With the rapid development of MOOC, course resources are quickly constructed. Because of lack of experience, the quality of many courses is affected. At the same time, there are still many inefficient teams. Therefore, it is very necessary to study scientific MOOC construction process. In the article, we introduced our experience in building four MOOC courses and proposed a scientific construction process. From the three stages of course planning, preparation courses, and video production, this article details the process of MOOC production. It can help teachers prepare MOOC. This process can ensure the quality of resource construction and increase work efficiency. Therefore, such research can promote the development of MOOC.

In this paper, we analyzed the learners' behavior data of the large-scale online course (MOOC). We used the data of learners' Learning behavior to establish the learner-video dipartite network. The two sets of the network are the learners and video collections. In this study, we found the pattern that learners watch videos obeys power-law distribution. In order to study the collective online learning habits, we performed a distribution fit on the learner's video behavior and calculate the parameters, and found that learners' online learning behavior obeys the power-law distribution. Based on the above research, we found the collective online learning habits, which provides a theoretical basis for online course builders to master learners' learning habits.

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MOOC (Massive open online courses) is developing rapidly, but the pass rate of the course is very low. It is significant to study the characteristics of learners who have obtained certificates. This article analyzes the video and completion behavior of various learners. The results show that the learner who obtained the certificate did not watch enough videos, but often completed a lot of exercises. It means they have acquired knowledge from other sources. MOOC is only a means of consolidating knowledge. The results of this study can help us find the right application model for MOOC. At the same time, MOOC operators are also reminded to improve the MOOC model for more learners to accept.