Reading is one of the main ways in which information is acquired and understanding of the text being read is essential to gaining new knowledge. The reader’s ability to understand the text is influenced by their knowledge, their current psychological state and the difficulty of the text itself. One of the ways to verify the level of comprehension is to test the reader based on the text. Another possibility is to determine the level of comprehension automatically based on the physiological characteristics of a person that were recorded during reading. It is known that the movement of the eyes while reading with comprehension reflects the thought processes behind the reading process.
This work focuses on the automatic text comprehension detection using eyetracking and natural language processing of texts. The current development of natural language processing allows us to automatically calculate many features based on the text being read that affect its complexity. These text features are directly associated with specific eye movements. The knowledge gained from the analysis was used in designing of our method and selection of interesting eyetracking features, features based on the text itself and their combination. In this moment we are working on preprocessing the data gained in conducted user study and verifying our model.