Analysis of Talent Cultivation Mode of Network and New Media Communication Majors in Colleges and Universities under the Background of Big Data

Abstract

In college education, the introduction of data analysis to quantitatively analyze students’ knowledge has become a new trend. The education of network and new media communication majors can also use big data to assist. To this end, this paper uses knowledge tracking technology to construct an auxiliary teaching model for network and new media communication majors in colleges and universities. A new cross-feature formula is used to incorporate many elements of students’ answers into the feature calculation to improve the operation effect of DKVMN model and avoid the complex operation brought by dimensionality reduction. Introducing the cross formula of guessing rate and error rate refreshment based on cognitive diagnostic model. The judgment level of the model is tested using a public dataset after the model has been constructed and put into practical use in a university’s network and new media communication department. Seventy-three students in the department had a good grasp of the knowledge points and six had an excellent understanding. The adjusted means of the experimental and control groups are 93.102 and 82.596 respectively, and there is a significant difference between the two groups of students’ academic performance (p<0.05). The model in this paper can accurately determine the mastery of students’ knowledge points and provide a development direction based on data analysis for teaching network and new media communication majors in colleges and universities.