Abstract:

Based on the analysis of the evaluation indexes of the large data from the MOOC platform, we introduce the theory of random graph and probability theory, and establish a probability graph model and an algorithm for course pass rate. Based on the network graph theory and the randomness of learning duration, we analyze the dynamic change process of course pass rate qualitatively through the mutual transformation of probability function between timedomain and frequencydomain. Our model can deal with continuous probability distribution cases and discrete probability distribution cases l, as well as massive course pass rate problems highly effectively. Finally, numerical experiment results on MATLAB illustrate the feasibility and effectiveness of the proposed model and algorithm, which can be applied to university MOOC platforms and show a good application prospect.

Key words: computer simulation, large data, course pass rate, prediction