The construction of a multi-platform system for teaching journalism and communication based on big data technology

Building experimental teaching platforms and analyzing student learning data to help improve instructional design are trends in the context of the big data era. This paper combines the innovative multi-type practical teaching theory to build an all-media experimental platform with three departments, namely, gathering department, editorial department and production department, and makes it available to journalism learners. The fuzzy C-mean clustering algorithm based on DTW is designed and applied to analyze the data of the all-media experimental platform. Combine the platform data to analyze the types of learners and the characteristics of learning behaviors of each category, and provide teaching improvement methods for teachers based on the relevant analysis results. By analyzing the data of the all-media experimental platform, it can be seen that there is an overall correlation between the students’ learning performance and the number of days of using the platform, the number of interactions, as well as the number of chapters studied, the number of forum speeches, and the number of horizontal playbacks. Students were further categorized into a total of six categories such as focused learners, continuous learners, etc. In which continuous learners accounted for the largest proportion of 38%, while abandoned learners accounted for the smallest proportion of only 2%, and other categories of learners accounted for between 4% and 25%. Improving teaching planning based on the results of platform data analysis will effectively improve the skill level of news communication learners and lay the foundation for their subsequent path to the news communication profession.