Year: 2024
Type: conference-paper
Abstract: The multimodal teaching quality evaluation system realizes the intelligent analysis of teaching process through deep learning technology. The system collects classroom video, text and image data and constructs a deep neural network model for feature extraction and quality evaluation. The experiment collects data from 523 courses for testing, and the results show that the model accuracy rate reaches 90.3%, which is 23.6% higher than the traditional evaluation method. The system is deployed with distributed architecture, the average response time is 120ms, and the user satisfaction is 92.8%. The research results provide a new type of solution for teaching quality evaluation and have good application value.
Author: Shuping Yuan
Institution: Dongying Vocational Institute
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4410210805