Exploration of micro-video teaching mode of college students using deep learning and human–computer interaction

Abstract

In order to improve the efficiency of teaching and learning in Colleges and Universities (CAUs), this work combines the Browser/Server (B/S) framework with Model View Presenter (MVP) technology to build a college student–oriented micro-video teaching system based on Deep Learning (DL) and Human–Computer Interaction (HCI) technology. Firstly, it makes an in-depth analysis of the problems in the classroom teaching of Chinese CAUs. Three functional modules are designed for the micro-video online teaching platform: video management, user learning, and system management. Then, it uses MVP technology to analyze the use-cases of these three functional modules in detail. Based on this, the micro-video online teaching platform is designed using the B/S framework. The teaching platform interface layer realizes the HCI between the platform and users. The business logic layer responds to the user requests submitted and returns the processing results to the interface layer. Finally, the function test and stress test of each module of the micro-video online teaching platform is carried out. The test results show that the response time of the proposed micro-video teaching platform increases with the number of users. Under the peak concurrent users, the system response time is 6 s, without abnormalities. Meanwhile, the proposed teaching platform has improved students' satisfaction with classroom teaching by nearly 15% and improved the compactness of the college classroom by nearly 12%. When the number of virtual users increases and the number of services increases linearly, the Random Access Memory and Central Processing Unit growth rate is significantly lower than that of the number of services. These outcomes indicate that many system resources are reused, and the system has good scalability, which can meet users' needs for the network video teaching system. The proposed teaching platform provides a new idea for applying DL and HCI technology in researching college students' micro-video teaching mode.