In this study, we propose a MOOC Analytic Statistical Visual model (MOOC-ASV) to explore students' engagement in MOOC courses and predict their performance on the basis of their behaviors logged as big data in MOOC platforms. The model has multifunctions, which performs on visually analyzing learners' data by state-of-the-art techniques. The model presents several visual analyses, which exhibit learners' interaction patterns with course videos and also statistical summaries of associated events for each video. The results showed positive indicators that would enable instructors to identify learners dropping out based on their interactions with videos. The model gives feedback into course instructors and developers to review and modify the course content, especially video content, which ultimately lead to improving the course quality and outcomes.