Massive online Open Courses (MOOCs) make extensive use of videos. Students interact with them by pausing, seeking forward or backward, replaying segments, etc. We can reasonably assume that students have different patterns of video interactions, but it remains hard to compare student video interactions. Some methods were developed, such as Markov Chain and Edit Distance. However, these methods have caveats as we show with prototypical examples. This paper proposes a new methodology of comparing video sequences of interaction based both on time spent in each state and the succession of states by computing the distance between the transition matrices of the video interaction sequences. Results show the proposed methodology can better characterize video interaction in a task to discriminate which student is interacting with a video, or which video a student is interacting with. [For the full proceedings, see ED607784.]