When reading long and complex texts, students may disengage and miss out on relevant content. In order to prevent disengaged behavior or to counteract it by means of an intervention, it is ideally detected an early stage. In this paper, we present a method for early disengagement detection that relies only on the classification of scrolling data. The presented method transforms scrolling data into a time series representation, where each point of the series represents the vertical position of the viewport in the text document. This time series representation is then classified using time series classification algorithms. We evaluated the method on a dataset of 565 university students reading eight different texts. We compared the algorithm performance with different time series lengths, data sampling strategies, the texts that make up the training data, and classification algorithms. The method can classify disengagement early with up to 70% accuracy. However, we also observe differences in the performance depending on which of the texts are included in the training dataset. We discuss our results and propose several possible improvements to enhance the method.

Eye movements in reading are known to reflect cognitive processes involved in reading comprehension at all linguistic levels, from the sub-lexical to the discourse level. This means that reading comprehension and other properties of the text and/or the reader should be possible to infer from eye movements. Consequently, we develop the first neural sequence architecture for this type of tasks which models scan paths in reading and incorporates lexical, semantic and other linguistic features of the stimulus text. Our proposed model outperforms state-of-the-art models in various tasks. These include inferring reading comprehension or text difficulty, and assessing whether the reader is a native speaker of the text’s language. We further conduct an ablation study to investigate the impact of each component of our proposed neural network on its performance.