The Realization of the Practical Ability Training System of Media Talents in OBE Mode Based on Network Information Technology

Abstract

With the development of social economy, the development of network information technology is getting faster and faster, and the competition in the network media industry is becoming more and more fierce. This kind of competition not only exists in China, but also fiercely in the world. International competition is not only conducive to the development of the entire network media industry in China, but also very important to the development of network media companies themselves. The current domestic online media market continues to open up to the outside world, and a large number of foreign online media companies have been introduced. This situation has brought development opportunities for our country’s online media companies, and at the same time brought corresponding fierce competition. With the advent of the era of smart media, the traditional media talent training model must be innovative in order not to be out of touch with the needs of the society. Improving the training of their practical ability is a top priority. This article discusses the practical ability of media talent training from various aspects such as the formulation and modification of talent training programs, strengthening the reserve of dual-qualified talents, building internship base construction, and improving the quality of graduation design. This article believes that the practice ability training of media talents should be improved in an all-round way based on the “output orientation” of the OBE concept. Based on this, a media talent practice ability training system under the OBE model is proposed, and the practice ability training of media talents under the OBE model is proposed. The system comprehensively sorts out the mixed information and reflects the data to make management transparent, rational and informatized, thereby improving the efficiency of talent training. Research shows that the average value of Transaction per second is 65.21, which shows that the number of transactions per second processed by the system can well simulate real information query use cases. The average transaction response time is 84.21 s, which is too long. But if the peak response time of 170 s is not considered, the system transaction processing time will eventually stabilize between 30 s, which meets the performance requirements.