Massive Open Online Courses (MOOCs) has seen a dramatic increase of participants in the last few years with an exponential growth of internet users all around the world. Despite flexible accessibility, the common trend observed in each course is that the number of active participants appears to decrease exponentially as the weeks progress.

The structure and nature of the courses affects the number of active participants directly. In this project, we take an initial step to use the deep learning algorithm to construct the dropout prediction model and produce the predicted individual student dropout probability.

Additional improvements are made to optimize the performance of the dropout prediction model and provide the course providers with appropriate interventions based on a temporal prediction mechanism. Our Exploratory Data Analysis demonstrates that there is strong correlation between click stream actions and successful learner outcomes. Among other features, the deep learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behaviour over time.