Visual learning analysis of physical virtual simulation experiments based on heterogeneous data features

Abstract

In order to provide a way to develop the teaching effectiveness of physics experiments, this paper optimizes the platform search engine by combining heterogeneous data features and representing document information as feature vectors based on visual learning analysis methods. The algorithm is dynamically adjusted according to the authority to build a network database. And the virtual physics experiments have interacted with virtual experimental equipment to build a physics virtual simulation experiment platform. The results show that the overall level of visual student portrait analysis is above 40%, and the average completion efficiency of visual evaluation tasks 1-9 is 87.9%, which helps the digital transformation and upgrading of experimental physics teaching and promotes the construction of high-quality virtual simulation experimental teaching system.

Keywords

• visual learning
• heterogeneous data
• dynamically adjusted
• search engine
• virtual experiment
• 68t05