Existing online learning evaluation methods do not accurately reflect learning effects, which only considers test and assignment scores. A comprehensive evaluation algorithm is proposed in this paper based on the big data of learning behavior. The conversion ratio is taken into account, which is defined by information entropy theory. The algorithm comprehensively considers the learner's multiple learning behaviors, such as viewing videos, doing exercises, taking exams, participating in discussions. The new evaluation algorithm can help learners understand the learning state and maintain their interest.

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MOOCs (massive open online courses) are developing rapidly, but they also face many problems. As the MOOC’s most important resource, the course videos have a very important influence on the learning. This article defines the ratio R (R=Average  viewing  duration/Video  length), which reflects the popularity of the video. By analyzing the relationship between the video length, release time, and R, we found a significant negative linear correlation between video length and R and video release time and R. However, when the number of videos is less than the threshold, the release time has less influence on R. This paper presents a video viewing behavior analysis algorithm based on multiple linear regression. The residual independence test proved that the algorithm has a good approximation to the data. It can predict the popularity of similar course videos to help producers optimize video design.

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