Application of video adaptive sampling based on soft computing in higher education evaluation and E-learning interactive environment

Abstract

The level of teaching quality largely reflects the overall strength of universities and has a direct impact on the quality of cultivating high-quality talents. In this context, higher education institutions are constantly striving to explore innovative methods, but this diverse educational environment also brings challenges to the evaluation methods of teacher teaching quality. This article explores the application of video adaptive sampling in AI data analysis of university education evaluation based on soft computing technology. In order to solve the data sampling problem in university education evaluation, this paper proposes a video adaptive sampling method based on soft computing. In this paper, an E-learning virtual interactive system is designed, multimedia teaching materials matching the requirements of E-learning environment are created, tools suitable for E-learning virtual interactive environment are selected and integrated, soft computing technology is adopted, intelligent algorithms and data analysis methods are combined. By extracting and analyzing the key information in the video, the video sampling strategy is dynamically adjusted to realize the adaptive video sampling. Modeling the content characteristics of videos through fuzzy logic, and using genetic algorithms to optimize the sampling frequency and accuracy of videos. The experimental results indicate that the video adaptive sampling method based on soft computing can effectively improve the accuracy and efficiency of AI data analysis in university education evaluation. Compared with traditional fixed sampling methods, this method can dynamically adjust according to the characteristics of video content, reduce redundant data sampling, and improve the accuracy of data analysis.