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Abstract Student’s shift of attention away from a current learning task to task-unrelated thought, also called mind wandering, occurs about 30% of the time spent on education-related activities. Its frequent occurrence has a negative effect on learning outcomes across learning tasks. Automated detection of mind wandering might offer an opportunity to assess the attentional state continuously and non-intrusively over time and hence enable large-scale research on learning materials and responding to inattention with targeted interventions. To achieve this, an accessible detection approach that performs well for various systems and settings is required. In this work, we explore a new, generalizable approach to video-based mind wandering detection that can be transferred to naturalistic settings across learning tasks. Therefore, we leverage two datasets, consisting of facial videos during reading in the lab (N = 135) and lecture viewing in-the-wild (N = 15). When predicting mind wandering, deep neural networks (DNN) and long short-term memory networks (LSTMs) achieve F$$_{1}$$ ${}_1$scores of 0.44 (AUC-PR = 0.40) and 0.459 (AUC-PR = 0.39), above chance level, with latent features based on transfer-learning on the lab data. When exploring generalizability by training on the lab dataset and predicting on the in-the-wild dataset, BiLSTMs on latent features perform comparably to the state-of-the-art with an F$$_{1}$$ ${}_1$score of 0.352 (AUC-PR = 0.26). Moreover, we investigate the fairness of predictive models across gender and show based on post-hoc explainability methods that employed latent features mainly encode information on eye and mouth areas. We discuss the benefits of generalizability and possible applications. 

Abstract We know that reading involves coordination between textual characteristics and visual attention, but research linking eye movements during reading and comprehension assessed after reading is surprisingly limited, especially for reading long connected texts. We tested two competing possibilities: (a) the weak association hypothesis: Links between eye movements and comprehension are weak and short‐lived, versus (b) the strong association hypothesis: The two are robustly linked, even after a delay. Using a predictive modeling approach, we trained regression models to predict comprehension scores from global eye movement features, using participant‐level cross‐validation to ensure that the models generalize across participants. We used data from three studies in which readers (Ns = 104, 130, 147) answered multiple‐choice comprehension questions ~30 min after reading a 6,500‐word text, or after reading up to eight 1,000‐word texts. The models generated accurate predictions of participants' text comprehension scores (correlations between observed and predicted comprehension: 0.384, 0.362, 0.372,ps < .001), in line with the strong association hypothesis. We found that making more, but shorter fixations, consistently predicted comprehension across all studies. Furthermore, models trained on one study's data could successfully predict comprehension on the others, suggesting generalizability across studies. Collectively, these findings suggest that there is a robust link between eye movements and subsequent comprehension of a long connected text, thereby connecting theories of low‐level eye movements with those of higher order text processing during reading.