Research of SPOC Online Learning Behavior Analysis Based on RFT

It has a direct impact on the learning effect that the occurrence of online learning behavior. The SPOC online learning platform Superstar (Chao Xing) used by our college is taken as an example to conduct this research. Student behavior data participating in SPOC platform online learning is collected, including the length of viewing resources, the number of times to log in the platform, and frequency, etc. The classical RFM model in the big data customer relationship management is improved according to actual needs based on the large amount of data in the online learning platform. And the SPOC online learning behavior analysis model based on RFM is proposed, that is RFT. Empirical analysis on SPOC platform online learning behavior is conducted with the RFT model. Students' learning habits and external influencing factors can be known through empirical research. In the experiment, the data is processed by attribute specification and standardization. Then the students are gathered using the K-Means clustering algorithm. And the R, F, and T indicators are visualized and analyzed through the radar chart and histogram.