Prediction and Simulation of Online Course Grade Passing Rate Based on Big Data Random Probability Model

For a systematic analysis of the indicators affecting teaching in a large-scale network and open teaching platform, random graphs and probability theory were introduced to construct a probability model and algorithm for predicting network teaching penetration rate. This simulation adopts network graph theory and the randomness of learning time using the probability parameters in the time-frequency interval to convert each other. The changes in classroom transmittance is measured, and continuous probability allocation and discrete empirical distribution functions are studied to understand network classroom transmittance through computer simulation. Based on online classroom pass rates provided by Matlab, the feasibility and effectiveness of the random probability model and calculation were verified and applied to online teaching platforms. The result shows the development prospect.