Mind wandering is a phenomenon that is used to describe moments where a person's attention appears to shift away to something that is not related to the primary task, which can have a negative influence on the task performance. In this research, the aim is to create a viable algorithm that can automatically detect mind wandering based on eye movement from the Mementos data set. A method to automatically detect mind wandering could be used in online education in order to help students study more effectively, for example. The Mementos data set contains, unlike previous research, videos captured in an uncontrolled environment using inexpensive equipment. Features based on fixations and saccades were used to create two algorithms which were able to perform better than chance, having an average AOC-ROC of 0.63 and 0.59, as well as having an average F1 score of 0.046 and 0.041 compared to the chance-based model with an F1 score of 0.029.