traditional teaching methods, and at the same time, a new kind of educational productivity is born It is this new product that will affect students’ learning drive and other factors, thereby determining the learning level of output quality. Combining the fact that information technology is used in the general trend of education and teaching, this article intends to analyze the improvement path of the effectiveness of information dissemination based on the characteristics of the learning community from the technical level [2]. By establishing an information dissemination model, using real information data to verify the pros and cons of model assumptions is a very effective way to achieve this goal [10,11,12] In this context, this article conducted a simulation study on the characteristics of university information network groups and information dissemination methods.
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